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Repository: feder-cr/Jobs_Applier_AI_Agent_AIHawk
Branch: main
Commit: ae33178d7baf
Files: 59
Total size: 240.1 KB
Directory structure:
gitextract_396899n1/
├── .github/
│ ├── CODE_OF_CONDUCT.md
│ ├── CONTRIBUTING.md
│ ├── FUNDING.yml
│ ├── ISSUE_TEMPLATE/
│ │ ├── bug-issue.yml
│ │ ├── config.yml
│ │ ├── documentation-issue.yml
│ │ └── enhancement-issue.yml
│ └── workflows/
│ ├── ci.yml
│ └── stale.yml
├── .gitignore
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── assets/
│ └── resume_schema.yaml
├── config.py
├── data_folder/
│ ├── plain_text_resume.yaml
│ ├── secrets.yaml
│ └── work_preferences.yaml
├── data_folder_example/
│ ├── plain_text_resume.yaml
│ ├── resume_liam_murphy.txt
│ ├── secrets.yaml
│ └── work_preferences.yaml
├── main.py
├── requirements.txt
└── src/
├── __init__.py
├── job.py
├── jobContext.py
├── job_application_saver.py
├── libs/
│ ├── llm_manager.py
│ └── resume_and_cover_builder/
│ ├── __init__.py
│ ├── config.py
│ ├── cover_letter_prompt/
│ │ ├── __init__.py
│ │ └── strings_feder-cr.py
│ ├── llm/
│ │ ├── llm_generate_cover_letter_from_job.py
│ │ ├── llm_generate_resume.py
│ │ ├── llm_generate_resume_from_job.py
│ │ └── llm_job_parser.py
│ ├── module_loader.py
│ ├── resume_facade.py
│ ├── resume_generator.py
│ ├── resume_job_description_prompt/
│ │ ├── __init__.py
│ │ └── strings_feder-cr.py
│ ├── resume_prompt/
│ │ ├── __init__.py
│ │ └── strings_feder-cr.py
│ ├── resume_style/
│ │ ├── __init__.py
│ │ ├── style_cloyola.css
│ │ ├── style_josylad_blue.css
│ │ ├── style_josylad_grey.css
│ │ ├── style_krishnavalliappan.css
│ │ └── style_samodum_bold.css
│ ├── style_manager.py
│ ├── template_base.py
│ └── utils.py
├── logging.py
├── resume_schemas/
│ ├── job_application_profile.py
│ └── resume.py
└── utils/
├── __init__.py
├── chrome_utils.py
└── constants.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/CODE_OF_CONDUCT.md
================================================
# Contributor Covenant Code of Conduct
## Our Pledge
In the interest of fostering an open and welcoming environment, we as
contributors and maintainers pledge to making participation in our project and
our community a harassment-free experience for everyone, regardless of age, body
size, disability, ethnicity, gender identity and expression, level of experience,
education, socio-economic status, nationality, personal appearance, race,
religion, or sexual identity and orientation.
## Our Standards
Examples of behavior that contributes to creating a positive environment
include:
- Using welcoming and inclusive language
- Being respectful of differing viewpoints and experiences
- Gracefully accepting constructive criticism
- Focusing on what is best for the community
- Showing empathy towards other community members
Examples of unacceptable behavior by participants include:
- The use of sexualized language or imagery and unwelcome sexual attention or
advances
- Trolling, insulting/derogatory comments, and personal or political attacks
- Public or private harassment
- Publishing others' private information, such as a physical or electronic
address, without explicit permission
- Other conduct which could reasonably be considered inappropriate in a
professional setting
## Our Responsibilities
Project maintainers are responsible for clarifying the standards of acceptable
behavior and are expected to take appropriate and fair corrective action in
response to any instances of unacceptable behavior.
Project maintainers have the right and responsibility to remove, edit, or
reject comments, commits, code, wiki edits, issues, and other contributions
that are not aligned to this Code of Conduct, or to ban temporarily or
permanently any contributor for other behaviors that they deem inappropriate,
threatening, offensive, or harmful.
## Scope
This Code of Conduct applies both within project spaces and in public spaces
when an individual is representing the project or its community. Examples of
representing a project or community include using an official project e-mail
address, posting via an official social media account, or acting as an appointed
representative at an online or offline event. Representation of a project may be
further defined and clarified by project maintainers.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported by contacting the project team at . All
complaints will be reviewed and investigated and will result in a response that
is deemed necessary and appropriate to the circumstances. The project team is
obligated to maintain confidentiality with regard to the reporter of an incident.
Further details of specific enforcement policies may be posted separately.
Project maintainers who do not follow or enforce the Code of Conduct in good
faith may face temporary or permanent repercussions as determined by other
members of the project's leadership.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
[homepage]: https://www.contributor-covenant.org
================================================
FILE: .github/CONTRIBUTING.md
================================================
# Issues Reporting Guidelines
Welcome to the AI Hawk Contributing Guide and Issues Tracker! To keep things organized and ensure issues are resolved quickly, please follow the guidelines below when submitting a bug report, feature request, or any other issue.
If you have a general question, are curious about how something in Python works, please remember that [Google](https://google.com) is your friend and it can answer many questions.
This is a work in progress and you may encounter bugs.
The employers who you are applying to are not looking for candidates who need someone to hold their hand and do everything for them, they are not your parents, they are your potential boses; they will be expecting you to be able to solve simple problems on your own, the AI Hawk mods and devs expect the same of you.
Please do not beg in the issues tracker, discussions or chat. We are not here to give you a job, we are here to provide you with a tool for you to go out and find a job on your own. We will try to have instructions for all steps of the process, but you must read the docs, learn on your own, and understand that this is an open-source project run by volunteers. It will require you to do some work of your own.
If you see something that needs to be documented, or some documentation which could be improved, submit a documentation request or document it yourself and submit a PR to help others understand how that part of the software functions and how to use it.
## Before You Submit an Issue
### 1. Search Existing Issues
Please search through the existing open issues and closed issues to ensure your issue hasn’t already been reported. This helps avoid duplicates and allows us to focus on unresolved problems.
### 2. Check Documentation
Review the README and any available documentation to see if your issue is covered.
Watch this [Intro to AI Hawk video on YouTube](https://www.youtube.com/watch?v=gdW9wogHEUM)
Join us on [Telegram](https://t.me/AIhawkCommunity) to check with the community about issues and ask for help with issues. If a dev, mod, contributor or other community member is available, a live conversation will likely resolve your small issues and configuration problems faster than using this issues tracker would.
### 3. Provide Detailed Information
If you are reporting a bug, make sure you include enough details to reproduce the issue. The more information you provide, the faster we can diagnose and fix the problem.
## Issue Types
### 1. Bug Reports
Please include the following information:
- **Description:** A clear and concise description of the problem.
- **Steps to Reproduce:** Provide detailed steps to reproduce the bug.
- **Expected Behavior:** What should have happened.
- **Actual Behavior:** What actually happened.
- **Environment Details:** Include your OS, browser version (if applicable), which LLM you are using and any other relevant environment details.
- **Logs/Screenshots:** If applicable, attach screenshots or log outputs.
### 2. Feature Requests
For new features or improvements:
- Clearly describe the feature you would like to see.
- Explain the problem this feature would solve or the benefit it would bring.
- If possible, provide examples or references to similar features in other tools or platforms.
### 3. Questions/Discussions
- If you’re unsure whether something is a bug or if you’re seeking clarification on functionality, you can ask a question. The best place to ask a question is on [Telegram](https://t.me/AIhawkCommunity). If you are asking a question on GitHub, please make sure to label your issue as a question.
## Issue Labeling and Response Time
We use the following labels to categorize issues:
- **bug:** An issue where something isn't functioning as expected.
- **documentation:** Improvements or additions to project documentation.
- **duplicate:** This issue or pull request already exists elsewhere.
- **enhancement:** A request for a new feature or improvement.
- **good first issue:** A simple issue suitable for newcomers.
- **help wanted:** The issue needs extra attention or assistance.
- **invalid:** The issue is not valid or doesn't seem correct.
- **question:** Additional information or clarification is needed.
- **wontfix:** The issue will not be fixed or addressed.
- We aim to respond to issues as early as possible. Please be patient, as maintainers may have limited availability.
## Contributing Fixes
If you’re able to contribute a fix for an issue:
1. Fork the repository and create a new branch for your fix.
2. Reference the issue number in your branch and pull request.
3. Submit a pull request with a detailed description of the changes and how they resolve the issue.
================================================
FILE: .github/FUNDING.yml
================================================
github: feder-cr
================================================
FILE: .github/ISSUE_TEMPLATE/bug-issue.yml
================================================
name: Bug report
description: Report a bug or an issue that isn't working as expected.
title: "[BUG]: <Provide a clear, descriptive title>"
labels: ["bug"]
assignees: []
body:
- type: markdown
attributes:
value: |
Please fill out the following information to help us resolve the issue.
- type: input
id: description
attributes:
label: Describe the bug
description: A clear and concise description of what the bug is.
placeholder: "Describe the bug in detail..."
- type: textarea
id: steps
attributes:
label: Steps to reproduce
description: |
Steps to reproduce the behavior:
1. Use branch named '...'
2. Go to file '...'
3. Find property named '...'
4. Change '...'
5. Run program using command '...'
6. See error
placeholder: "List the steps to reproduce the bug..."
- type: input
id: expected
attributes:
label: Expected behavior
description: What you expected to happen.
placeholder: "What was the expected result?"
- type: input
id: actual
attributes:
label: Actual behavior
description: What actually happened instead.
placeholder: "What happened instead?"
- type: dropdown
id: branch
attributes:
label: Branch
description: Specify the branch you were using when the bug occurred.
options:
- main
- other
- type: input
id: otherBranch
attributes:
label: Branch name
description: If you selected ```other``` branch for the previous question, what is the branch name?
placeholder: "what-is-the-name-of-the-branch-you-were-using"
- type: input
id: pythonVersion
attributes:
label: Python version
description: Specify the version of Python you were using when the bug occurred.
placeholder: "e.g., 3.12.5(64b)"
- type: input
id: llm
attributes:
label: LLM Used
description: Specify the LLM provider you were using when the bug occurred.
placeholder: "e.g., ChatGPT"
- type: input
id: model
attributes:
label: Model used
description: Specify the LLM model you were using when the bug occurred.
placeholder: "e.g., GPT-4o-mini"
- type: textarea
id: additional
attributes:
label: Additional context
description: Add any other context about the problem here.
placeholder: "Any additional information..."
================================================
FILE: .github/ISSUE_TEMPLATE/config.yml
================================================
blank_issues_enabled: true
contact_links:
- name: Questions
url: t.me/AIhawkCommunity
about: You can join the discussions on Telegram.
- name: New issue
url: >-
https://github.com/feder-cr/Auto_Jobs_Applier_AIHawk/blob/v3/.github/CONTRIBUTING.md
about: "Before opening a new issue, please make sure to read CONTRIBUTING.md"
================================================
FILE: .github/ISSUE_TEMPLATE/documentation-issue.yml
================================================
name: Documentation request
description: Suggest improvements or additions to the project's documentation.
title: "[DOCS]: <Provide a short title>"
labels: ["documentation"]
assignees: []
body:
- type: markdown
attributes:
value: |
Thanks for helping to improve the project's documentation! Please provide the following details to ensure your request is clear.
- type: input
id: doc_section
attributes:
label: Affected documentation section
description: Specify which part of the documentation needs improvement or addition.
placeholder: "e.g., Installation Guide, API Reference..."
- type: textarea
id: description
attributes:
label: Documentation improvement description
description: Describe the specific improvements or additions you suggest.
placeholder: "Explain what changes you propose and why..."
- type: input
id: reason
attributes:
label: Why is this change necessary?
description: Explain why the documentation needs to be updated or expanded.
placeholder: "Describe the issue or gap in the documentation..."
- type: input
id: additional
attributes:
label: Additional context
description: Add any other context, such as related documentation, external resources, or screenshots.
placeholder: "Add any other supporting information..."
================================================
FILE: .github/ISSUE_TEMPLATE/enhancement-issue.yml
================================================
name: Feature request
description: Suggest a new feature or improvement for the project.
title: "[FEATURE]: <Provide a descriptive title>"
labels: ["enhancement"]
assignees: []
body:
- type: markdown
attributes:
value: |
Thank you for suggesting a feature! Please fill out the form below to help us understand your idea.
- type: input
id: summary
attributes:
label: Feature summary
description: Provide a short summary of the feature you're requesting.
placeholder: "Summarize the feature in a few words..."
- type: textarea
id: description
attributes:
label: Feature description
description: A detailed description of the feature or improvement.
placeholder: "Describe the feature in detail..."
- type: input
id: motivation
attributes:
label: Motivation
description: Explain why this feature would be beneficial and how it solves a problem.
placeholder: "Why do you need this feature?"
- type: textarea
id: alternatives
attributes:
label: Alternatives considered
description: List any alternative solutions or features you've considered.
placeholder: "Are there any alternative features or solutions you’ve considered?"
- type: input
id: additional
attributes:
label: Additional context
description: Add any other context or screenshots to support your feature request.
placeholder: "Any additional information..."
================================================
FILE: .github/workflows/ci.yml
================================================
name: Python CI
on:
push:
pull_request:
types: [opened, synchronize, reopened]
jobs:
test:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v3
with:
python-version: '3.x'
- name: Install dependencies
run: pip install -r requirements.txt
- name: Run tests
run: pytest
================================================
FILE: .github/workflows/stale.yml
================================================
name: Mark and Close Stale Issues
on:
# Schedule the workflow to run periodically (e.g., daily at 1:30 AM UTC)
schedule:
- cron: "30 1 * * *"
workflow_dispatch:
jobs:
stale:
runs-on: ubuntu-latest
permissions:
issues: write
pull-requests: write
steps:
- name: Run Stale Action
uses: actions/stale@v9
with:
repo-token: ${{ secrets.GITHUB_TOKEN }}
days-before-issue-stale: 10 # Days of inactivity before marking an issue as stale
days-before-issue-close: 5 # Days after being marked stale before closing the issue
stale-issue-label: "stale" # Label to apply to stale issues
exempt-issue-labels: "pinned,important" # Labels to exclude from being marked as stale
exempt-issue-assignees: true # Exempt issues with assignees from being marked as stale
stale-issue-message: "This issue has been marked as stale due to inactivity. Please comment or update if this is still relevant."
close-issue-message: "This issue was closed due to prolonged inactivity."
days-before-pr-stale: 10 # Days of inactivity before marking a PR as stale
days-before-pr-close: 2 # Days after being marked stale before closing the PR
stale-pr-label: "stale" # Label to apply to stale PRs
exempt-pr-labels: "pinned,important" # Labels to exclude from being marked as stale
stale-pr-message: >
"This pull request has been marked as stale due to inactivity.
To keep it open, you can:
- Show progress by updating the PR with new commits.
- Continue the conversation by adding comments or requesting clarification on any blockers.
- Resolve pending feedback by replying to unresolved comments or implementing suggested changes.
- Indicate readiness for review by explicitly requesting a review from maintainers or reviewers.
If no action is taken within 7 days, this pull request will be closed."
close-pr-message: "This PR was closed due to prolonged inactivity."
remove-stale-when-updated: true # Remove the stale label if there is new activity
operations-per-run: 20 # Number of issues to process per run (default is 30)
================================================
FILE: .gitignore
================================================
# application files and logs
/generated_cv
/log/*
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
chrome_profile/*
data_folder/output/*
answers.json
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
.venv
.pytest_cache
virtual
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv’s dependency resolution may lead to different
# Pipfile.lock files generated on each colleague’s machine.
# Thus, uncomment the following line if the pipenv environment is expected to be identical
# across all environments.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# MacOS
.DS_Store
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
# PyCharm and all JetBrains IDEs
# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
.idea/
*.iml
# Visual Studio Code
.vscode/
# Visual Studio 2015/2017/2019/2022
.vs/
*.opendb
*.VC.db
# User-specific files
*.suo
*.user
*.userosscache
*.sln.docstates
# Mono Auto Generated Files
mono_crash.*
job_applications/
================================================
FILE: CONTRIBUTING.md
================================================
# Contributing to Auto_Jobs_Applier_AIHawk
## Table of Contents
- [Issue Labels](#issue-labels)
- [Bug Reports](#bug-reports)
- [Feature Requests](#feature-requests)
- [Branch Rules](#branch-rules)
- [Version Control](#version-control)
- [Release Process](#release-process)
- [Roles](#roles)
- [Pull Request Process](#pull-request-process)
- [Code Style Guidelines](#code-style-guidelines)
- [Development Setup](#development-setup)
- [Testing](#testing)
- [Communication](#communication)
- [Development Diagrams](./docs/development_diagrams.md)
Thank you for your interest in contributing to Auto_Jobs_Applier_AIHawk. This document provides guidelines for contributing to the project.
## Issue Labels
The project uses the following labels:
- **bug**: Something isn't working correctly
- **enhancement**: New feature requests
- **good first issue**: Good for newcomers
- **help wanted**: Extra attention needed
- **documentation**: Documentation improvements
## Bug Reports
When submitting a bug report, please include:
- A clear, descriptive title prefixed with [BUG]
- Steps to reproduce the issue
- Expected behavior
- Actual behavior
- Any error messages or screenshots
- Your environment details (OS, Python version, etc.)
## Feature Requests
For feature requests, please:
- Prefix the title with [FEATURE]
- Include a feature summary
- Provide detailed feature description
- Explain your motivation for the feature
- List any alternatives you've considered
## Branch Rules
- `main` - Production-ready code, protected branch
- `develop` - Integration branch for features
- `feature/*` - New features
- `release/*` - Release preparation
- `bugfix/*` - Bug fixes for development
- `hotfix/*` - Emergency production fixes
## Version Control
- Semantic versioning: `vMAJOR.MINOR.PATCH`
- Release tags on `main` branch only
- Package versions match git tags
## Release Process
week one for `release/v4.1.0`
- Planning meeting for `release/v4.1.0` with release scope and milestone objectives set by the maintainers. Release and maintainer meeting agendas and schedules are posted on the project repository [wiki](https://github.com/AIHawk/AIHawk/wiki) and shared in the `#releases` channel on Discord.
- `release/v4.0.0` release candidate ready for release
- `release/v4.0.0` merge into `develop`, `main`
- tag `main` as `release/v4.0.0`
- `release/v4.0.0` published to AIHawk/releases and PyPI as a package with release documentation
- delete `release/v4.0.0` branch
release/v4.1.0 release weeks
- Contributers work on issues and PRs, prioritizing next milestone
- Maintainers review PRs from `feature/*`, `bugfix/*` branches and issues, merging into `develop`
- Maintainers review PRs from `hotfix/*` branches and issues, merged into `main` and `develop`, `main` tagged and merged into `4.0.1` package and `release/v4.0.1` and `release/v4.1.0`, documentation is updated
last week, release candidate
- `develop` is frozen, only bug fixes
- create release branch `release/v4.1.0` from `develop`
- only bug fixes are merged into `release/v4.1.0`
- additional testing and release candidate review
week one is repeated for `release/v4.2.0`
```mermaid
gantt
title Release Cycle Process
dateFormat YYYY-MM-DD
section Retro/Plan
Planning release/v4.1.0 : 2025-01-01, 2d
Publish release/v4.0.0 :milestone, m1, 2025-01-01, 1d
section Dev Cycle
Feature Development :2025-01-03, 27d
PR Reviews :2025-01-03, 27d
section Release
Freeze develop :milestone, m3, 2025-01-30, 1d
Create release/v4.1.0 :milestone, m4, 2025-01-30, 1d
Bug Fixes Only :2025-01-30, 2d
RC Testing :2025-01-30, 2d
section Next Cycle
Skip Weekend :2025-02-01, 2d
Planning release/v4.2.0 :2025-02-03, 2d
Publish release/v4.1.0 :milestone, m4, 2025-02-03, 1d
```
## Roles
### Organization Owner
- Has full access to all repositories
- Controls organization-wide settings and permissions
- Can set base permissions for all members
- Manages repository settings and collaborator access
### Release Manager
- Creates and manages release branch from develop
- Coordinates release cycles and versioning
- Merges release into main
### Maintainer
- Reviews and approves develop, feature PRs
- Triage issues, bugs, PRs
- Manages feature, bugfix PRs merge into develop
- Leads feature development, bug prioritization
- Manages README, CONTRIBUTING, and other documentation
### Moderator
- Moderates Telegram, Discord channels
- Manages project wiki
- Contributes to README, CONTRIBUTING, and other documentation
### Contributor
- Creates feature branches from develop
- Implements new features, bug fixes, and other changes
- creates PRs on features
- Collaborates with other developers on features
## Pull Request Process
1. Fork the repository
2. Create a new branch for your feature or bug
3. Write clear commit messages
4. Update documentation as needed
5. Add tests for new functionality
6. Ensure tests pass
7. Submit a pull request with a clear description
## Merging Pull Requests
- All PRs are reviewed by maintainers
- At least 2 Maintainers approve PRs for merge
- PRs are merged into `develop`
- PRs are tested and verified to work as expected
## Code Style Guidelines
- Follow PEP 8 standards for Python code
- Include docstrings for new functions and classes
- Add comments for complex logic
- Maintain consistent naming conventions
- Security best practices
- Any performance considerations
## Development Setup
1. Clone the repository
2. Install dependencies from requirements.txt
3. Set up necessary API keys and configurations
## Testing
Before submitting a PR:
- Test your changes thoroughly
- Ensure existing tests pass
- Add new tests for new functionality
- Verify functionality with different configurations
## Communication
- Be respectful and constructive in discussions
- Use clear and concise language
- Reference relevant issues in commits and PRs
- Ask for help when needed
The project maintainers reserve the right to reject any contribution that doesn't meet these guidelines or align with the project's goals.
================================================
FILE: LICENSE
================================================
GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
Copyright (C) 2024 AI Hawk FOSS <https://github.com/AIHawk-FOSS/Auto_Jobs_Applier_AI_Agent/graphs/contributors> </br>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU Affero General Public License is a free, copyleft license for
software and other kinds of works, specifically designed to ensure
cooperation with the community in the case of network server software.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
our General Public Licenses are intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
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If the Program specifies that a proxy can decide which future
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17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
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END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.
================================================
FILE: README.md
================================================
<div align="center">
# AIHawk: The first Jobs Applier AI Web Agent
AIHawk's core architecture remains **open source**, allowing developers to inspect and extend the codebase. However, due to copyright considerations, we have removed all third‑party provider plugins from this repository.
---
AIHawk has been featured by major media outlets for revolutionizing how job seekers interact with the job market:
[**Business Insider**](https://www.businessinsider.com/aihawk-applies-jobs-for-you-linkedin-risks-inaccuracies-mistakes-2024-11)
[**TechCrunch**](https://techcrunch.com/2024/10/10/a-reporter-used-ai-to-apply-to-2843-jobs/)
[**Semafor**](https://www.semafor.com/article/09/12/2024/linkedins-have-nots-and-have-bots)
[**Dev.by**](https://devby.io/news/ya-razoslal-rezume-na-2843-vakansii-po-17-v-chas-kak-ii-boty-vytesnyaut-ludei-iz-protsessa-naima.amp)
[**Wired**](https://www.wired.it/article/aihawk-come-automatizzare-ricerca-lavoro/)
[**The Verge**](https://www.theverge.com/2024/10/10/24266898/ai-is-enabling-job-seekers-to-think-like-spammers)
[**Vanity Fair**](https://www.vanityfair.it/article/intelligenza-artificiale-candidature-di-lavoro)
[**404 Media**](https://www.404media.co/i-applied-to-2-843-roles-the-rise-of-ai-powered-job-application-bots/)
================================================
FILE: assets/resume_schema.yaml
================================================
# YAML Schema for plain_text_resume.yaml
personal_information:
type: object
properties:
name: {type: string}
surname: {type: string}
date_of_birth: {type: string, format: date}
country: {type: string}
zip_code: {type: string, pattern: "^[0-9]{5,10}$"}
city: {type: string}
address: {type: string}
phone_prefix: {type: string, format: phone_prefix}
phone: {type: string, format: phone}
email: {type: string, format: email}
github: {type: string, format: uri}
linkedin: {type: string, format: uri}
required: [name, surname, date_of_birth, country, city, address, zip_code, phone_prefix, phone, email]
education_details:
type: array
items:
type: object
properties:
degree: {type: string}
university: {type: string}
gpa: {type: string}
graduation_year: {type: string}
field_of_study: {type: string}
exam:
type: object
additionalProperties: {type: string}
required: [degree, university, gpa, graduation_year, field_of_study]
experience_details:
type: array
items:
type: object
properties:
position: {type: string}
company: {type: string}
employment_period: {type: string}
location: {type: string}
industry: {type: string}
key_responsibilities:
type: object
additionalProperties: {type: string}
skills_acquired:
type: array
items: {type: string}
required: [position, company, employment_period, location, industry, key_responsibilities, skills_acquired]
projects:
type: array
items:
type: object
properties:
name: {type: string}
description: {type: string}
link: {type: string, format: uri}
required: [name, description]
achievements:
type: array
items:
type: object
properties:
name: {type: string}
description: {type: string}
required: [name, description]
certifications:
type: array
items: {type: string}
languages:
type: array
items:
type: object
properties:
language: {type: string}
proficiency: {type: string, enum: [Native, Fluent, Intermediate, Beginner]}
required: [language, proficiency]
interests:
type: array
items: {type: string}
availability:
type: object
properties:
notice_period: {type: string}
required: [notice_period]
salary_expectations:
type: object
properties:
salary_range_usd: {type: string}
required: [salary_range_usd]
self_identification:
type: object
properties:
gender: {type: string}
pronouns: {type: string}
veteran: {type: string, enum: [Yes, No]}
disability: {type: string, enum: [Yes, No]}
ethnicity: {type: string}
required: [gender, pronouns, veteran, disability, ethnicity]
legal_authorization:
type: object
properties:
eu_work_authorization: {type: string, enum: [Yes, No]}
us_work_authorization: {type: string, enum: [Yes, No]}
requires_us_visa: {type: string, enum: [Yes, No]}
requires_us_sponsorship: {type: string, enum: [Yes, No]}
requires_eu_visa: {type: string, enum: [Yes, No]}
legally_allowed_to_work_in_eu: {type: string, enum: [Yes, No]}
legally_allowed_to_work_in_us: {type: string, enum: [Yes, No]}
requires_eu_sponsorship: {type: string, enum: [Yes, No]}
required: [eu_work_authorization, us_work_authorization, requires_us_visa, requires_us_sponsorship, requires_eu_visa, legally_allowed_to_work_in_eu, legally_allowed_to_work_in_us, requires_eu_sponsorship]
work_preferences:
type: object
properties:
remote_work: {type: string, enum: [Yes, No]}
in_person_work: {type: string, enum: [Yes, No]}
open_to_relocation: {type: string, enum: [Yes, No]}
willing_to_complete_assessments: {type: string, enum: [Yes, No]}
willing_to_undergo_drug_tests: {type: string, enum: [Yes, No]}
willing_to_undergo_background_checks: {type: string, enum: [Yes, No]}
required: [remote_work, in_person_work, open_to_relocation, willing_to_complete_assessments, willing_to_undergo_drug_tests, willing_to_undergo_background_checks]
================================================
FILE: config.py
================================================
# In this file, you can set the configurations of the app.
from src.utils.constants import DEBUG, ERROR, LLM_MODEL, OPENAI
#config related to logging must have prefix LOG_
LOG_LEVEL = 'ERROR'
LOG_SELENIUM_LEVEL = ERROR
LOG_TO_FILE = False
LOG_TO_CONSOLE = False
MINIMUM_WAIT_TIME_IN_SECONDS = 60
JOB_APPLICATIONS_DIR = "job_applications"
JOB_SUITABILITY_SCORE = 7
JOB_MAX_APPLICATIONS = 5
JOB_MIN_APPLICATIONS = 1
LLM_MODEL_TYPE = 'openai'
LLM_MODEL = 'gpt-4o-mini'
# Only required for OLLAMA models
LLM_API_URL = ''
================================================
FILE: data_folder/plain_text_resume.yaml
================================================
personal_information:
name: "[Your Name]"
surname: "[Your Surname]"
date_of_birth: "[Your Date of Birth]"
country: "[Your Country]"
city: "[Your City]"
address: "[Your Address]"
zip_code: "[Your zip code]"
phone_prefix: "[Your Phone Prefix]"
phone: "[Your Phone Number]"
email: "[Your Email Address]"
github: "[Your GitHub Profile URL]"
linkedin: "[Your LinkedIn Profile URL]"
education_details:
- education_level: "[Your Education Level]"
institution: "[Your Institution]"
field_of_study: "[Your Field of Study]"
final_evaluation_grade: "[Your Final Evaluation Grade]"
start_date: "[Start Date]"
year_of_completion: "[Year of Completion]"
exam:
exam_name_1: "[Grade]"
exam_name_2: "[Grade]"
exam_name_3: "[Grade]"
exam_name_4: "[Grade]"
exam_name_5: "[Grade]"
exam_name_6: "[Grade]"
experience_details:
- position: "[Your Position]"
company: "[Company Name]"
employment_period: "[Employment Period]"
location: "[Location]"
industry: "[Industry]"
key_responsibilities:
- responsibility_1: "[Responsibility Description]"
- responsibility_2: "[Responsibility Description]"
- responsibility_3: "[Responsibility Description]"
skills_acquired:
- "[Skill]"
- "[Skill]"
- "[Skill]"
- position: "[Your Position]"
company: "[Company Name]"
employment_period: "[Employment Period]"
location: "[Location]"
industry: "[Industry]"
key_responsibilities:
- responsibility_1: "[Responsibility Description]"
- responsibility_2: "[Responsibility Description]"
- responsibility_3: "[Responsibility Description]"
skills_acquired:
- "[Skill]"
- "[Skill]"
- "[Skill]"
projects:
- name: "[Project Name]"
description: "[Project Description]"
link: "[Project Link]"
- name: "[Project Name]"
description: "[Project Description]"
link: "[Project Link]"
achievements:
- name: "[Achievement Name]"
description: "[Achievement Description]"
- name: "[Achievement Name]"
description: "[Achievement Description]"
certifications:
- name: "[Certification Name]"
description: "[Certification Description]"
- name: "[Certification Name]"
description: "[Certification Description]"
languages:
- language: "[Language]"
proficiency: "[Proficiency Level]"
- language: "[Language]"
proficiency: "[Proficiency Level]"
interests:
- "[Interest]"
- "[Interest]"
- "[Interest]"
availability:
notice_period: "[Notice Period]"
salary_expectations:
salary_range_usd: "[Salary Range]"
self_identification:
gender: "[Gender]"
pronouns: "[Pronouns]"
veteran: "[Yes/No]"
disability: "[Yes/No]"
ethnicity: "[Ethnicity]"
legal_authorization:
eu_work_authorization: "[Yes/No]"
us_work_authorization: "[Yes/No]"
requires_us_visa: "[Yes/No]"
requires_us_sponsorship: "[Yes/No]"
requires_eu_visa: "[Yes/No]"
legally_allowed_to_work_in_eu: "[Yes/No]"
legally_allowed_to_work_in_us: "[Yes/No]"
requires_eu_sponsorship: "[Yes/No]"
canada_work_authorization: "[Yes/No]"
requires_canada_visa: "[Yes/No]"
legally_allowed_to_work_in_canada: "[Yes/No]"
requires_canada_sponsorship: "[Yes/No]"
uk_work_authorization: "[Yes/No]"
requires_uk_visa: "[Yes/No]"
legally_allowed_to_work_in_uk: "[Yes/No]"
requires_uk_sponsorship: "[Yes/No]"
work_preferences:
remote_work: "[Yes/No]"
in_person_work: "[Yes/No]"
open_to_relocation: "[Yes/No]"
willing_to_complete_assessments: "[Yes/No]"
willing_to_undergo_drug_tests: "[Yes/No]"
willing_to_undergo_background_checks: "[Yes/No]"
================================================
FILE: data_folder/secrets.yaml
================================================
llm_api_key: 'sk-11KRr4uuTwpRGfeRTfj1T9BlbkFJjP8QTrswHU1yGruru2FR'
================================================
FILE: data_folder/work_preferences.yaml
================================================
remote: true
hybrid: true
onsite: true
experience_level:
internship: false
entry: true
associate: true
mid_senior_level: true
director: false
executive: false
job_types:
full_time: true
contract: false
part_time: false
temporary: true
internship: false
other: false
volunteer: true
date:
all_time: false
month: false
week: false
24_hours: true
positions:
- Software engineer
locations:
- Germany
apply_once_at_company: true
distance: 100
company_blacklist:
- wayfair
- Crossover
title_blacklist:
- word1
- word2
location_blacklist:
- Brazil
================================================
FILE: data_folder_example/plain_text_resume.yaml
================================================
personal_information:
name: "solid"
surname: "snake"
date_of_birth: "12/01/1861"
country: "Ireland"
city: "Dublin"
zip_code: "520123"
address: "12 Fox road"
phone_prefix: "+1"
phone: "7819117091"
email: "hi@gmail.com"
github: "https://github.com/lol"
linkedin: "https://www.linkedin.com/in/thezucc/"
education_details:
- education_level: "Master's Degree"
institution: "Bob academy"
field_of_study: "Bobs Engineering"
final_evaluation_grade: "4.0"
year_of_completion: "2023"
start_date: "2022"
additional_info:
exam:
Algorithms: "A"
Linear Algebra: "A"
Database Systems: "A"
Operating Systems: "A-"
Web Development: "A"
experience_details:
- position: "X"
company: "Y."
employment_period: "06/2019 - Present"
location: "San Francisco, CA"
industry: "Technology"
key_responsibilities:
- responsibility: "Developed web applications using React and Node.js"
- responsibility: "Collaborated with cross-functional teams to design and implement new features"
- responsibility: "Troubleshot and resolved complex software issues"
skills_acquired:
- "React"
- "Node.js"
- "Software Troubleshooting"
- position: "Software Developer"
company: "Innovatech"
employment_period: "06/2015 - 12/2017"
location: "Milan, Italy"
industry: "Technology"
key_responsibilities:
- responsibility: "Developed and maintained web applications using modern technologies"
- responsibility: "Collaborated with UX/UI designers to enhance user experience"
- responsibility: "Implemented automated testing procedures to ensure code quality"
skills_acquired:
- "Web development"
- "User experience design"
- "Automated testing"
- position: "Junior Developer"
company: "StartUp Hub"
employment_period: "01/2014 - 05/2015"
location: "Florence, Italy"
industry: "Startups"
key_responsibilities:
- responsibility: "Assisted in the development of mobile applications and web platforms"
- responsibility: "Participated in code reviews and contributed to software design discussions"
- responsibility: "Resolved bugs and implemented feature enhancements"
skills_acquired:
- "Mobile app development"
- "Code reviews"
- "Bug fixing"
projects:
- name: "X"
description: "Y blah blah blah "
link: "https://github.com/haveagoodday"
achievements:
- name: "Employee of the Month"
description: "Recognized for exceptional performance and contributions to the team."
- name: "Hackathon Winner"
description: "Won first place in a national hackathon competition."
certifications:
- name: "Certified Scrum Master"
description: "Recognized certification for proficiency in Agile methodologies and Scrum framework."
- name: "AWS Certified Solutions Architect"
description: "Certification demonstrating expertise in designing, deploying, and managing applications on AWS."
languages:
- language: "English"
proficiency: "Fluent"
- language: "Spanish"
proficiency: "Intermediate"
interests:
- "Machine Learning"
- "Cybersecurity"
- "Open Source Projects"
- "Digital Marketing"
- "Entrepreneurship"
availability:
notice_period: "2 weeks"
salary_expectations:
salary_range_usd: "90000 - 110000"
self_identification:
gender: "Female"
pronouns: "She/Her"
veteran: "No"
disability: "No"
ethnicity: "Asian"
legal_authorization:
eu_work_authorization: "Yes"
us_work_authorization: "Yes"
requires_us_visa: "No"
requires_us_sponsorship: "Yes"
requires_eu_visa: "No"
legally_allowed_to_work_in_eu: "Yes"
legally_allowed_to_work_in_us: "Yes"
requires_eu_sponsorship: "No"
canada_work_authorization: "Yes"
requires_canada_visa: "No"
legally_allowed_to_work_in_canada: "Yes"
requires_canada_sponsorship: "No"
uk_work_authorization: "Yes"
requires_uk_visa: "No"
legally_allowed_to_work_in_uk: "Yes"
requires_uk_sponsorship: "No"
work_preferences:
remote_work: "Yes"
in_person_work: "Yes"
open_to_relocation: "Yes"
willing_to_complete_assessments: "Yes"
willing_to_undergo_drug_tests: "Yes"
willing_to_undergo_background_checks: "Yes"
================================================
FILE: data_folder_example/resume_liam_murphy.txt
================================================
Liam Murphy
Galway, Ireland
Email: liam.murphy@gmail.com | AIHawk: liam-murphy
GitHub: liam-murphy | Phone: +353 871234567
Education
Bachelor's Degree in Computer Science
National University of Ireland, Galway (GPA: 4/4)
Graduation Year: 2020
Experience
Co-Founder & Software Engineer
CryptoWave Solutions (03/2021 - Present)
Location: Ireland | Industry: Blockchain Technology
Co-founded and led a startup specializing in app and software development with a focus on blockchain technology
Provided blockchain consultations for 10+ companies, enhancing their software capabilities with secure, decentralized solutions
Developed blockchain applications, integrated cutting-edge technology to meet client needs and drive industry innovation
Research Intern
National University of Ireland, Galway (11/2022 - 03/2023)
Location: Galway, Ireland | Industry: IoT Security Research
Conducted in-depth research on IoT security, focusing on binary instrumentation and runtime monitoring
Performed in-depth study of the MQTT protocol and Falco
Developed multiple software components including MQTT packet analysis library, Falco adapter, and RML monitor in Prolog
Authored thesis "Binary Instrumentation for Runtime Monitoring of Internet of Things Systems Using Falco"
Software Engineer
University Hospital Galway (05/2022 - 11/2022)
Location: Galway, Ireland | Industry: Healthcare IT
Integrated and enforced robust security protocols
Developed and maintained a critical software tool for password validation used by over 1,600 employees
Played an integral role in the hospital's cybersecurity team
Projects
JobBot
AI-driven tool to automate and personalize job applications on AIHawk, gained over 3000 stars on GitHub, improving efficiency and reducing application time
Link: JobBot
mqtt-packet-parser
Developed a Node.js module for parsing MQTT packets, improved parsing efficiency by 40%
Link: mqtt-packet-parser
Achievements
Winner of an Irish public competition - Won first place in a public competition with a perfect score of 70/70, securing a Software Developer position at University Hospital Galway
Galway Merit Scholarship - Awarded annually from 2018 to 2020 in recognition of academic excellence and contribution
GitHub Recognition - Gained over 3000 stars on GitHub with JobBot project
Certifications
C1
Languages
English - Native
Spanish - Professional
Interests
Full-Stack Development, Software Architecture, IoT system design and development, Artificial Intelligence, Cloud Technologies
================================================
FILE: data_folder_example/secrets.yaml
================================================
llm_api_key: 'sk-11KRr4uuTwpRGfeRTfj1T9BlbkFJjP8QTrswHU1yGruru2FR'
================================================
FILE: data_folder_example/work_preferences.yaml
================================================
remote: true
hybrid: true
onsite: true
experience_level:
internship: false
entry: true
associate: true
mid_senior_level: true
director: false
executive: false
job_types:
full_time: true
contract: false
part_time: false
temporary: true
internship: false
other: false
volunteer: true
date:
all_time: false
month: false
week: false
24_hours: true
positions:
- Software engineer
locations:
- Germany
apply_once_at_company: true
distance: 100
company_blacklist:
- wayfair
- Crossover
title_blacklist:
- word1
- word2
location_blacklist:
- Brazil
================================================
FILE: main.py
================================================
import base64
import sys
from pathlib import Path
import traceback
from typing import List, Optional, Tuple, Dict
import click
import inquirer
import yaml
from selenium import webdriver
from selenium.common.exceptions import WebDriverException
from selenium.webdriver.chrome.service import Service as ChromeService
from webdriver_manager.chrome import ChromeDriverManager
import re
from src.libs.resume_and_cover_builder import ResumeFacade, ResumeGenerator, StyleManager
from src.resume_schemas.job_application_profile import JobApplicationProfile
from src.resume_schemas.resume import Resume
from src.logging import logger
from src.utils.chrome_utils import init_browser
from src.utils.constants import (
PLAIN_TEXT_RESUME_YAML,
SECRETS_YAML,
WORK_PREFERENCES_YAML,
)
# from ai_hawk.bot_facade import AIHawkBotFacade
# from ai_hawk.job_manager import AIHawkJobManager
# from ai_hawk.llm.llm_manager import GPTAnswerer
class ConfigError(Exception):
"""Custom exception for configuration-related errors."""
pass
class ConfigValidator:
"""Validates configuration and secrets YAML files."""
EMAIL_REGEX = re.compile(r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$")
REQUIRED_CONFIG_KEYS = {
"remote": bool,
"experience_level": dict,
"job_types": dict,
"date": dict,
"positions": list,
"locations": list,
"location_blacklist": list,
"distance": int,
"company_blacklist": list,
"title_blacklist": list,
}
EXPERIENCE_LEVELS = [
"internship",
"entry",
"associate",
"mid_senior_level",
"director",
"executive",
]
JOB_TYPES = [
"full_time",
"contract",
"part_time",
"temporary",
"internship",
"other",
"volunteer",
]
DATE_FILTERS = ["all_time", "month", "week", "24_hours"]
APPROVED_DISTANCES = {0, 5, 10, 25, 50, 100}
@staticmethod
def validate_email(email: str) -> bool:
"""Validate the format of an email address."""
return bool(ConfigValidator.EMAIL_REGEX.match(email))
@staticmethod
def load_yaml(yaml_path: Path) -> dict:
"""Load and parse a YAML file."""
try:
with open(yaml_path, "r") as stream:
return yaml.safe_load(stream)
except yaml.YAMLError as exc:
raise ConfigError(f"Error reading YAML file {yaml_path}: {exc}")
except FileNotFoundError:
raise ConfigError(f"YAML file not found: {yaml_path}")
@classmethod
def validate_config(cls, config_yaml_path: Path) -> dict:
"""Validate the main configuration YAML file."""
parameters = cls.load_yaml(config_yaml_path)
# Check for required keys and their types
for key, expected_type in cls.REQUIRED_CONFIG_KEYS.items():
if key not in parameters:
if key in ["company_blacklist", "title_blacklist", "location_blacklist"]:
parameters[key] = []
else:
raise ConfigError(f"Missing required key '{key}' in {config_yaml_path}")
elif not isinstance(parameters[key], expected_type):
if key in ["company_blacklist", "title_blacklist", "location_blacklist"] and parameters[key] is None:
parameters[key] = []
else:
raise ConfigError(
f"Invalid type for key '{key}' in {config_yaml_path}. Expected {expected_type.__name__}."
)
cls._validate_experience_levels(parameters["experience_level"], config_yaml_path)
cls._validate_job_types(parameters["job_types"], config_yaml_path)
cls._validate_date_filters(parameters["date"], config_yaml_path)
cls._validate_list_of_strings(parameters, ["positions", "locations"], config_yaml_path)
cls._validate_distance(parameters["distance"], config_yaml_path)
cls._validate_blacklists(parameters, config_yaml_path)
return parameters
@classmethod
def _validate_experience_levels(cls, experience_levels: dict, config_path: Path):
"""Ensure experience levels are booleans."""
for level in cls.EXPERIENCE_LEVELS:
if not isinstance(experience_levels.get(level), bool):
raise ConfigError(
f"Experience level '{level}' must be a boolean in {config_path}"
)
@classmethod
def _validate_job_types(cls, job_types: dict, config_path: Path):
"""Ensure job types are booleans."""
for job_type in cls.JOB_TYPES:
if not isinstance(job_types.get(job_type), bool):
raise ConfigError(
f"Job type '{job_type}' must be a boolean in {config_path}"
)
@classmethod
def _validate_date_filters(cls, date_filters: dict, config_path: Path):
"""Ensure date filters are booleans."""
for date_filter in cls.DATE_FILTERS:
if not isinstance(date_filters.get(date_filter), bool):
raise ConfigError(
f"Date filter '{date_filter}' must be a boolean in {config_path}"
)
@classmethod
def _validate_list_of_strings(cls, parameters: dict, keys: list, config_path: Path):
"""Ensure specified keys are lists of strings."""
for key in keys:
if not all(isinstance(item, str) for item in parameters[key]):
raise ConfigError(
f"'{key}' must be a list of strings in {config_path}"
)
@classmethod
def _validate_distance(cls, distance: int, config_path: Path):
"""Validate the distance value."""
if distance not in cls.APPROVED_DISTANCES:
raise ConfigError(
f"Invalid distance value '{distance}' in {config_path}. Must be one of: {cls.APPROVED_DISTANCES}"
)
@classmethod
def _validate_blacklists(cls, parameters: dict, config_path: Path):
"""Ensure blacklists are lists."""
for blacklist in ["company_blacklist", "title_blacklist", "location_blacklist"]:
if not isinstance(parameters.get(blacklist), list):
raise ConfigError(
f"'{blacklist}' must be a list in {config_path}"
)
if parameters[blacklist] is None:
parameters[blacklist] = []
@staticmethod
def validate_secrets(secrets_yaml_path: Path) -> str:
"""Validate the secrets YAML file and retrieve the LLM API key."""
secrets = ConfigValidator.load_yaml(secrets_yaml_path)
mandatory_secrets = ["llm_api_key"]
for secret in mandatory_secrets:
if secret not in secrets:
raise ConfigError(f"Missing secret '{secret}' in {secrets_yaml_path}")
if not secrets[secret]:
raise ConfigError(f"Secret '{secret}' cannot be empty in {secrets_yaml_path}")
return secrets["llm_api_key"]
class FileManager:
"""Handles file system operations and validations."""
REQUIRED_FILES = [SECRETS_YAML, WORK_PREFERENCES_YAML, PLAIN_TEXT_RESUME_YAML]
@staticmethod
def validate_data_folder(app_data_folder: Path) -> Tuple[Path, Path, Path, Path]:
"""Validate the existence of the data folder and required files."""
if not app_data_folder.is_dir():
raise FileNotFoundError(f"Data folder not found: {app_data_folder}")
missing_files = [file for file in FileManager.REQUIRED_FILES if not (app_data_folder / file).exists()]
if missing_files:
raise FileNotFoundError(f"Missing files in data folder: {', '.join(missing_files)}")
output_folder = app_data_folder / "output"
output_folder.mkdir(exist_ok=True)
return (
app_data_folder / SECRETS_YAML,
app_data_folder / WORK_PREFERENCES_YAML,
app_data_folder / PLAIN_TEXT_RESUME_YAML,
output_folder,
)
@staticmethod
def get_uploads(plain_text_resume_file: Path) -> Dict[str, Path]:
"""Convert resume file paths to a dictionary."""
if not plain_text_resume_file.exists():
raise FileNotFoundError(f"Plain text resume file not found: {plain_text_resume_file}")
uploads = {"plainTextResume": plain_text_resume_file}
return uploads
def create_cover_letter(parameters: dict, llm_api_key: str):
"""
Logic to create a CV.
"""
try:
logger.info("Generating a CV based on provided parameters.")
# Carica il resume in testo semplice
with open(parameters["uploads"]["plainTextResume"], "r", encoding="utf-8") as file:
plain_text_resume = file.read()
style_manager = StyleManager()
available_styles = style_manager.get_styles()
if not available_styles:
logger.warning("No styles available. Proceeding without style selection.")
else:
# Present style choices to the user
choices = style_manager.format_choices(available_styles)
questions = [
inquirer.List(
"style",
message="Select a style for the resume:",
choices=choices,
)
]
style_answer = inquirer.prompt(questions)
if style_answer and "style" in style_answer:
selected_choice = style_answer["style"]
for style_name, (file_name, author_link) in available_styles.items():
if selected_choice.startswith(style_name):
style_manager.set_selected_style(style_name)
logger.info(f"Selected style: {style_name}")
break
else:
logger.warning("No style selected. Proceeding with default style.")
questions = [
inquirer.Text('job_url', message="Please enter the URL of the job description:")
]
answers = inquirer.prompt(questions)
job_url = answers.get('job_url')
resume_generator = ResumeGenerator()
resume_object = Resume(plain_text_resume)
driver = init_browser()
resume_generator.set_resume_object(resume_object)
resume_facade = ResumeFacade(
api_key=llm_api_key,
style_manager=style_manager,
resume_generator=resume_generator,
resume_object=resume_object,
output_path=Path("data_folder/output"),
)
resume_facade.set_driver(driver)
resume_facade.link_to_job(job_url)
result_base64, suggested_name = resume_facade.create_cover_letter()
# Decodifica Base64 in dati binari
try:
pdf_data = base64.b64decode(result_base64)
except base64.binascii.Error as e:
logger.error("Error decoding Base64: %s", e)
raise
# Definisci il percorso della cartella di output utilizzando `suggested_name`
output_dir = Path(parameters["outputFileDirectory"]) / suggested_name
# Crea la cartella se non esiste
try:
output_dir.mkdir(parents=True, exist_ok=True)
logger.info(f"Cartella di output creata o già esistente: {output_dir}")
except IOError as e:
logger.error("Error creating output directory: %s", e)
raise
output_path = output_dir / "cover_letter_tailored.pdf"
try:
with open(output_path, "wb") as file:
file.write(pdf_data)
logger.info(f"CV salvato in: {output_path}")
except IOError as e:
logger.error("Error writing file: %s", e)
raise
except Exception as e:
logger.exception(f"An error occurred while creating the CV: {e}")
raise
def create_resume_pdf_job_tailored(parameters: dict, llm_api_key: str):
"""
Logic to create a CV.
"""
try:
logger.info("Generating a CV based on provided parameters.")
# Carica il resume in testo semplice
with open(parameters["uploads"]["plainTextResume"], "r", encoding="utf-8") as file:
plain_text_resume = file.read()
style_manager = StyleManager()
available_styles = style_manager.get_styles()
if not available_styles:
logger.warning("No styles available. Proceeding without style selection.")
else:
# Present style choices to the user
choices = style_manager.format_choices(available_styles)
questions = [
inquirer.List(
"style",
message="Select a style for the resume:",
choices=choices,
)
]
style_answer = inquirer.prompt(questions)
if style_answer and "style" in style_answer:
selected_choice = style_answer["style"]
for style_name, (file_name, author_link) in available_styles.items():
if selected_choice.startswith(style_name):
style_manager.set_selected_style(style_name)
logger.info(f"Selected style: {style_name}")
break
else:
logger.warning("No style selected. Proceeding with default style.")
questions = [inquirer.Text('job_url', message="Please enter the URL of the job description:")]
answers = inquirer.prompt(questions)
job_url = answers.get('job_url')
resume_generator = ResumeGenerator()
resume_object = Resume(plain_text_resume)
driver = init_browser()
resume_generator.set_resume_object(resume_object)
resume_facade = ResumeFacade(
api_key=llm_api_key,
style_manager=style_manager,
resume_generator=resume_generator,
resume_object=resume_object,
output_path=Path("data_folder/output"),
)
resume_facade.set_driver(driver)
resume_facade.link_to_job(job_url)
result_base64, suggested_name = resume_facade.create_resume_pdf_job_tailored()
# Decodifica Base64 in dati binari
try:
pdf_data = base64.b64decode(result_base64)
except base64.binascii.Error as e:
logger.error("Error decoding Base64: %s", e)
raise
# Definisci il percorso della cartella di output utilizzando `suggested_name`
output_dir = Path(parameters["outputFileDirectory"]) / suggested_name
# Crea la cartella se non esiste
try:
output_dir.mkdir(parents=True, exist_ok=True)
logger.info(f"Cartella di output creata o già esistente: {output_dir}")
except IOError as e:
logger.error("Error creating output directory: %s", e)
raise
output_path = output_dir / "resume_tailored.pdf"
try:
with open(output_path, "wb") as file:
file.write(pdf_data)
logger.info(f"CV salvato in: {output_path}")
except IOError as e:
logger.error("Error writing file: %s", e)
raise
except Exception as e:
logger.exception(f"An error occurred while creating the CV: {e}")
raise
def create_resume_pdf(parameters: dict, llm_api_key: str):
"""
Logic to create a CV.
"""
try:
logger.info("Generating a CV based on provided parameters.")
# Load the plain text resume
with open(parameters["uploads"]["plainTextResume"], "r", encoding="utf-8") as file:
plain_text_resume = file.read()
# Initialize StyleManager
style_manager = StyleManager()
available_styles = style_manager.get_styles()
if not available_styles:
logger.warning("No styles available. Proceeding without style selection.")
else:
# Present style choices to the user
choices = style_manager.format_choices(available_styles)
questions = [
inquirer.List(
"style",
message="Select a style for the resume:",
choices=choices,
)
]
style_answer = inquirer.prompt(questions)
if style_answer and "style" in style_answer:
selected_choice = style_answer["style"]
for style_name, (file_name, author_link) in available_styles.items():
if selected_choice.startswith(style_name):
style_manager.set_selected_style(style_name)
logger.info(f"Selected style: {style_name}")
break
else:
logger.warning("No style selected. Proceeding with default style.")
# Initialize the Resume Generator
resume_generator = ResumeGenerator()
resume_object = Resume(plain_text_resume)
driver = init_browser()
resume_generator.set_resume_object(resume_object)
# Create the ResumeFacade
resume_facade = ResumeFacade(
api_key=llm_api_key,
style_manager=style_manager,
resume_generator=resume_generator,
resume_object=resume_object,
output_path=Path("data_folder/output"),
)
resume_facade.set_driver(driver)
result_base64 = resume_facade.create_resume_pdf()
# Decode Base64 to binary data
try:
pdf_data = base64.b64decode(result_base64)
except base64.binascii.Error as e:
logger.error("Error decoding Base64: %s", e)
raise
# Define the output directory using `suggested_name`
output_dir = Path(parameters["outputFileDirectory"])
# Write the PDF file
output_path = output_dir / "resume_base.pdf"
try:
with open(output_path, "wb") as file:
file.write(pdf_data)
logger.info(f"Resume saved at: {output_path}")
except IOError as e:
logger.error("Error writing file: %s", e)
raise
except Exception as e:
logger.exception(f"An error occurred while creating the CV: {e}")
raise
def handle_inquiries(selected_actions: List[str], parameters: dict, llm_api_key: str):
"""
Decide which function to call based on the selected user actions.
:param selected_actions: List of actions selected by the user.
:param parameters: Configuration parameters dictionary.
:param llm_api_key: API key for the language model.
"""
try:
if selected_actions:
if "Generate Resume" == selected_actions:
logger.info("Crafting a standout professional resume...")
create_resume_pdf(parameters, llm_api_key)
if "Generate Resume Tailored for Job Description" == selected_actions:
logger.info("Customizing your resume to enhance your job application...")
create_resume_pdf_job_tailored(parameters, llm_api_key)
if "Generate Tailored Cover Letter for Job Description" == selected_actions:
logger.info("Designing a personalized cover letter to enhance your job application...")
create_cover_letter(parameters, llm_api_key)
else:
logger.warning("No actions selected. Nothing to execute.")
except Exception as e:
logger.exception(f"An error occurred while handling inquiries: {e}")
raise
def prompt_user_action() -> str:
"""
Use inquirer to ask the user which action they want to perform.
:return: Selected action.
"""
try:
questions = [
inquirer.List(
'action',
message="Select the action you want to perform:",
choices=[
"Generate Resume",
"Generate Resume Tailored for Job Description",
"Generate Tailored Cover Letter for Job Description",
],
),
]
answer = inquirer.prompt(questions)
if answer is None:
print("No answer provided. The user may have interrupted.")
return ""
return answer.get('action', "")
except Exception as e:
print(f"An error occurred: {e}")
return ""
def main():
"""Main entry point for the AIHawk Job Application Bot."""
try:
# Define and validate the data folder
data_folder = Path("data_folder")
secrets_file, config_file, plain_text_resume_file, output_folder = FileManager.validate_data_folder(data_folder)
# Validate configuration and secrets
config = ConfigValidator.validate_config(config_file)
llm_api_key = ConfigValidator.validate_secrets(secrets_file)
# Prepare parameters
config["uploads"] = FileManager.get_uploads(plain_text_resume_file)
config["outputFileDirectory"] = output_folder
# Interactive prompt for user to select actions
selected_actions = prompt_user_action()
# Handle selected actions and execute them
handle_inquiries(selected_actions, config, llm_api_key)
except ConfigError as ce:
logger.error(f"Configuration error: {ce}")
logger.error(
"Refer to the configuration guide for troubleshooting: "
"https://github.com/feder-cr/Auto_Jobs_Applier_AIHawk?tab=readme-ov-file#configuration"
)
except FileNotFoundError as fnf:
logger.error(f"File not found: {fnf}")
logger.error("Ensure all required files are present in the data folder.")
except RuntimeError as re:
logger.error(f"Runtime error: {re}")
logger.debug(traceback.format_exc())
except Exception as e:
logger.exception(f"An unexpected error occurred: {e}")
if __name__ == "__main__":
main()
================================================
FILE: requirements.txt
================================================
click
git+https://github.com/feder-cr/lib_resume_builder_AIHawk.git
httpx~=0.27.2
inputimeout==1.0.4
jsonschema==4.23.0
jsonschema-specifications==2023.12.1
langchain==0.2.11
langchain-anthropic
langchain-huggingface
langchain-community==0.2.10
langchain-core==0.2.36
langchain-google-genai==1.0.10
langchain-ollama==0.1.3
langchain-openai==0.1.17
langchain-text-splitters==0.2.2
langsmith==0.1.93
Levenshtein==0.25.1
loguru==0.7.2
openai==1.37.1
pdfminer.six==20221105
pytest>=8.3.3
python-dotenv~=1.0.1
PyYAML~=6.0.2
regex==2024.7.24
reportlab==4.2.2
selenium==4.9.1
webdriver-manager==4.0.2
pytest
pytest-mock
pytest-cov
undetected-chromedriver==3.5.5
================================================
FILE: src/__init__.py
================================================
================================================
FILE: src/job.py
================================================
from dataclasses import dataclass
from src.logging import logger
@dataclass
class Job:
role: str = ""
company: str = ""
location: str = ""
link: str = ""
apply_method: str = ""
description: str = ""
summarize_job_description: str = ""
recruiter_link: str = ""
resume_path: str = ""
cover_letter_path: str = ""
def formatted_job_information(self):
"""
Formats the job information as a markdown string.
"""
logger.debug(f"Formatting job information for job: {self.role} at {self.company}")
job_information = f"""
# Job Description
## Job Information
- Position: {self.role}
- At: {self.company}
- Location: {self.location}
- Recruiter Profile: {self.recruiter_link or 'Not available'}
## Description
{self.description or 'No description provided.'}
"""
formatted_information = job_information.strip()
logger.debug(f"Formatted job information: {formatted_information}")
return formatted_information
================================================
FILE: src/jobContext.py
================================================
from src.job import Job
from src.job_application import JobApplication
from dataclasses import dataclass
@dataclass
class JobContext:
job: Job = None
job_application: JobApplication = None
================================================
FILE: src/job_application_saver.py
================================================
from src.logging import logger
import os
import json
import shutil
from dataclasses import asdict
from config import JOB_APPLICATIONS_DIR
from job import Job
from job_application import JobApplication
# Base directory where all applications will be saved
BASE_DIR = JOB_APPLICATIONS_DIR
class ApplicationSaver:
def __init__(self, job_application: JobApplication):
self.job_application = job_application
self.job_application_files_path = None
# Function to create a directory for each job application
def create_application_directory(self):
job = self.job_application.job
# Create a unique directory name using the application ID and company name
dir_name = f"{job.id} - {job.company} {job.title}"
dir_path = os.path.join(BASE_DIR, dir_name)
# Create the directory if it doesn't exist
os.makedirs(dir_path, exist_ok=True)
self.job_application_files_path = dir_path
return dir_path
# Function to save the job application details as a JSON file
def save_application_details(self):
if self.job_application_files_path is None:
raise ValueError(
"Job application file path is not set. Please create the application directory first."
)
json_file_path = os.path.join(
self.job_application_files_path, "job_application.json"
)
with open(json_file_path, "w") as json_file:
json.dump(self.job_application.application, json_file, indent=4)
# Function to save files like Resume and CV
def save_file(self, dir_path, file_path, new_filename):
if dir_path is None:
raise ValueError("dir path cannot be None")
# Copy the file to the application directory with a new name
destination = os.path.join(dir_path, new_filename)
shutil.copy(file_path, destination)
# Function to save job description as a text file
def save_job_description(self):
if self.job_application_files_path is None:
raise ValueError(
"Job application file path is not set. Please create the application directory first."
)
job: Job = self.job_application.job
json_file_path = os.path.join(
self.job_application_files_path, "job_description.json"
)
with open(json_file_path, "w") as json_file:
json.dump(asdict(job), json_file, indent=4)
@staticmethod
def save(job_application: JobApplication):
saver = ApplicationSaver(job_application)
saver.create_application_directory()
saver.save_application_details()
saver.save_job_description()
# todo: tempory fix, to rely on resume and cv path from job object instead of job application object
if job_application.resume_path:
saver.save_file(
saver.job_application_files_path,
job_application.job.resume_path,
"resume.pdf",
)
logger.debug(f"Saving cover letter to path: {job_application.cover_letter_path}")
if job_application.cover_letter_path:
saver.save_file(
saver.job_application_files_path,
job_application.job.cover_letter_path,
"cover_letter.pdf"
)
================================================
FILE: src/libs/llm_manager.py
================================================
import json
import os
import re
import textwrap
import time
from abc import ABC, abstractmethod
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Union
import httpx
from dotenv import load_dotenv
from langchain_core.messages import BaseMessage
from langchain_core.messages.ai import AIMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompt_values import StringPromptValue
from langchain_core.prompts import ChatPromptTemplate
from Levenshtein import distance
import ai_hawk.llm.prompts as prompts
from config import JOB_SUITABILITY_SCORE
from src.utils.constants import (
AVAILABILITY,
CERTIFICATIONS,
CLAUDE,
COMPANY,
CONTENT,
COVER_LETTER,
EDUCATION_DETAILS,
EXPERIENCE_DETAILS,
FINISH_REASON,
GEMINI,
HUGGINGFACE,
ID,
INPUT_TOKENS,
INTERESTS,
JOB_APPLICATION_PROFILE,
JOB_DESCRIPTION,
LANGUAGES,
LEGAL_AUTHORIZATION,
LLM_MODEL_TYPE,
LOGPROBS,
MODEL,
MODEL_NAME,
OLLAMA,
OPENAI,
PERPLEXITY,
OPTIONS,
OUTPUT_TOKENS,
PERSONAL_INFORMATION,
PHRASE,
PROJECTS,
PROMPTS,
QUESTION,
REPLIES,
RESPONSE_METADATA,
RESUME,
RESUME_EDUCATIONS,
RESUME_JOBS,
RESUME_PROJECTS,
RESUME_SECTION,
SALARY_EXPECTATIONS,
SELF_IDENTIFICATION,
SYSTEM_FINGERPRINT,
TEXT,
TIME,
TOKEN_USAGE,
TOTAL_COST,
TOTAL_TOKENS,
USAGE_METADATA,
WORK_PREFERENCES,
)
from src.job import Job
from src.logging import logger
import config as cfg
load_dotenv()
class AIModel(ABC):
@abstractmethod
def invoke(self, prompt: str) -> str:
pass
class OpenAIModel(AIModel):
def __init__(self, api_key: str, llm_model: str):
from langchain_openai import ChatOpenAI
self.model = ChatOpenAI(
model_name=llm_model, openai_api_key=api_key, temperature=0.4
)
def invoke(self, prompt: str) -> BaseMessage:
logger.debug("Invoking OpenAI API")
response = self.model.invoke(prompt)
return response
class ClaudeModel(AIModel):
def __init__(self, api_key: str, llm_model: str):
from langchain_anthropic import ChatAnthropic
self.model = ChatAnthropic(model=llm_model, api_key=api_key, temperature=0.4)
def invoke(self, prompt: str) -> BaseMessage:
response = self.model.invoke(prompt)
logger.debug("Invoking Claude API")
return response
class OllamaModel(AIModel):
def __init__(self, llm_model: str, llm_api_url: str):
from langchain_ollama import ChatOllama
if len(llm_api_url) > 0:
logger.debug(f"Using Ollama with API URL: {llm_api_url}")
self.model = ChatOllama(model=llm_model, base_url=llm_api_url)
else:
self.model = ChatOllama(model=llm_model)
def invoke(self, prompt: str) -> BaseMessage:
response = self.model.invoke(prompt)
return response
class PerplexityModel(AIModel):
def __init__(self, api_key: str, llm_model: str):
from langchain_community.chat_models import ChatPerplexity
self.model = ChatPerplexity(model=llm_model, api_key=api_key, temperature=0.4)
def invoke(self, prompt: str) -> BaseMessage:
response = self.model.invoke(prompt)
return response
# gemini doesn't seem to work because API doesn't rstitute answers for questions that involve answers that are too short
class GeminiModel(AIModel):
def __init__(self, api_key: str, llm_model: str):
from langchain_google_genai import (
ChatGoogleGenerativeAI,
HarmBlockThreshold,
HarmCategory,
)
self.model = ChatGoogleGenerativeAI(
model=llm_model,
google_api_key=api_key,
safety_settings={
HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_DEROGATORY: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_TOXICITY: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_VIOLENCE: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_SEXUAL: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_MEDICAL: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_DANGEROUS: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
},
)
def invoke(self, prompt: str) -> BaseMessage:
response = self.model.invoke(prompt)
return response
class HuggingFaceModel(AIModel):
def __init__(self, api_key: str, llm_model: str):
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
self.model = HuggingFaceEndpoint(
repo_id=llm_model, huggingfacehub_api_token=api_key, temperature=0.4
)
self.chatmodel = ChatHuggingFace(llm=self.model)
def invoke(self, prompt: str) -> BaseMessage:
response = self.chatmodel.invoke(prompt)
logger.debug(
f"Invoking Model from Hugging Face API. Response: {response}, Type: {type(response)}"
)
return response
class AIAdapter:
def __init__(self, config: dict, api_key: str):
self.model = self._create_model(config, api_key)
def _create_model(self, config: dict, api_key: str) -> AIModel:
llm_model_type = cfg.LLM_MODEL_TYPE
llm_model = cfg.LLM_MODEL
llm_api_url = cfg.LLM_API_URL
logger.debug(f"Using {llm_model_type} with {llm_model}")
if llm_model_type == OPENAI:
return OpenAIModel(api_key, llm_model)
elif llm_model_type == CLAUDE:
return ClaudeModel(api_key, llm_model)
elif llm_model_type == OLLAMA:
return OllamaModel(llm_model, llm_api_url)
elif llm_model_type == GEMINI:
return GeminiModel(api_key, llm_model)
elif llm_model_type == HUGGINGFACE:
return HuggingFaceModel(api_key, llm_model)
elif llm_model_type == PERPLEXITY:
return PerplexityModel(api_key, llm_model)
else:
raise ValueError(f"Unsupported model type: {llm_model_type}")
def invoke(self, prompt: str) -> str:
return self.model.invoke(prompt)
class LLMLogger:
def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, GeminiModel]):
self.llm = llm
logger.debug(f"LLMLogger successfully initialized with LLM: {llm}")
@staticmethod
def log_request(prompts, parsed_reply: Dict[str, Dict]):
logger.debug("Starting log_request method")
logger.debug(f"Prompts received: {prompts}")
logger.debug(f"Parsed reply received: {parsed_reply}")
try:
calls_log = os.path.join(Path("data_folder/output"), "open_ai_calls.json")
logger.debug(f"Logging path determined: {calls_log}")
except Exception as e:
logger.error(f"Error determining the log path: {str(e)}")
raise
if isinstance(prompts, StringPromptValue):
logger.debug("Prompts are of type StringPromptValue")
prompts = prompts.text
logger.debug(f"Prompts converted to text: {prompts}")
elif isinstance(prompts, Dict):
logger.debug("Prompts are of type Dict")
try:
prompts = {
f"prompt_{i + 1}": prompt.content
for i, prompt in enumerate(prompts.messages)
}
logger.debug(f"Prompts converted to dictionary: {prompts}")
except Exception as e:
logger.error(f"Error converting prompts to dictionary: {str(e)}")
raise
else:
logger.debug("Prompts are of unknown type, attempting default conversion")
try:
prompts = {
f"prompt_{i + 1}": prompt.content
for i, prompt in enumerate(prompts.messages)
}
logger.debug(
f"Prompts converted to dictionary using default method: {prompts}"
)
except Exception as e:
logger.error(f"Error converting prompts using default method: {str(e)}")
raise
try:
current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
logger.debug(f"Current time obtained: {current_time}")
except Exception as e:
logger.error(f"Error obtaining current time: {str(e)}")
raise
try:
token_usage = parsed_reply[USAGE_METADATA]
output_tokens = token_usage[OUTPUT_TOKENS]
input_tokens = token_usage[INPUT_TOKENS]
total_tokens = token_usage[TOTAL_TOKENS]
logger.debug(
f"Token usage - Input: {input_tokens}, Output: {output_tokens}, Total: {total_tokens}"
)
except KeyError as e:
logger.error(f"KeyError in parsed_reply structure: {str(e)}")
raise
try:
model_name = parsed_reply[RESPONSE_METADATA][MODEL_NAME]
logger.debug(f"Model name: {model_name}")
except KeyError as e:
logger.error(f"KeyError in response_metadata: {str(e)}")
raise
try:
prompt_price_per_token = 0.00000015
completion_price_per_token = 0.0000006
total_cost = (input_tokens * prompt_price_per_token) + (
output_tokens * completion_price_per_token
)
logger.debug(f"Total cost calculated: {total_cost}")
except Exception as e:
logger.error(f"Error calculating total cost: {str(e)}")
raise
try:
log_entry = {
MODEL: model_name,
TIME: current_time,
PROMPTS: prompts,
REPLIES: parsed_reply[CONTENT],
TOTAL_TOKENS: total_tokens,
INPUT_TOKENS: input_tokens,
OUTPUT_TOKENS: output_tokens,
TOTAL_COST: total_cost,
}
logger.debug(f"Log entry created: {log_entry}")
except KeyError as e:
logger.error(
f"Error creating log entry: missing key {str(e)} in parsed_reply"
)
raise
try:
with open(calls_log, "a", encoding="utf-8") as f:
json_string = json.dumps(log_entry, ensure_ascii=False, indent=4)
f.write(json_string + "\n")
logger.debug(f"Log entry written to file: {calls_log}")
except Exception as e:
logger.error(f"Error writing log entry to file: {str(e)}")
raise
class LoggerChatModel:
def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, GeminiModel]):
self.llm = llm
logger.debug(f"LoggerChatModel successfully initialized with LLM: {llm}")
def __call__(self, messages: List[Dict[str, str]]) -> str:
logger.debug(f"Entering __call__ method with messages: {messages}")
while True:
try:
logger.debug("Attempting to call the LLM with messages")
reply = self.llm.invoke(messages)
logger.debug(f"LLM response received: {reply}")
parsed_reply = self.parse_llmresult(reply)
logger.debug(f"Parsed LLM reply: {parsed_reply}")
LLMLogger.log_request(prompts=messages, parsed_reply=parsed_reply)
logger.debug("Request successfully logged")
return reply
except httpx.HTTPStatusError as e:
logger.error(f"HTTPStatusError encountered: {str(e)}")
if e.response.status_code == 429:
retry_after = e.response.headers.get("retry-after")
retry_after_ms = e.response.headers.get("retry-after-ms")
if retry_after:
wait_time = int(retry_after)
logger.warning(
f"Rate limit exceeded. Waiting for {wait_time} seconds before retrying (extracted from 'retry-after' header)..."
)
time.sleep(wait_time)
elif retry_after_ms:
wait_time = int(retry_after_ms) / 1000.0
logger.warning(
f"Rate limit exceeded. Waiting for {wait_time} seconds before retrying (extracted from 'retry-after-ms' header)..."
)
time.sleep(wait_time)
else:
wait_time = 30
logger.warning(
f"'retry-after' header not found. Waiting for {wait_time} seconds before retrying (default)..."
)
time.sleep(wait_time)
else:
logger.error(
f"HTTP error occurred with status code: {e.response.status_code}, waiting 30 seconds before retrying"
)
time.sleep(30)
except Exception as e:
logger.error(f"Unexpected error occurred: {str(e)}")
logger.info(
"Waiting for 30 seconds before retrying due to an unexpected error."
)
time.sleep(30)
continue
def parse_llmresult(self, llmresult: AIMessage) -> Dict[str, Dict]:
logger.debug(f"Parsing LLM result: {llmresult}")
try:
if hasattr(llmresult, USAGE_METADATA):
content = llmresult.content
response_metadata = llmresult.response_metadata
id_ = llmresult.id
usage_metadata = llmresult.usage_metadata
parsed_result = {
CONTENT: content,
RESPONSE_METADATA: {
MODEL_NAME: response_metadata.get(
MODEL_NAME, ""
),
SYSTEM_FINGERPRINT: response_metadata.get(
SYSTEM_FINGERPRINT, ""
),
FINISH_REASON: response_metadata.get(
FINISH_REASON, ""
),
LOGPROBS: response_metadata.get(
LOGPROBS, None
),
},
ID: id_,
USAGE_METADATA: {
INPUT_TOKENS: usage_metadata.get(
INPUT_TOKENS, 0
),
OUTPUT_TOKENS: usage_metadata.get(
OUTPUT_TOKENS, 0
),
TOTAL_TOKENS: usage_metadata.get(
TOTAL_TOKENS, 0
),
},
}
else:
content = llmresult.content
response_metadata = llmresult.response_metadata
id_ = llmresult.id
token_usage = response_metadata[TOKEN_USAGE]
parsed_result = {
CONTENT: content,
RESPONSE_METADATA: {
MODEL_NAME: response_metadata.get(
MODEL, ""
),
FINISH_REASON: response_metadata.get(
FINISH_REASON, ""
),
},
ID: id_,
USAGE_METADATA: {
INPUT_TOKENS: token_usage.prompt_tokens,
OUTPUT_TOKENS: token_usage.completion_tokens,
TOTAL_TOKENS: token_usage.total_tokens,
},
}
logger.debug(f"Parsed LLM result successfully: {parsed_result}")
return parsed_result
except KeyError as e:
logger.error(f"KeyError while parsing LLM result: missing key {str(e)}")
raise
except Exception as e:
logger.error(f"Unexpected error while parsing LLM result: {str(e)}")
raise
class GPTAnswerer:
def __init__(self, config, llm_api_key):
self.ai_adapter = AIAdapter(config, llm_api_key)
self.llm_cheap = LoggerChatModel(self.ai_adapter)
@property
def job_description(self):
return self.job.description
@staticmethod
def find_best_match(text: str, options: list[str]) -> str:
logger.debug(f"Finding best match for text: '{text}' in options: {options}")
distances = [
(option, distance(text.lower(), option.lower())) for option in options
]
best_option = min(distances, key=lambda x: x[1])[0]
logger.debug(f"Best match found: {best_option}")
return best_option
@staticmethod
def _remove_placeholders(text: str) -> str:
logger.debug(f"Removing placeholders from text: {text}")
text = text.replace("PLACEHOLDER", "")
return text.strip()
@staticmethod
def _preprocess_template_string(template: str) -> str:
logger.debug("Preprocessing template string")
return textwrap.dedent(template)
def set_resume(self, resume):
logger.debug(f"Setting resume: {resume}")
self.resume = resume
def set_job(self, job: Job):
logger.debug(f"Setting job: {job}")
self.job = job
self.job.set_summarize_job_description(
self.summarize_job_description(self.job.description)
)
def set_job_application_profile(self, job_application_profile):
logger.debug(f"Setting job application profile: {job_application_profile}")
self.job_application_profile = job_application_profile
def _clean_llm_output(self, output: str) -> str:
return output.replace("*", "").replace("#", "").strip()
def summarize_job_description(self, text: str) -> str:
logger.debug(f"Summarizing job description: {text}")
prompts.summarize_prompt_template = self._preprocess_template_string(
prompts.summarize_prompt_template
)
prompt = ChatPromptTemplate.from_template(prompts.summarize_prompt_template)
chain = prompt | self.llm_cheap | StrOutputParser()
raw_output = chain.invoke({TEXT: text})
output = self._clean_llm_output(raw_output)
logger.debug(f"Summary generated: {output}")
return output
def _create_chain(self, template: str):
logger.debug(f"Creating chain with template: {template}")
prompt = ChatPromptTemplate.from_template(template)
return prompt | self.llm_cheap | StrOutputParser()
def answer_question_textual_wide_range(self, question: str) -> str:
logger.debug(f"Answering textual question: {question}")
chains = {
PERSONAL_INFORMATION: self._create_chain(
prompts.personal_information_template
),
SELF_IDENTIFICATION: self._create_chain(
prompts.self_identification_template
),
LEGAL_AUTHORIZATION: self._create_chain(
prompts.legal_authorization_template
),
WORK_PREFERENCES: self._create_chain(
prompts.work_preferences_template
),
EDUCATION_DETAILS: self._create_chain(
prompts.education_details_template
),
EXPERIENCE_DETAILS: self._create_chain(
prompts.experience_details_template
),
PROJECTS: self._create_chain(prompts.projects_template),
AVAILABILITY: self._create_chain(prompts.availability_template),
SALARY_EXPECTATIONS: self._create_chain(
prompts.salary_expectations_template
),
CERTIFICATIONS: self._create_chain(
prompts.certifications_template
),
LANGUAGES: self._create_chain(prompts.languages_template),
INTERESTS: self._create_chain(prompts.interests_template),
COVER_LETTER: self._create_chain(prompts.coverletter_template),
}
prompt = ChatPromptTemplate.from_template(prompts.determine_section_template)
chain = prompt | self.llm_cheap | StrOutputParser()
raw_output = chain.invoke({QUESTION: question})
output = self._clean_llm_output(raw_output)
match = re.search(
r"(Personal information|Self Identification|Legal Authorization|Work Preferences|Education "
r"Details|Experience Details|Projects|Availability|Salary "
r"Expectations|Certifications|Languages|Interests|Cover letter)",
output,
re.IGNORECASE,
)
if not match:
raise ValueError("Could not extract section name from the response.")
section_name = match.group(1).lower().replace(" ", "_")
if section_name == "cover_letter":
chain = chains.get(section_name)
raw_output = chain.invoke(
{
RESUME: self.resume,
JOB_DESCRIPTION: self.job_description,
COMPANY: self.job.company,
}
)
output = self._clean_llm_output(raw_output)
logger.debug(f"Cover letter generated: {output}")
return output
resume_section = getattr(self.resume, section_name, None) or getattr(
self.job_application_profile, section_name, None
)
if resume_section is None:
logger.error(
f"Section '{section_name}' not found in either resume or job_application_profile."
)
raise ValueError(
f"Section '{section_name}' not found in either resume or job_application_profile."
)
chain = chains.get(section_name)
if chain is None:
logger.error(f"Chain not defined for section '{section_name}'")
raise ValueError(f"Chain not defined for section '{section_name}'")
raw_output = chain.invoke(
{RESUME_SECTION: resume_section, QUESTION: question}
)
output = self._clean_llm_output(raw_output)
logger.debug(f"Question answered: {output}")
return output
def answer_question_numeric(
self, question: str, default_experience: str = 3
) -> str:
logger.debug(f"Answering numeric question: {question}")
func_template = self._preprocess_template_string(
prompts.numeric_question_template
)
prompt = ChatPromptTemplate.from_template(func_template)
chain = prompt | self.llm_cheap | StrOutputParser()
raw_output_str = chain.invoke(
{
RESUME_EDUCATIONS: self.resume.education_details,
RESUME_JOBS: self.resume.experience_details,
RESUME_PROJECTS: self.resume.projects,
QUESTION: question,
}
)
output_str = self._clean_llm_output(raw_output_str)
logger.debug(f"Raw output for numeric question: {output_str}")
try:
output = self.extract_number_from_string(output_str)
logger.debug(f"Extracted number: {output}")
except ValueError:
logger.warning(
f"Failed to extract number, using default experience: {default_experience}"
)
output = default_experience
return output
def extract_number_from_string(self, output_str):
logger.debug(f"Extracting number from string: {output_str}")
numbers = re.findall(r"\d+", output_str)
if numbers:
logger.debug(f"Numbers found: {numbers}")
return str(numbers[0])
else:
logger.error("No numbers found in the string")
raise ValueError("No numbers found in the string")
def answer_question_from_options(self, question: str, options: list[str]) -> str:
logger.debug(f"Answering question from options: {question}")
func_template = self._preprocess_template_string(prompts.options_template)
prompt = ChatPromptTemplate.from_template(func_template)
chain = prompt | self.llm_cheap | StrOutputParser()
raw_output_str = chain.invoke(
{
RESUME: self.resume,
JOB_APPLICATION_PROFILE: self.job_application_profile,
QUESTION: question,
OPTIONS: options,
}
)
output_str = self._clean_llm_output(raw_output_str)
logger.debug(f"Raw output for options question: {output_str}")
best_option = self.find_best_match(output_str, options)
logger.debug(f"Best option determined: {best_option}")
return best_option
def determine_resume_or_cover(self, phrase: str) -> str:
logger.debug(
f"Determining if phrase refers to resume or cover letter: {phrase}"
)
prompt = ChatPromptTemplate.from_template(
prompts.resume_or_cover_letter_template
)
chain = prompt | self.llm_cheap | StrOutputParser()
raw_response = chain.invoke({PHRASE: phrase})
response = self._clean_llm_output(raw_response)
logger.debug(f"Response for resume_or_cover: {response}")
if "resume" in response:
return "resume"
elif "cover" in response:
return "cover"
else:
return "resume"
def is_job_suitable(self):
logger.info("Checking if job is suitable")
prompt = ChatPromptTemplate.from_template(prompts.is_relavant_position_template)
chain = prompt | self.llm_cheap | StrOutputParser()
raw_output = chain.invoke(
{
RESUME: self.resume,
JOB_DESCRIPTION: self.job_description,
}
)
output = self._clean_llm_output(raw_output)
logger.debug(f"Job suitability output: {output}")
try:
score = re.search(r"Score:\s*(\d+)", output, re.IGNORECASE).group(1)
reasoning = re.search(r"Reasoning:\s*(.+)", output, re.IGNORECASE | re.DOTALL).group(1)
except AttributeError:
logger.warning("Failed to extract score or reasoning from LLM. Proceeding with application, but job may or may not be suitable.")
return True
logger.info(f"Job suitability score: {score}")
if int(score) < JOB_SUITABILITY_SCORE:
logger.debug(f"Job is not suitable: {reasoning}")
return int(score) >= JOB_SUITABILITY_SCORE
================================================
FILE: src/libs/resume_and_cover_builder/__init__.py
================================================
__version__ = '0.1'
# Import all the necessary classes and functions, called when the package is imported
from .resume_generator import ResumeGenerator
from .style_manager import StyleManager
from .resume_facade import ResumeFacade
================================================
FILE: src/libs/resume_and_cover_builder/config.py
================================================
"""
This module is used to store the global configuration of the application.
"""
# app/libs/resume_and_cover_builder/config.py
from pathlib import Path
class GlobalConfig:
def __init__(self):
self.STRINGS_MODULE_RESUME_PATH: Path = None
self.STRINGS_MODULE_RESUME_JOB_DESCRIPTION_PATH: Path = None
self.STRINGS_MODULE_COVER_LETTER_JOB_DESCRIPTION_PATH: Path = None
self.STRINGS_MODULE_NAME: str = None
self.STYLES_DIRECTORY: Path = None
self.LOG_OUTPUT_FILE_PATH: Path = None
self.API_KEY: str = None
self.html_template = """
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Resume</title>
<link href="https://fonts.googleapis.com/css2?family=Barlow:wght@400;600&display=swap" rel="stylesheet" />
<link href="https://fonts.googleapis.com/css2?family=Barlow:wght@400;600&display=swap" rel="stylesheet" />
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css" />
<style>
$style_css
</style>
</head>
<body>
$body
</body>
</html>
"""
global_config = GlobalConfig()
================================================
FILE: src/libs/resume_and_cover_builder/cover_letter_prompt/__init__.py
================================================
================================================
FILE: src/libs/resume_and_cover_builder/cover_letter_prompt/strings_feder-cr.py
================================================
from src.libs.resume_and_cover_builder.template_base import prompt_cover_letter_template
cover_letter_template = """
Compose a brief and impactful cover letter based on the provided job description and resume. The letter should be no longer than three paragraphs and should be written in a professional, yet conversational tone. Avoid using any placeholders, and ensure that the letter flows naturally and is tailored to the job.
Analyze the job description to identify key qualifications and requirements. Introduce the candidate succinctly, aligning their career objectives with the role. Highlight relevant skills and experiences from the resume that directly match the job’s demands, using specific examples to illustrate these qualifications. Reference notable aspects of the company, such as its mission or values, that resonate with the candidate’s professional goals. Conclude with a strong statement of why the candidate is a good fit for the position, expressing a desire to discuss further.
Please write the cover letter in a way that directly addresses the job role and the company’s characteristics, ensuring it remains concise and engaging without unnecessary embellishments. The letter should be formatted into paragraphs and should not include a greeting or signature.
## Rules:
- Do not include any introductions, explanations, or additional information.
## Details :
- **Job Description:**
```
{job_description}
```
- **My resume:**
```
{resume}
```
"""+ prompt_cover_letter_template
summarize_prompt_template = """
As a seasoned HR expert, your task is to identify and outline the key skills and requirements necessary for the position of this job. Use the provided job description as input to extract all relevant information. This will involve conducting a thorough analysis of the job's responsibilities and the industry standards. You should consider both the technical and soft skills needed to excel in this role. Additionally, specify any educational qualifications, certifications, or experiences that are essential. Your analysis should also reflect on the evolving nature of this role, considering future trends and how they might affect the required competencies.
Rules:
Remove boilerplate text
Include only relevant information to match the job description against the resume
# Analysis Requirements
Your analysis should include the following sections:
Technical Skills: List all the specific technical skills required for the role based on the responsibilities described in the job description.
Soft Skills: Identify the necessary soft skills, such as communication abilities, problem-solving, time management, etc.
Educational Qualifications and Certifications: Specify the essential educational qualifications and certifications for the role.
Professional Experience: Describe the relevant work experiences that are required or preferred.
Role Evolution: Analyze how the role might evolve in the future, considering industry trends and how these might influence the required skills.
# Final Result:
Your analysis should be structured in a clear and organized document with distinct sections for each of the points listed above. Each section should contain:
This comprehensive overview will serve as a guideline for the recruitment process, ensuring the identification of the most qualified candidates.
# Job Description:
```
{text}
```
---
# Job Description Summary"""
================================================
FILE: src/libs/resume_and_cover_builder/llm/llm_generate_cover_letter_from_job.py
================================================
"""
This creates the cover letter (in html, utils will then convert in PDF) matching with job description and plain-text resume
"""
# app/libs/resume_and_cover_builder/llm_generate_cover_letter_from_job.py
import os
import textwrap
from ..utils import LoggerChatModel
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from pathlib import Path
from dotenv import load_dotenv
from requests.exceptions import HTTPError as HTTPStatusError
from pathlib import Path
from loguru import logger
# Load environment variables from .env file
load_dotenv()
# Configure log file
log_folder = 'log/cover_letter/gpt_cover_letter_job_descr'
if not os.path.exists(log_folder):
os.makedirs(log_folder)
log_path = Path(log_folder).resolve()
logger.add(log_path / "gpt_cover_letter_job_descr.log", rotation="1 day", compression="zip", retention="7 days", level="DEBUG")
class LLMCoverLetterJobDescription:
def __init__(self, openai_api_key, strings):
self.llm_cheap = LoggerChatModel(ChatOpenAI(model_name="gpt-4o-mini", openai_api_key=openai_api_key, temperature=0.4))
self.llm_embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
self.strings = strings
@staticmethod
def _preprocess_template_string(template: str) -> str:
"""
Preprocess the template string by removing leading whitespace and indentation.
Args:
template (str): The template string to preprocess.
Returns:
str: The preprocessed template string.
"""
return textwrap.dedent(template)
def set_resume(self, resume) -> None:
"""
Set the resume text to be used for generating the cover letter.
Args:
resume (str): The plain text resume to be used.
"""
self.resume = resume
def set_job_description_from_text(self, job_description_text) -> None:
"""
Set the job description text to be used for generating the cover letter.
Args:
job_description_text (str): The plain text job description to be used.
"""
logger.debug("Starting job description summarization...")
prompt = ChatPromptTemplate.from_template(self.strings.summarize_prompt_template)
chain = prompt | self.llm_cheap | StrOutputParser()
output = chain.invoke({"text": job_description_text})
self.job_description = output
logger.debug(f"Job description summarization complete: {self.job_description}")
def generate_cover_letter(self) -> str:
"""
Generate the cover letter based on the job description and resume.
Returns:
str: The generated cover letter
"""
logger.debug("Starting cover letter generation...")
prompt_template = self._preprocess_template_string(self.strings.cover_letter_template)
logger.debug(f"Cover letter template after preprocessing: {prompt_template}")
prompt = ChatPromptTemplate.from_template(prompt_template)
logger.debug(f"Prompt created: {prompt}")
chain = prompt | self.llm_cheap | StrOutputParser()
logger.debug(f"Chain created: {chain}")
input_data = {
"job_description": self.job_description,
"resume": self.resume
}
logger.debug(f"Input data: {input_data}")
output = chain.invoke(input_data)
logger.debug(f"Cover letter generation result: {output}")
logger.debug("Cover letter generation completed")
return output
================================================
FILE: src/libs/resume_and_cover_builder/llm/llm_generate_resume.py
================================================
"""
Create a class that generates a resume based on a resume and a resume template.
"""
# app/libs/resume_and_cover_builder/gpt_resume.py
import os
import textwrap
from src.libs.resume_and_cover_builder.utils import LoggerChatModel
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from dotenv import load_dotenv
from concurrent.futures import ThreadPoolExecutor, as_completed
from loguru import logger
from pathlib import Path
# Load environment variables from .env file
load_dotenv()
# Configure log file
log_folder = 'log/resume/gpt_resume'
if not os.path.exists(log_folder):
os.makedirs(log_folder)
log_path = Path(log_folder).resolve()
logger.add(log_path / "gpt_resume.log", rotation="1 day", compression="zip", retention="7 days", level="DEBUG")
class LLMResumer:
def __init__(self, openai_api_key, strings):
self.llm_cheap = LoggerChatModel(
ChatOpenAI(
model_name="gpt-4o-mini", openai_api_key=openai_api_key, temperature=0.4
)
)
self.strings = strings
@staticmethod
def _preprocess_template_string(template: str) -> str:
"""
Preprocess the template string by removing leading whitespace and indentation.
Args:
template (str): The template string to preprocess.
Returns:
str: The preprocessed template string.
"""
return textwrap.dedent(template)
def set_resume(self, resume) -> None:
"""
Set the resume object to be used for generating the resume.
Args:
resume (Resume): The resume object to be used.
"""
self.resume = resume
def generate_header(self, data = None) -> str:
"""
Generate the header section of the resume.
Args:
data (dict): The personal information to use for generating the header.
Returns:
str: The generated header section.
"""
header_prompt_template = self._preprocess_template_string(
self.strings.prompt_header
)
prompt = ChatPromptTemplate.from_template(header_prompt_template)
chain = prompt | self.llm_cheap | StrOutputParser()
input_data = {
"personal_information": self.resume.personal_information
} if data is None else data
output = chain.invoke(input_data)
return output
def generate_education_section(self, data = None) -> str:
"""
Generate the education section of the resume.
Args:
data (dict): The education details to use for generating the education section.
Returns:
str: The generated education section.
"""
logger.debug("Starting education section generation")
education_prompt_template = self._preprocess_template_string(self.strings.prompt_education)
logger.debug(f"Education template: {education_prompt_template}")
prompt = ChatPromptTemplate.from_template(education_prompt_template)
logger.debug(f"Prompt: {prompt}")
chain = prompt | self.llm_cheap | StrOutputParser()
logger.debug(f"Chain created: {chain}")
input_data = {
"education_details": self.resume.education_details
} if data is None else data
output = chain.invoke(input_data)
logger.debug(f"Chain invocation result: {output}")
logger.debug("Education section generation completed")
return output
def generate_work_experience_section(self, data = None) -> str:
"""
Generate the work experience section of the resume.
Args:
data (dict): The work experience details to use for generating the work experience section.
Returns:
str: The generated work experience section.
"""
logger.debug("Starting work experience section generation")
work_experience_prompt_template = self._preprocess_template_string(self.strings.prompt_working_experience)
logger.debug(f"Work experience template: {work_experience_prompt_template}")
prompt = ChatPromptTemplate.from_template(work_experience_prompt_template)
logger.debug(f"Prompt: {prompt}")
chain = prompt | self.llm_cheap | StrOutputParser()
logger.debug(f"Chain created: {chain}")
input_data = {
"experience_details": self.resume.experience_details
} if data is None else data
output = chain.invoke(input_data)
logger.debug(f"Chain invocation result: {output}")
logger.debug("Work experience section generation completed")
return output
def generate_projects_section(self, data = None) -> str:
"""
Generate the side projects section of the resume.
Args:
data (dict): The side projects to use for generating the side projects section.
Returns:
str: The generated side projects section.
"""
logger.debug("Starting side projects section generation")
projects_prompt_template = self._preprocess_template_string(self.strings.prompt_projects)
logger.debug(f"Side projects template: {projects_prompt_template}")
prompt = ChatPromptTemplate.from_template(projects_prompt_template)
logger.debug(f"Prompt: {prompt}")
chain = prompt | self.llm_cheap | StrOutputParser()
logger.debug(f"Chain created: {chain}")
input_data = {
"projects": self.resume.projects
} if data is None else data
output = chain.invoke(input_data)
logger.debug(f"Chain invocation result: {output}")
logger.debug("Side projects section generation completed")
return output
def generate_achievements_section(self, data = None) -> str:
"""
Generate the achievements section of the resume.
Args:
data (dict): The achievements to use for generating the achievements section.
Returns:
str: The generated achievements section.
"""
logger.debug("Starting achievements section generation")
achievements_prompt_template = self._preprocess_template_string(self.strings.prompt_achievements)
logger.debug(f"Achievements template: {achievements_prompt_template}")
prompt = ChatPromptTemplate.from_template(achievements_prompt_template)
logger.debug(f"Prompt: {prompt}")
chain = prompt | self.llm_cheap | StrOutputParser()
logger.debug(f"Chain created: {chain}")
input_data = {
"achievements": self.resume.achievements,
"certifications": self.resume.certifications,
} if data is None else data
logger.debug(f"Input data for the chain: {input_data}")
output = chain.invoke(input_data)
logger.debug(f"Chain invocation result: {output}")
logger.debug("Achievements section generation completed")
return output
def generate_certifications_section(self, data = None) -> str:
"""
Generate the certifications section of the resume.
Returns:
str: The generated certifications section.
"""
logger.debug("Starting Certifications section generation")
certifications_prompt_template = self._preprocess_template_string(self.strings.prompt_certifications)
logger.debug(f"Certifications template: {certifications_prompt_template}")
prompt = ChatPromptTemplate.from_template(certifications_prompt_template)
logger.debug(f"Prompt: {prompt}")
chain = prompt | self.llm_cheap | StrOutputParser()
logger.debug(f"Chain created: {chain}")
input_data = {
"certifications": self.resume.certifications
} if data is None else data
logger.debug(f"Input data for the chain: {input_data}")
output = chain.invoke(input_data)
logger.debug(f"Chain invocation result: {output}")
logger.debug("Certifications section generation completed")
return output
def generate_additional_skills_section(self, data = None) -> str:
"""
Generate the additional skills section of the resume.
Returns:
str: The generated additional skills section.
"""
additional_skills_prompt_template = self._preprocess_template_string(self.strings.prompt_additional_skills)
skills = set()
if self.resume.experience_details:
for exp in self.resume.experience_details:
if exp.skills_acquired:
skills.update(exp.skills_acquired)
if self.resume.education_details:
for edu in self.resume.education_details:
if edu.exam:
for exam in edu.exam:
skills.update(exam.keys())
prompt = ChatPromptTemplate.from_template(additional_skills_prompt_template)
chain = prompt | self.llm_cheap | StrOutputParser()
input_data = {
"languages": self.resume.languages,
"interests": self.resume.interests,
"skills": skills,
} if data is None else data
output = chain.invoke(input_data)
return output
def generate_html_resume(self) -> str:
"""
Generate the full HTML resume based on the resume object.
Returns:
str: The generated HTML resume.
"""
def header_fn():
if self.resume.personal_information:
return self.generate_header()
return ""
def education_fn():
if self.resume.education_details:
return self.generate_education_section()
return ""
def work_experience_fn():
if self.resume.experience_details:
return self.generate_work_experience_section()
return ""
def projects_fn():
if self.resume.projects:
return self.generate_projects_section()
return ""
def achievements_fn():
if self.resume.achievements:
return self.generate_achievements_section()
return ""
def certifications_fn():
if self.resume.certifications:
return self.generate_certifications_section()
return ""
def additional_skills_fn():
if (self.resume.experience_details or self.resume.education_details or
self.resume.languages or self.resume.interests):
return self.generate_additional_skills_section()
return ""
# Create a dictionary to map the function names to their respective callables
functions = {
"header": header_fn,
"education": education_fn,
"work_experience": work_experience_fn,
"projects": projects_fn,
"achievements": achievements_fn,
"certifications": certifications_fn,
"additional_skills": additional_skills_fn,
}
# Use ThreadPoolExecutor to run the functions in parallel
with ThreadPoolExecutor() as executor:
future_to_section = {executor.submit(fn): section for section, fn in functions.items()}
results = {}
for future in as_completed(future_to_section):
section = future_to_section[future]
try:
result = future.result()
if result:
results[section] = result
except Exception as exc:
logger.error(f'{section} raised an exception: {exc}')
full_resume = "<body>\n"
full_resume += f" {results.get('header', '')}\n"
full_resume += " <main>\n"
full_resume += f" {results.get('education', '')}\n"
full_resume += f" {results.get('work_experience', '')}\n"
full_resume += f" {results.get('projects', '')}\n"
full_resume += f" {results.get('achievements', '')}\n"
full_resume += f" {results.get('certifications', '')}\n"
full_resume += f" {results.get('additional_skills', '')}\n"
full_resume += " </main>\n"
full_resume += "</body>"
return full_resume
================================================
FILE: src/libs/resume_and_cover_builder/llm/llm_generate_resume_from_job.py
================================================
"""
Create a class that generates a job description based on a resume and a job description template.
"""
# app/libs/resume_and_cover_builder/llm_generate_resume_from_job.py
import os
from src.libs.resume_and_cover_builder.llm.llm_generate_resume import LLMResumer
from src.libs.resume_and_cover_builder.utils import LoggerChatModel
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from dotenv import load_dotenv
from loguru import logger
from pathlib import Path
# Load environment variables from .env file
load_dotenv()
log_folder = 'log/resume/gpt_resum_job_descr'
if not os.path.exists(log_folder):
os.makedirs(log_folder)
log_path = Path(log_folder).resolve()
logger.add(log_path / "gpt_resum_job_descr.log", rotation="1 day", compression="zip", retention="7 days", level="DEBUG")
class LLMResumeJobDescription(LLMResumer):
def __init__(self, openai_api_key, strings):
super().__init__(openai_api_key, strings)
def set_job_description_from_text(self, job_description_text) -> None:
"""
Set the job description text to be used for generating the resume.
Args:
job_description_text (str): The plain text job description to be used.
"""
prompt = ChatPromptTemplate.from_template(self.strings.summarize_prompt_template)
chain = prompt | self.llm_cheap | StrOutputParser()
output = chain.invoke({"text": job_description_text})
self.job_description = output
def generate_header(self) -> str:
"""
Generate the header section of the resume.
Returns:
str: The generated header section.
"""
return super().generate_header(data={
"personal_information": self.resume.personal_information,
"job_description": self.job_description
})
def generate_education_section(self) -> str:
"""
Generate the education section of the resume.
Returns:
str: The generated education section.
"""
return super().generate_education_section(data={
"education_details": self.resume.education_details,
"job_description": self.job_description
})
def generate_work_experience_section(self) -> str:
"""
Generate the work experience section of the resume.
Returns:
str: The generated work experience section.
"""
return super().generate_work_experience_section(data={
"experience_details": self.resume.experience_details,
"job_description": self.job_description
})
def generate_projects_section(self) -> str:
"""
Generate the side projects section of the resume.
Returns:
str: The generated side projects section.
"""
return super().generate_projects_section(data={
"projects": self.resume.projects,
"job_description": self.job_description
})
def generate_achievements_section(self) -> str:
"""
Generate the achievements section of the resume.
Returns:
str: The generated achievements section.
"""
return super().generate_achievements_section(data={
"achievements": self.resume.achievements,
"job_description": self.job_description
})
def generate_certifications_section(self) -> str:
"""
Generate the certifications section of the resume.
Returns:
str: The generated certifications section.
"""
return super().generate_certifications_section(data={
"certifications": self.resume.certifications,
"job_description": self.job_description
})
def generate_additional_skills_section(self) -> str:
"""
Generate the additional skills section of the resume.
Returns:
str: The generated additional skills section.
"""
additional_skills_prompt_template = self._preprocess_template_string(
self.strings.prompt_additional_skills
)
skills = set()
if self.resume.experience_details:
for exp in self.resume.experience_details:
if exp.skills_acquired:
skills.update(exp.skills_acquired)
if self.resume.education_details:
for edu in self.resume.education_details:
if edu.exam:
for exam in edu.exam:
skills.update(exam.keys())
prompt = ChatPromptTemplate.from_template(additional_skills_prompt_template)
chain = prompt | self.llm_cheap | StrOutputParser()
output = chain.invoke({
"languages": self.resume.languages,
"interests": self.resume.interests,
"skills": skills,
"job_description": self.job_description
})
return output
================================================
FILE: src/libs/resume_and_cover_builder/llm/llm_job_parser.py
================================================
import os
import tempfile
import textwrap
import time
import re # For email validation
from src.libs.resume_and_cover_builder.utils import LoggerChatModel
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
from langchain_openai import ChatOpenAI
from dotenv import load_dotenv
from concurrent.futures import ThreadPoolExecutor, as_completed
from loguru import logger
from pathlib import Path
from langchain_core.prompt_values import StringPromptValue
from langchain_core.runnables import RunnablePassthrough
from langchain_text_splitters import TokenTextSplitter
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from lib_resume_builder_AIHawk.config import global_config
from langchain_community.document_loaders import TextLoader
from requests.exceptions import HTTPError as HTTPStatusError # HTTP error handling
import openai
# Load environment variables from the .env file
load_dotenv()
# Configure the log file
log_folder = 'log/resume/gpt_resume'
if not os.path.exists(log_folder):
os.makedirs(log_folder)
log_path = Path(log_folder).resolve()
logger.add(log_path / "gpt_resume.log", rotation="1 day", compression="zip", retention="7 days", level="DEBUG")
class LLMParser:
def __init__(self, openai_api_key):
self.llm = LoggerChatModel(
ChatOpenAI(
model_name="gpt-4o-mini", openai_api_key=openai_api_key, temperature=0.4
)
)
self.llm_embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) # Initialize embeddings
self.vectorstore = None # Will be initialized after document loading
@staticmethod
def _preprocess_template_string(template: str) -> str:
"""
Preprocess the template string by removing leading whitespaces and indentation.
Args:
template (str): The template string to preprocess.
Returns:
str: The preprocessed template string.
"""
return textwrap.dedent(template)
def set_body_html(self, body_html):
"""
Retrieves the job description from HTML, processes it, and initializes the vectorstore.
Args:
body_html (str): The HTML content to process.
"""
# Save the HTML content to a temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix=".html", mode="w", encoding="utf-8") as temp_file:
temp_file.write(body_html)
temp_file_path = temp_file.name
try:
loader = TextLoader(temp_file_path, encoding="utf-8", autodetect_encoding=True)
document = loader.load()
logger.debug("Document successfully loaded.")
except Exception as e:
logger.error(f"Error during document loading: {e}")
raise
finally:
os.remove(temp_file_path)
logger.debug(f"Temporary file removed: {temp_file_path}")
# Split the text into chunks
text_splitter = TokenTextSplitter(chunk_size=500, chunk_overlap=50)
all_splits = text_splitter.split_documents(document)
logger.debug(f"Text split into {len(all_splits)} fragments.")
# Create the vectorstore using FAISS
try:
self.vectorstore = FAISS.from_documents(documents=all_splits, embedding=self.llm_embeddings)
logger.debug("Vectorstore successfully initialized.")
except Exception as e:
logger.error(f"Error during vectorstore creation: {e}")
raise
def _retrieve_context(self, query: str, top_k: int = 3) -> str:
"""
Retrieves the most relevant text fragments using the retriever.
Args:
query (str): The search query.
top_k (int): Number of fragments to retrieve.
Returns:
str: Concatenated text fragments.
"""
if not self.vectorstore:
raise ValueError("Vectorstore not initialized. Run extract_job_description first.")
retriever = self.vectorstore.as_retriever()
retrieved_docs = retriever.get_relevant_documents(query)[:top_k]
context = "\n\n".join(doc.page_content for doc in retrieved_docs)
logger.debug(f"Context retrieved for query '{query}': {context[:200]}...") # Log the first 200 characters
return context
def _extract_information(self, question: str, retrieval_query: str) -> str:
"""
Generic method to extract specific information using the retriever and LLM.
Args:
question (str): The question to ask the LLM for extraction.
retrieval_query (str): The query to use for retrieving relevant context.
Returns:
str: The extracted information.
"""
context = self._retrieve_context(retrieval_query)
prompt = ChatPromptTemplate.from_template(
template="""
You are an expert in extracting specific information from job descriptions.
Carefully read the job description context below and provide a clear and concise answer to the question.
Context: {context}
Question: {question}
Answer:
"""
)
formatted_prompt = prompt.format(context=context, question=question)
logger.debug(f"Formatted prompt for extraction: {formatted_prompt[:200]}...") # Log the first 200 characters
try:
chain = prompt | self.llm | StrOutputParser()
result = chain.invoke({"context": context, "question": question})
extracted_info = result.strip()
logger.debug(f"Extracted information: {extracted_info}")
return extracted_info
except Exception as e:
logger.error(f"Error during information extraction: {e}")
return ""
def extract_job_description(self) -> str:
"""
Extracts the company name from the job description.
Returns:
str: The extracted job description.
"""
question = "What is the job description of the company?"
retrieval_query = "Job description"
logger.debug("Starting job description extraction.")
return self._extract_information(question, retrieval_query)
def extract_company_name(self) -> str:
"""
Extracts the company name from the job description.
Returns:
str: The extracted company name.
"""
question = "What is the company's name?"
retrieval_query = "Company name"
logger.debug("Starting company name extraction.")
return self._extract_information(question, retrieval_query)
def extract_role(self) -> str:
"""
Extracts the sought role/title from the job description.
Returns:
str: The extracted role/title.
"""
question = "What is the role or title sought in this job description?"
retrieval_query = "Job title"
logger.debug("Starting role/title extraction.")
return self._extract_information(question, retrieval_query)
def extract_location(self) -> str:
"""
Extracts the location from the job description.
Returns:
str: The extracted location.
"""
question = "What is the location mentioned in this job description?"
retrieval_query = "Location"
logger.debug("Starting location extraction.")
return self._extract_information(question, retrieval_query)
def extract_recruiter_email(self) -> str:
"""
Extracts the recruiter's email from the job description.
Returns:
str: The extracted recruiter's email.
"""
question = "What is the recruiter's email address in this job description?"
retrieval_query = "Recruiter email"
logger.debug("Starting recruiter email extraction.")
email = self._extract_information(question, retrieval_query)
# Validate the extracted email using regex
email_regex = r'[\w\.-]+@[\w\.-]+\.\w+'
if re.match(email_regex, email):
logger.debug("Valid recruiter's email.")
return email
else:
logger.warning("Invalid or not found recruiter's email.")
return ""
================================================
FILE: src/libs/resume_and_cover_builder/module_loader.py
================================================
"""
This module is used to store the global configuration of the application.
"""
# app/libs/resume_and_cover_builder/module_loader.py
import importlib
import sys
def load_module(module_path: str, module_name: str):
spec = importlib.util.spec_from_file_location(module_name, module_path)
module = importlib.util.module_from_spec(spec)
sys.modules[module_name] = module
spec.loader.exec_module(module)
return module
================================================
FILE: src/libs/resume_and_cover_builder/resume_facade.py
================================================
"""
This module contains the FacadeManager class, which is responsible for managing the interaction between the user and other components of the application.
"""
# app/libs/resume_and_cover_builder/manager_facade.py
import hashlib
import inquirer
from pathlib import Path
from loguru import logger
from src.libs.resume_and_cover_builder.llm.llm_job_parser import LLMParser
from src.job import Job
from src.utils.chrome_utils import HTML_to_PDF
from .config import global_config
class ResumeFacade:
def __init__(self, api_key, style_manager, resume_generator, resume_object, output_path):
"""
Initialize the FacadeManager with the given API key, style manager, resume generator, resume object, and log path.
Args:
api_key (str): The OpenAI API key to be used for generating text.
style_manager (StyleManager): The StyleManager instance to manage the styles.
resume_generator (ResumeGenerator): The ResumeGenerator instance to generate resumes and cover letters.
resume_object (str): The resume object to be used for generating resumes and cover letters.
output_path (str): The path to the log file.
"""
lib_directory = Path(__file__).resolve().parent
global_config.STRINGS_MODULE_RESUME_PATH = lib_directory / "resume_prompt/strings_feder-cr.py"
global_config.STRINGS_MODULE_RESUME_JOB_DESCRIPTION_PATH = lib_directory / "resume_job_description_prompt/strings_feder-cr.py"
global_config.STRINGS_MODULE_COVER_LETTER_JOB_DESCRIPTION_PATH = lib_directory / "cover_letter_prompt/strings_feder-cr.py"
global_config.STRINGS_MODULE_NAME = "strings_feder_cr"
global_config.STYLES_DIRECTORY = lib_directory / "resume_style"
global_config.LOG_OUTPUT_FILE_PATH = output_path
global_config.API_KEY = api_key
self.style_manager = style_manager
self.resume_generator = resume_generator
self.resume_generator.set_resume_object(resume_object)
self.selected_style = None # Property to store the selected style
def set_driver(self, driver):
self.driver = driver
def prompt_user(self, choices: list[str], message: str) -> str:
"""
Prompt the user with the given message and choices.
Args:
choices (list[str]): The list of choices to present to the user.
message (str): The message to display to the user.
Returns:
str: The choice selected by the user.
"""
questions = [
inquirer.List('selection', message=message, choices=choices),
]
return inquirer.prompt(questions)['selection']
def prompt_for_text(self, message: str) -> str:
"""
Prompt the user to enter text with the given message.
Args:
message (str): The message to display to the user.
Returns:
str: The text entered by the user.
"""
questions = [
inquirer.Text('text', message=message),
]
return inquirer.prompt(questions)['text']
def link_to_job(self, job_url):
self.driver.get(job_url)
self.driver.implicitly_wait(10)
body_element = self.driver.find_element("tag name", "body")
body_element = body_element.get_attribute("outerHTML")
self.llm_job_parser = LLMParser(openai_api_key=global_config.API_KEY)
self.llm_job_parser.set_body_html(body_element)
self.job = Job()
self.job.role = self.llm_job_parser.extract_role()
self.job.company = self.llm_job_parser.extract_company_name()
self.job.description = self.llm_job_parser.extract_job_description()
self.job.location = self.llm_job_parser.extract_location()
self.job.link = job_url
logger.info(f"Extracting job details from URL: {job_url}")
def create_resume_pdf_job_tailored(self) -> tuple[bytes, str]:
"""
Create a resume PDF using the selected style and the given job description text.
Args:
job_url (str): The job URL to generate the hash for.
job_description_text (str): The job description text to include in the resume.
Returns:
tuple: A tuple containing the PDF content as bytes and the unique filename.
"""
style_path = self.style_manager.get_style_path()
if style_path is None:
raise ValueError("You must choose a style before generating the PDF.")
html_resume = self.resume_generator.create_resume_job_description_text(style_path, self.job.description)
# Generate a unique name using the job URL hash
suggested_name = hashlib.md5(self.job.link.encode()).hexdigest()[:10]
result = HTML_to_PDF(html_resume, self.driver)
self.driver.quit()
return result, suggested_name
def create_resume_pdf(self) -> tuple[bytes, str]:
"""
Create a resume PDF using the selected style and the given job description text.
Args:
job_url (str): The job URL to generate the hash for.
job_description_text (str): The job description text to include in the resume.
Returns:
tuple: A tuple containing the PDF content as bytes and the unique filename.
"""
style_path = self.style_manager.get_style_path()
if style_path is None:
raise ValueError("You must choose a style before generating the PDF.")
html_resume = self.resume_generator.create_resume(style_path)
result = HTML_to_PDF(html_resume, self.driver)
self.driver.quit()
return result
def create_cover_letter(self) -> tuple[bytes, str]:
"""
Create a cover letter based on the given job description text and job URL.
Args:
job_url (str): The job URL to generate the hash for.
job_description_text (str): The job description text to include in the cover letter.
Returns:
tuple: A tuple containing the PDF content as bytes and the unique filename.
"""
style_path = self.style_manager.get_style_path()
if style_path is None:
raise ValueError("You must choose a style before generating the PDF.")
cover_letter_html = self.resume_generator.create_cover_letter_job_description(style_path, self.job.description)
# Generate a unique name using the job URL hash
suggested_name = hashlib.md5(self.job.link.encode()).hexdigest()[:10]
result = HTML_to_PDF(cover_letter_html, self.driver)
self.driver.quit()
return result, suggested_name
================================================
FILE: src/libs/resume_and_cover_builder/resume_generator.py
================================================
"""
This module is responsible for generating resumes and cover letters using the LLM model.
"""
# app/libs/resume_and_cover_builder/resume_generator.py
from string import Template
from typing import Any
from src.libs.resume_and_cover_builder.llm.llm_generate_resume import LLMResumer
from src.libs.resume_and_cover_builder.llm.llm_generate_resume_from_job import LLMResumeJobDescription
from src.libs.resume_and_cover_builder.llm.llm_generate_cover_letter_from_job import LLMCoverLetterJobDescription
from .module_loader import load_module
from .config import global_config
class ResumeGenerator:
def __init__(self):
pass
def set_resume_object(self, resume_object):
self.resume_object = resume_object
def _create_resume(self, gpt_answerer: Any, style_path):
# Imposta il resume nell'oggetto gpt_answerer
gpt_answerer.set_resume(self.resume_object)
# Leggi il template HTML
template = Template(global_config.html_template)
try:
with open(style_path, "r") as f:
style_css = f.read() # Correzione: chiama il metodo `read` con le parentesi
except FileNotFoundError:
raise ValueError(f"Il file di stile non è stato trovato nel percorso: {style_path}")
except Exception as e:
raise RuntimeError(f"Errore durante la lettura del file CSS: {e}")
# Genera l'HTML del resume
body_html = gpt_answerer.generate_html_resume()
# Applica i contenuti al template
return template.substitute(body=body_html, style_css=style_css)
def create_resume(self, style_path):
strings = load_module(global_config.STRINGS_MODULE_RESUME_PATH, global_config.STRINGS_MODULE_NAME)
gpt_answerer = LLMResumer(global_config.API_KEY, strings)
return self._create_resume(gpt_answerer, style_path)
def create_resume_job_description_text(self, style_path: str, job_description_text: str):
strings = load_module(global_config.STRINGS_MODULE_RESUME_JOB_DESCRIPTION_PATH, global_config.STRINGS_MODULE_NAME)
gpt_answerer = LLMResumeJobDescription(global_config.API_KEY, strings)
gpt_answerer.set_job_description_from_text(job_description_text)
return self._create_resume(gpt_answerer, style_path)
def create_cover_letter_job_description(self, style_path: str, job_description_text: str):
strings = load_module(global_config.STRINGS_MODULE_COVER_LETTER_JOB_DESCRIPTION_PATH, global_config.STRINGS_MODULE_NAME)
gpt_answerer = LLMCoverLetterJobDescription(global_config.API_KEY, strings)
gpt_answerer.set_resume(self.resume_object)
gpt_answerer.set_job_description_from_text(job_description_text)
cover_letter_html = gpt_answerer.generate_cover_letter()
template = Template(global_config.html_template)
with open(style_path, "r") as f:
style_css = f.read()
return template.substitute(body=cover_letter_html, style_css=style_css)
================================================
FILE: src/libs/resume_and_cover_builder/resume_job_description_prompt/__init__.py
================================================
================================================
FILE: src/libs/resume_and_cover_builder/resume_job_description_prompt/strings_feder-cr.py
================================================
from src.libs.resume_and_cover_builder.template_base import prompt_header_template, prompt_education_template, prompt_working_experience_template, prompt_projects_template, prompt_additional_skills_template, prompt_certifications_template, prompt_achievements_template
prompt_header = """
Act as an HR expert and resume writer specializing in ATS-friendly resumes. Your task is to create a professional and polished header for the resume. The header should:
1. **Contact Information**: Include your full name, city and country, phone number, email address, LinkedIn profile, and GitHub profile.
2. **Formatting**: Ensure the contact details are presented clearly and are easy to read.
To implement this:
- If any of the contact information fields (e.g., LinkedIn profile, GitHub profile) are not provided (i.e., `None`), omit them from the header.
- **My information:**
{personal_information}
""" + prompt_header_template
prompt_education = """
Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to articulate the educational background for a resume, ensuring it aligns with the provided job description. For each educational entry, ensure you include:
1. **Institution Name and Location**: Specify the university or educational institution’s name and location.
2. **Degree and Field of Study**: Clearly indicate the degree earned and the field of study.
3. **Grade**: Include your Grade if it is strong and relevant.
4. **Relevant Coursework**: List key courses with their grades to showcase your academic strengths. If no coursework is provided, omit this section from the template.
To implement this, follow these steps:
- If the exam details are not provided (i.e., `None`), skip the coursework section when filling out the template.
- If the exam details are available, fill out the coursework section accordingly.
- **My information:**
{education_details}
- **Job Description:**
{job_description}
"""+ prompt_education_template
prompt_working_experience = """
Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to detail the work experience for a resume, ensuring it aligns with the provided job description. For each job entry, ensure you include:
1. **Company Name and Location**: Provide the name of the company and its location.
2. **Job Title**: Clearly state your job title.
3. **Dates of Employment**: Include the start and end dates of your employment.
4. **Responsibilities and Achievements**: Describe your key responsibilities and notable achievements, emphasizing measurable results and specific contributions.
Ensure that the descriptions highlight relevant experience and align with the job description.
To implement this:
- If any of the work experience details (e.g., responsibilities, achievements) are not provided (i.e., `None`), omit those sections when filling out the template.
- **My information:**
{experience_details}
- **Job Description:**
{job_description}
"""+ prompt_working_experience_template
prompt_projects = """
Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to highlight notable side projects based on the provided job description. For each project, ensure you include:
1. **Project Name and Link**: Provide the name of the project and include a link to the GitHub repository or project page.
2. **Project Details**: Describe any notable recognition or achievements related to the project, such as GitHub stars or community feedback.
3. **Technical Contributions**: Highlight your specific contributions and the technologies used in the project.
Ensure that the project descriptions demonstrate your skills and achievements relevant to the job description.
To implement this:
- If any of the project details (e.g., link, achievements) are not provided (i.e., `None`), omit those sections when filling out the template.
- **My information:**
{projects}
- **Job Description:**
{job_description}
"""+ prompt_projects_template
prompt_achievements = """
Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to list significant achievements based on the provided job description. For each achievement, ensure you include:
1. **Award or Recognition**: Clearly state the name of the award, recognition, scholarship, or honor.
2. **Description**: Provide a brief description of the achievement and its relevance to your career or academic journey.
Ensure that the achievements are clearly presented and effectively highlight your accomplishments.
To implement this:
- If any of the achievement details (e.g., certifications, descriptions) are not provided (i.e., `None`), omit those sections when filling out the template.
- **My information:**
{achievements}
- **Job Description:**
{job_description}
"""+ prompt_achievements_template
prompt_certifications = """
Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to list significant certifications based on the provided details. For each certification, ensure you include:
1. **Certification Name**: Clearly state the name of the certification.
2. **Description**: Provide a brief description of the certification and its relevance to your professional or academic career.
Ensure that the certifications are clearly presented and effectively highlight your qualifications.
To implement this:
If any of the certification details (e.g., descriptions) are not provided (i.e., None), omit those sections when filling out the template.
- **My information:**
{certifications}
- **Job Description:**
{job_description}
"""+ prompt_certifications_template
prompt_additional_skills = """
Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to list additional skills relevant to the job. For each skill, ensure you include:
Do not add any information beyond what is listed in the provided data fields. Only use the information provided in the 'languages', 'interests', and 'skills' fields to formulate your responses. Avoid extrapolating or incorporating details from the job description or other external sources.
1. **Skill Category**: Clearly state the category or type of skill.
2. **Specific Skills**: List the specific skills or technologies within each category.
3. **Proficiency and Experience**: Briefly describe your experience and proficiency level.
Ensure that the skills listed are relevant and accurately reflect your expertise in the field.
To implement this:
- If any of the skill details (e.g., languages, interests, skills) are not provided (i.e., `None`), omit those sections when filling out the template.
- **My information:**
{languages}
{interests}
{skills}
- **Job Description:**
{job_description}
"""+ prompt_additional_skills_template
summarize_prompt_template = """
As a seasoned HR expert, your task is to identify and outline the key skills and requirements necessary for the position of this job. Use the provided job description as input to extract all relevant information. This will involve conducting a thorough analysis of the job's responsibilities and the industry standards. You should consider both the technical and soft skills needed to excel in this role. Additionally, specify any educational qualifications, certifications, or experiences that are essential. Your analysis should also reflect on the evolving nature of this role, considering future trends and how they might affect the required competencies.
Rules:
Remove boilerplate text
Include only relevant information to match the job description against the resume
# Analysis Requirements
Your analysis should include the following sections:
Technical Skills: List all the specific technical skills required for the role based on the responsibilities described in the job description.
Soft Skills: Identify the necessary soft skills, such as communication abilities, problem-solving, time management, etc.
Educational Qualifications and Certifications: Specify the essential educational qualifications and certifications for the role.
Professional Experience: Describe the relevant work experiences that are required or preferred.
Role Evolution: Analyze how the role might evolve in the future, considering industry trends and how these might influence the required skills.
# Final Result:
Your analysis should be structured in a clear and organized document with distinct sections for each of the points listed above. Each section should contain:
This comprehensive overview will serve as a guideline for the recruitment process, ensuring the identification of the most qualified candidates.
# Job Description:
```
{text}
```
---
# Job Description Summary"""
================================================
FILE: src/libs/resume_and_cover_builder/resume_prompt/__init__.py
================================================
================================================
FILE: src/libs/resume_and_cover_builder/resume_prompt/strings_feder-cr.py
================================================
from src.libs.resume_and_cover_builder.template_base import prompt_header_template, prompt_education_template, prompt_working_experience_template, prompt_projects_template, prompt_achievements_template, prompt_certifications_template, prompt_additional_skills_template
prompt_header = """
Act as an HR expert and resume writer specializing in ATS-friendly resumes. Your task is to create a professional and polished header for the resume. The header should:
1. **Contact Information**: Include your full name, city and country, phone number, email address, LinkedIn profile, and GitHub profile. Exclude any information that is not provided.
2. **Formatting**: Ensure the contact details are presented clearly and are easy to read.
- **My information:**
{personal_information}
""" + prompt_header_template
prompt_education = """
Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to articulate the educational background for a resume. For each educational entry, ensure you include:
1. **Institution Name and Location**: Specify the university or educational institution’s name and location.
2. **Degree and Field of Study**: Clearly indicate the degree earned and the field of study.
3. **Grade**: Include your Grade if it is strong and relevant.
4. **Relevant Coursework**: List key courses with their grades to showcase your academic strengths.
- **My information:**
{education_details}
"""+ prompt_education_template
prompt_working_experience = """
Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to detail the work experience for a resume. For each job entry, ensure you include:
1. **Company Name and Location**: Provide the name of the company and its location.
2. **Job Title**: Clearly state your job title.
3. **Dates of Employment**: Include the start and end dates of your employment.
4. **Responsibilities and Achievements**: Describe your key responsibilities and notable achievements, emphasizing measurable results and specific contributions.
- **My information:**
{experience_details}
"""+ prompt_working_experience_template
prompt_projects = """
Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to highlight notable side projects. For each project, ensure you include:
1. **Project Name and Link**: Provide the name of the project and include a link to the GitHub repository or project page.
2. **Project Details**: Describe any notable recognition or achievements related to the project, such as GitHub stars or community feedback.
3. **Technical Contributions**: Highlight your specific contributions and the technologies used in the project.
- **My information:**
{projects}
"""+ prompt_projects_template
prompt_achievements = """
Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to list significant achievements. For each achievement, ensure you include:
1. **Award or Recognition**: Clearly state the name of the award, recognition, scholarship, or honor.
2. **Description**: Provide a brief description of the achievement and its relevance to your career or academic journey.
- **My information:**
{achievements}
"""+ prompt_achievements_template
prompt_certifications = """
Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to list significant certifications based on the provided details. For each certification, ensure you include:
1. **Certification Name**: Clearly state the name of the certification.
2. **Description**: Provide a brief description of the certification and its relevance to your professional or academic career.
Ensure that the certifications are clearly presented and effectively highlight your qualifications.
To implement this:
If any of the certification details (e.g., descriptions) are not provided (i.e., None), omit those sections when filling out the template.
- **My information:**
{certifications}
"""+ prompt_certifications_template
prompt_additional_skills = """
Act as an HR expert and resume writer with a specialization in creating ATS-friendly resumes. Your task is to list additional skills relevant to the job. For each skill, ensure you include:
1. **Skill Category**: Clearly state the category or type of skill.
2. **Specific Skills**: List the specific skills or technologies within each category.
3. **Proficiency and Experience**: Briefly describe your experience and proficiency level.
- **My information:**
{languages}
{interests}
{skills}
"""+ prompt_additional_skills_template
================================================
FILE: src/libs/resume_and_cover_builder/resume_style/__init__.py
================================================
================================================
FILE: src/libs/resume_and_cover_builder/resume_style/style_cloyola.css
================================================
/*Cloyola Grey $https://github.com/cloyola*/
@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600;700&display=swap');
@import url('https://fonts.googleapis.com/css2?family=Roboto:ital,wght@0,100;0,300;0,400;0,500;0,700;0,900;1,100;1,300;1,400;1,500;1,700;1,900&display=swap');
body {
font-family: 'Roboto', sans-serif;
line-height: 1.4;
color: #333;
max-width: 700px;
margin: 0 auto;
padding: 10px;
font-size: 9pt;
}
header {
text-align: left;
margin-bottom: 20px;
background-color: #7c7c7c40;
padding: 20px;
border-radius: 8px;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
h1 {
font-size: 18pt;
font-weight: 700;
margin: 0 0 5px 0;
}
.contact-info {
display: flex;
justify-content: left;
flex-wrap: wrap;
gap: 10px;
font-size: 9pt;
font-weight: normal;
}
.contact-info p {
margin: 0;
}
.contact-info a {
color: #0077b5;
text-decoration: none;
}
.fab,
.fas {
margin-right: 3px;
}
span.entry-location {
font-weight: normal;
}
h2 {
font-size: 14pt;
font-weight: 600;
border-bottom: 1px dotted #4c4c4c;
padding-bottom: 2px;
margin: 10px 0 5px 0;
text-align: left;
}
.entry {
margin-bottom: 15px; /*margin-bottom: 8px;*/
background-color: #fff;
padding: 15px;
border-radius: 8px;
box-shadow: 3px 3px 5px 2px rgba(0, 0, 0, 0.2);
}
.entry-header {
display: flex;
justify-content: space-between;
font-weight: 600;
font-size: 10pt;
}
.entry-details {
display: flex;
justify-content: space-between;
font-style: italic;
margin-bottom: 2px;
font-size: 9pt;
}
.compact-list {
margin: 2px 0;
padding-left: 15px;
}
.compact-list li {
margin-bottom: 2px;
}
.two-column {
display: flex;
justify-content: space-between;
}
.two-column ul {
width: 48%;
margin: 0;
padding-left: 15px;
list-style-type: circle;
}
a {
color: #0077b5;
text-decoration: none;
}
a:hover {
text-decoration: underline;
}
@media print {
body {
padding: 0;
margin: 0;
font-size: 9pt;
}
@page {
margin: 0.5cm;
}
h1 {
font-size: 18pt;
}
h2 {
font-size: 11pt;
}
.contact-info {
font-size: 8pt;
}
.entry-details {
font-size: 7pt;
}
.compact-list {
padding-left: 12px;
}
}
================================================
FILE: src/libs/resume_and_cover_builder/resume_style/style_josylad_blue.css
================================================
/*Modern Blue$https://github.com/josylad*/
@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600;700&display=swap');
body {
font-family: 'Poppins', sans-serif;
line-height: 1.6;
colo
gitextract_396899n1/
├── .github/
│ ├── CODE_OF_CONDUCT.md
│ ├── CONTRIBUTING.md
│ ├── FUNDING.yml
│ ├── ISSUE_TEMPLATE/
│ │ ├── bug-issue.yml
│ │ ├── config.yml
│ │ ├── documentation-issue.yml
│ │ └── enhancement-issue.yml
│ └── workflows/
│ ├── ci.yml
│ └── stale.yml
├── .gitignore
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── assets/
│ └── resume_schema.yaml
├── config.py
├── data_folder/
│ ├── plain_text_resume.yaml
│ ├── secrets.yaml
│ └── work_preferences.yaml
├── data_folder_example/
│ ├── plain_text_resume.yaml
│ ├── resume_liam_murphy.txt
│ ├── secrets.yaml
│ └── work_preferences.yaml
├── main.py
├── requirements.txt
└── src/
├── __init__.py
├── job.py
├── jobContext.py
├── job_application_saver.py
├── libs/
│ ├── llm_manager.py
│ └── resume_and_cover_builder/
│ ├── __init__.py
│ ├── config.py
│ ├── cover_letter_prompt/
│ │ ├── __init__.py
│ │ └── strings_feder-cr.py
│ ├── llm/
│ │ ├── llm_generate_cover_letter_from_job.py
│ │ ├── llm_generate_resume.py
│ │ ├── llm_generate_resume_from_job.py
│ │ └── llm_job_parser.py
│ ├── module_loader.py
│ ├── resume_facade.py
│ ├── resume_generator.py
│ ├── resume_job_description_prompt/
│ │ ├── __init__.py
│ │ └── strings_feder-cr.py
│ ├── resume_prompt/
│ │ ├── __init__.py
│ │ └── strings_feder-cr.py
│ ├── resume_style/
│ │ ├── __init__.py
│ │ ├── style_cloyola.css
│ │ ├── style_josylad_blue.css
│ │ ├── style_josylad_grey.css
│ │ ├── style_krishnavalliappan.css
│ │ └── style_samodum_bold.css
│ ├── style_manager.py
│ ├── template_base.py
│ └── utils.py
├── logging.py
├── resume_schemas/
│ ├── job_application_profile.py
│ └── resume.py
└── utils/
├── __init__.py
├── chrome_utils.py
└── constants.py
SYMBOL INDEX (184 symbols across 19 files)
FILE: main.py
class ConfigError (line 30) | class ConfigError(Exception):
class ConfigValidator (line 35) | class ConfigValidator:
method validate_email (line 72) | def validate_email(email: str) -> bool:
method load_yaml (line 77) | def load_yaml(yaml_path: Path) -> dict:
method validate_config (line 88) | def validate_config(cls, config_yaml_path: Path) -> dict:
method _validate_experience_levels (line 114) | def _validate_experience_levels(cls, experience_levels: dict, config_p...
method _validate_job_types (line 123) | def _validate_job_types(cls, job_types: dict, config_path: Path):
method _validate_date_filters (line 132) | def _validate_date_filters(cls, date_filters: dict, config_path: Path):
method _validate_list_of_strings (line 141) | def _validate_list_of_strings(cls, parameters: dict, keys: list, confi...
method _validate_distance (line 150) | def _validate_distance(cls, distance: int, config_path: Path):
method _validate_blacklists (line 158) | def _validate_blacklists(cls, parameters: dict, config_path: Path):
method validate_secrets (line 169) | def validate_secrets(secrets_yaml_path: Path) -> str:
class FileManager (line 184) | class FileManager:
method validate_data_folder (line 190) | def validate_data_folder(app_data_folder: Path) -> Tuple[Path, Path, P...
method get_uploads (line 210) | def get_uploads(plain_text_resume_file: Path) -> Dict[str, Path]:
function create_cover_letter (line 220) | def create_cover_letter(parameters: dict, llm_api_key: str):
function create_resume_pdf_job_tailored (line 307) | def create_resume_pdf_job_tailored(parameters: dict, llm_api_key: str):
function create_resume_pdf (line 392) | def create_resume_pdf(parameters: dict, llm_api_key: str):
function handle_inquiries (line 471) | def handle_inquiries(selected_actions: List[str], parameters: dict, llm_...
function prompt_user_action (line 499) | def prompt_user_action() -> str:
function main (line 527) | def main():
FILE: src/job.py
class Job (line 5) | class Job:
method formatted_job_information (line 17) | def formatted_job_information(self):
FILE: src/jobContext.py
class JobContext (line 8) | class JobContext:
FILE: src/job_application_saver.py
class ApplicationSaver (line 16) | class ApplicationSaver:
method __init__ (line 18) | def __init__(self, job_application: JobApplication):
method create_application_directory (line 23) | def create_application_directory(self):
method save_application_details (line 36) | def save_application_details(self):
method save_file (line 50) | def save_file(self, dir_path, file_path, new_filename):
method save_job_description (line 59) | def save_job_description(self):
method save (line 74) | def save(job_application: JobApplication):
FILE: src/libs/llm_manager.py
class AIModel (line 80) | class AIModel(ABC):
method invoke (line 82) | def invoke(self, prompt: str) -> str:
class OpenAIModel (line 86) | class OpenAIModel(AIModel):
method __init__ (line 87) | def __init__(self, api_key: str, llm_model: str):
method invoke (line 94) | def invoke(self, prompt: str) -> BaseMessage:
class ClaudeModel (line 100) | class ClaudeModel(AIModel):
method __init__ (line 101) | def __init__(self, api_key: str, llm_model: str):
method invoke (line 106) | def invoke(self, prompt: str) -> BaseMessage:
class OllamaModel (line 112) | class OllamaModel(AIModel):
method __init__ (line 113) | def __init__(self, llm_model: str, llm_api_url: str):
method invoke (line 122) | def invoke(self, prompt: str) -> BaseMessage:
class PerplexityModel (line 126) | class PerplexityModel(AIModel):
method __init__ (line 127) | def __init__(self, api_key: str, llm_model: str):
method invoke (line 131) | def invoke(self, prompt: str) -> BaseMessage:
class GeminiModel (line 136) | class GeminiModel(AIModel):
method __init__ (line 137) | def __init__(self, api_key: str, llm_model: str):
method invoke (line 162) | def invoke(self, prompt: str) -> BaseMessage:
class HuggingFaceModel (line 167) | class HuggingFaceModel(AIModel):
method __init__ (line 168) | def __init__(self, api_key: str, llm_model: str):
method invoke (line 176) | def invoke(self, prompt: str) -> BaseMessage:
class AIAdapter (line 184) | class AIAdapter:
method __init__ (line 185) | def __init__(self, config: dict, api_key: str):
method _create_model (line 188) | def _create_model(self, config: dict, api_key: str) -> AIModel:
method invoke (line 211) | def invoke(self, prompt: str) -> str:
class LLMLogger (line 215) | class LLMLogger:
method __init__ (line 216) | def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, G...
method log_request (line 221) | def log_request(prompts, parsed_reply: Dict[str, Dict]):
class LoggerChatModel (line 327) | class LoggerChatModel:
method __init__ (line 328) | def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, G...
method __call__ (line 332) | def __call__(self, messages: List[Dict[str, str]]) -> str:
method parse_llmresult (line 387) | def parse_llmresult(self, llmresult: AIMessage) -> Dict[str, Dict]:
class GPTAnswerer (line 461) | class GPTAnswerer:
method __init__ (line 462) | def __init__(self, config, llm_api_key):
method job_description (line 467) | def job_description(self):
method find_best_match (line 471) | def find_best_match(text: str, options: list[str]) -> str:
method _remove_placeholders (line 481) | def _remove_placeholders(text: str) -> str:
method _preprocess_template_string (line 487) | def _preprocess_template_string(template: str) -> str:
method set_resume (line 491) | def set_resume(self, resume):
method set_job (line 495) | def set_job(self, job: Job):
method set_job_application_profile (line 502) | def set_job_application_profile(self, job_application_profile):
method _clean_llm_output (line 506) | def _clean_llm_output(self, output: str) -> str:
method summarize_job_description (line 509) | def summarize_job_description(self, text: str) -> str:
method _create_chain (line 521) | def _create_chain(self, template: str):
method answer_question_textual_wide_range (line 526) | def answer_question_textual_wide_range(self, question: str) -> str:
method answer_question_numeric (line 610) | def answer_question_numeric(
method extract_number_from_string (line 639) | def extract_number_from_string(self, output_str):
method answer_question_from_options (line 649) | def answer_question_from_options(self, question: str, options: list[st...
method determine_resume_or_cover (line 668) | def determine_resume_or_cover(self, phrase: str) -> str:
method is_job_suitable (line 686) | def is_job_suitable(self):
FILE: src/libs/resume_and_cover_builder/config.py
class GlobalConfig (line 7) | class GlobalConfig:
method __init__ (line 8) | def __init__(self):
FILE: src/libs/resume_and_cover_builder/llm/llm_generate_cover_letter_from_job.py
class LLMCoverLetterJobDescription (line 27) | class LLMCoverLetterJobDescription:
method __init__ (line 28) | def __init__(self, openai_api_key, strings):
method _preprocess_template_string (line 34) | def _preprocess_template_string(template: str) -> str:
method set_resume (line 44) | def set_resume(self, resume) -> None:
method set_job_description_from_text (line 52) | def set_job_description_from_text(self, job_description_text) -> None:
method generate_cover_letter (line 65) | def generate_cover_letter(self) -> str:
FILE: src/libs/resume_and_cover_builder/llm/llm_generate_resume.py
class LLMResumer (line 26) | class LLMResumer:
method __init__ (line 27) | def __init__(self, openai_api_key, strings):
method _preprocess_template_string (line 36) | def _preprocess_template_string(template: str) -> str:
method set_resume (line 46) | def set_resume(self, resume) -> None:
method generate_header (line 54) | def generate_header(self, data = None) -> str:
method generate_education_section (line 73) | def generate_education_section(self, data = None) -> str:
method generate_work_experience_section (line 101) | def generate_work_experience_section(self, data = None) -> str:
method generate_projects_section (line 129) | def generate_projects_section(self, data = None) -> str:
method generate_achievements_section (line 157) | def generate_achievements_section(self, data = None) -> str:
method generate_certifications_section (line 188) | def generate_certifications_section(self, data = None) -> str:
method generate_additional_skills_section (line 216) | def generate_additional_skills_section(self, data = None) -> str:
method generate_html_resume (line 246) | def generate_html_resume(self) -> str:
FILE: src/libs/resume_and_cover_builder/llm/llm_generate_resume_from_job.py
class LLMResumeJobDescription (line 24) | class LLMResumeJobDescription(LLMResumer):
method __init__ (line 25) | def __init__(self, openai_api_key, strings):
method set_job_description_from_text (line 28) | def set_job_description_from_text(self, job_description_text) -> None:
method generate_header (line 39) | def generate_header(self) -> str:
method generate_education_section (line 50) | def generate_education_section(self) -> str:
method generate_work_experience_section (line 61) | def generate_work_experience_section(self) -> str:
method generate_projects_section (line 72) | def generate_projects_section(self) -> str:
method generate_achievements_section (line 83) | def generate_achievements_section(self) -> str:
method generate_certifications_section (line 95) | def generate_certifications_section(self) -> str:
method generate_additional_skills_section (line 106) | def generate_additional_skills_section(self) -> str:
FILE: src/libs/resume_and_cover_builder/llm/llm_job_parser.py
class LLMParser (line 35) | class LLMParser:
method __init__ (line 36) | def __init__(self, openai_api_key):
method _preprocess_template_string (line 46) | def _preprocess_template_string(template: str) -> str:
method set_body_html (line 56) | def set_body_html(self, body_html):
method _retrieve_context (line 91) | def _retrieve_context(self, query: str, top_k: int = 3) -> str:
method _extract_information (line 109) | def _extract_information(self, question: str, retrieval_query: str) ->...
method extract_job_description (line 145) | def extract_job_description(self) -> str:
method extract_company_name (line 156) | def extract_company_name(self) -> str:
method extract_role (line 167) | def extract_role(self) -> str:
method extract_location (line 178) | def extract_location(self) -> str:
method extract_recruiter_email (line 189) | def extract_recruiter_email(self) -> str:
FILE: src/libs/resume_and_cover_builder/module_loader.py
function load_module (line 8) | def load_module(module_path: str, module_name: str):
FILE: src/libs/resume_and_cover_builder/resume_facade.py
class ResumeFacade (line 16) | class ResumeFacade:
method __init__ (line 17) | def __init__(self, api_key, style_manager, resume_generator, resume_ob...
method set_driver (line 40) | def set_driver(self, driver):
method prompt_user (line 43) | def prompt_user(self, choices: list[str], message: str) -> str:
method prompt_for_text (line 57) | def prompt_for_text(self, message: str) -> str:
method link_to_job (line 71) | def link_to_job(self, job_url):
method create_resume_pdf_job_tailored (line 88) | def create_resume_pdf_job_tailored(self) -> tuple[bytes, str]:
method create_resume_pdf (line 113) | def create_resume_pdf(self) -> tuple[bytes, str]:
method create_cover_letter (line 131) | def create_cover_letter(self) -> tuple[bytes, str]:
FILE: src/libs/resume_and_cover_builder/resume_generator.py
class ResumeGenerator (line 13) | class ResumeGenerator:
method __init__ (line 14) | def __init__(self):
method set_resume_object (line 17) | def set_resume_object(self, resume_object):
method _create_resume (line 21) | def _create_resume(self, gpt_answerer: Any, style_path):
method create_resume (line 42) | def create_resume(self, style_path):
method create_resume_job_description_text (line 47) | def create_resume_job_description_text(self, style_path: str, job_desc...
method create_cover_letter_job_description (line 53) | def create_cover_letter_job_description(self, style_path: str, job_des...
FILE: src/libs/resume_and_cover_builder/style_manager.py
class StyleManager (line 10) | class StyleManager:
method __init__ (line 11) | def __init__(self):
method get_styles (line 20) | def get_styles(self) -> Dict[str, Tuple[str, str]]:
method format_choices (line 55) | def format_choices(self, styles_to_files: Dict[str, Tuple[str, str]]) ...
method set_selected_style (line 65) | def set_selected_style(self, selected_style: str):
method get_style_path (line 74) | def get_style_path(self) -> Optional[Path]:
FILE: src/libs/resume_and_cover_builder/utils.py
class LLMLogger (line 19) | class LLMLogger:
method __init__ (line 21) | def __init__(self, llm: ChatOpenAI):
method log_request (line 25) | def log_request(prompts, parsed_reply: Dict[str, Dict]):
class LoggerChatModel (line 77) | class LoggerChatModel:
method __init__ (line 79) | def __init__(self, llm: ChatOpenAI):
method __call__ (line 82) | def __call__(self, messages: List[Dict[str, str]]) -> str:
method parse_llmresult (line 109) | def parse_llmresult(self, llmresult: AIMessage) -> Dict[str, Dict]:
FILE: src/logging.py
function remove_default_loggers (line 11) | def remove_default_loggers():
function init_loguru_logger (line 19) | def init_loguru_logger():
function init_selenium_logger (line 55) | def init_selenium_logger():
FILE: src/resume_schemas/job_application_profile.py
class SelfIdentification (line 9) | class SelfIdentification:
class LegalAuthorization (line 18) | class LegalAuthorization:
class WorkPreferences (line 39) | class WorkPreferences:
class Availability (line 49) | class Availability:
class SalaryExpectations (line 54) | class SalaryExpectations:
class JobApplicationProfile (line 59) | class JobApplicationProfile:
method __init__ (line 66) | def __init__(self, yaml_str: str):
method __str__ (line 174) | def __str__(self):
FILE: src/resume_schemas/resume.py
class PersonalInformation (line 8) | class PersonalInformation(BaseModel):
class EducationDetails (line 23) | class EducationDetails(BaseModel):
class ExperienceDetails (line 33) | class ExperienceDetails(BaseModel):
class Project (line 43) | class Project(BaseModel):
class Achievement (line 49) | class Achievement(BaseModel):
class Certifications (line 54) | class Certifications(BaseModel):
class Language (line 59) | class Language(BaseModel):
class Availability (line 64) | class Availability(BaseModel):
class SalaryExpectations (line 68) | class SalaryExpectations(BaseModel):
class SelfIdentification (line 72) | class SelfIdentification(BaseModel):
class LegalAuthorization (line 80) | class LegalAuthorization(BaseModel):
class Resume (line 91) | class Resume(BaseModel):
method normalize_exam_format (line 102) | def normalize_exam_format(exam):
method __init__ (line 107) | def __init__(self, yaml_str: str):
method _process_personal_information (line 125) | def _process_personal_information(self, data: Dict[str, Any]) -> Perso...
method _process_education_details (line 135) | def _process_education_details(self, data: List[Dict[str, Any]]) -> Li...
method _process_experience_details (line 160) | def _process_experience_details(self, data: List[Dict[str, Any]]) -> L...
class Exam (line 191) | class Exam:
class Responsibility (line 196) | class Responsibility:
FILE: src/utils/chrome_utils.py
function chrome_browser_options (line 10) | def chrome_browser_options():
function init_browser (line 38) | def init_browser() -> webdriver.Chrome:
function HTML_to_PDF (line 51) | def HTML_to_PDF(html_content, driver):
Condensed preview — 59 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (260K chars).
[
{
"path": ".github/CODE_OF_CONDUCT.md",
"chars": 3208,
"preview": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, w"
},
{
"path": ".github/CONTRIBUTING.md",
"chars": 4718,
"preview": "# Issues Reporting Guidelines\n\nWelcome to the AI Hawk Contributing Guide and Issues Tracker! To keep things organized an"
},
{
"path": ".github/FUNDING.yml",
"chars": 17,
"preview": "github: feder-cr\n"
},
{
"path": ".github/ISSUE_TEMPLATE/bug-issue.yml",
"chars": 2484,
"preview": "name: Bug report\ndescription: Report a bug or an issue that isn't working as expected.\ntitle: \"[BUG]: <Provide a clear, "
},
{
"path": ".github/ISSUE_TEMPLATE/config.yml",
"chars": 350,
"preview": "blank_issues_enabled: true\ncontact_links:\n - name: Questions\n url: t.me/AIhawkCommunity\n about: You can join the "
},
{
"path": ".github/ISSUE_TEMPLATE/documentation-issue.yml",
"chars": 1383,
"preview": "name: Documentation request\ndescription: Suggest improvements or additions to the project's documentation.\ntitle: \"[DOCS"
},
{
"path": ".github/ISSUE_TEMPLATE/enhancement-issue.yml",
"chars": 1482,
"preview": "name: Feature request\ndescription: Suggest a new feature or improvement for the project.\ntitle: \"[FEATURE]: <Provide a d"
},
{
"path": ".github/workflows/ci.yml",
"chars": 443,
"preview": "name: Python CI\n\non:\n push:\n pull_request:\n types: [opened, synchronize, reopened]\n\njobs:\n test:\n runs-on: ubun"
},
{
"path": ".github/workflows/stale.yml",
"chars": 2294,
"preview": "name: Mark and Close Stale Issues\n\non:\n # Schedule the workflow to run periodically (e.g., daily at 1:30 AM UTC)\n sche"
},
{
"path": ".gitignore",
"chars": 2391,
"preview": "# application files and logs\n/generated_cv\n/log/*\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.c"
},
{
"path": "CONTRIBUTING.md",
"chars": 6209,
"preview": "# Contributing to Auto_Jobs_Applier_AIHawk\n\n## Table of Contents\n\n- [Issue Labels](#issue-labels)\n- [Bug Reports](#bug-r"
},
{
"path": "LICENSE",
"chars": 34571,
"preview": " GNU AFFERO GENERAL PUBLIC LICENSE\n Version 3, 19 November 2007\n\n Copyright (C)"
},
{
"path": "README.md",
"chars": 1277,
"preview": "\n<div align=\"center\">\n\n\n# AIHawk: The first Jobs Applier AI Web Agent\n\n\nAIHawk's core architecture remains **open source"
},
{
"path": "assets/resume_schema.yaml",
"chars": 4092,
"preview": "# YAML Schema for plain_text_resume.yaml\n\npersonal_information:\n type: object\n properties:\n name: {type: string}\n "
},
{
"path": "config.py",
"chars": 523,
"preview": "# In this file, you can set the configurations of the app.\n\nfrom src.utils.constants import DEBUG, ERROR, LLM_MODEL, OPE"
},
{
"path": "data_folder/plain_text_resume.yaml",
"chars": 3647,
"preview": "personal_information:\n name: \"[Your Name]\"\n surname: \"[Your Surname]\"\n date_of_birth: \"[Your Date of Birth]\"\n countr"
},
{
"path": "data_folder/secrets.yaml",
"chars": 67,
"preview": "llm_api_key: 'sk-11KRr4uuTwpRGfeRTfj1T9BlbkFJjP8QTrswHU1yGruru2FR'\n"
},
{
"path": "data_folder/work_preferences.yaml",
"chars": 598,
"preview": "remote: true\nhybrid: true\nonsite: true\n\nexperience_level:\n internship: false\n entry: true\n associate: true\n mid_seni"
},
{
"path": "data_folder_example/plain_text_resume.yaml",
"chars": 4273,
"preview": "personal_information:\n name: \"solid\"\n surname: \"snake\"\n date_of_birth: \"12/01/1861\"\n country: \"Ireland\"\n city: \"Dub"
},
{
"path": "data_folder_example/resume_liam_murphy.txt",
"chars": 2504,
"preview": "Liam Murphy\nGalway, Ireland\nEmail: liam.murphy@gmail.com | AIHawk: liam-murphy\nGitHub: liam-murphy | Phone: +353 8712345"
},
{
"path": "data_folder_example/secrets.yaml",
"chars": 66,
"preview": "llm_api_key: 'sk-11KRr4uuTwpRGfeRTfj1T9BlbkFJjP8QTrswHU1yGruru2FR'"
},
{
"path": "data_folder_example/work_preferences.yaml",
"chars": 600,
"preview": "remote: true\nhybrid: true\nonsite: true\n\nexperience_level:\n internship: false\n entry: true\n associate: true\n mid_seni"
},
{
"path": "main.py",
"chars": 22141,
"preview": "import base64\nimport sys\nfrom pathlib import Path\nimport traceback\nfrom typing import List, Optional, Tuple, Dict\n\nimpor"
},
{
"path": "requirements.txt",
"chars": 654,
"preview": "click\ngit+https://github.com/feder-cr/lib_resume_builder_AIHawk.git\nhttpx~=0.27.2\ninputimeout==1.0.4\njsonschema==4.23.0\n"
},
{
"path": "src/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/job.py",
"chars": 1087,
"preview": "from dataclasses import dataclass\nfrom src.logging import logger\n\n@dataclass\nclass Job:\n role: str = \"\"\n company: "
},
{
"path": "src/jobContext.py",
"chars": 199,
"preview": "from src.job import Job\nfrom src.job_application import JobApplication\n\n\nfrom dataclasses import dataclass\n\n@dataclass\nc"
},
{
"path": "src/job_application_saver.py",
"chars": 3344,
"preview": "from src.logging import logger\nimport os\nimport json\nimport shutil\n\nfrom dataclasses import asdict\n\nfrom config import J"
},
{
"path": "src/libs/llm_manager.py",
"chars": 27253,
"preview": "import json\nimport os\nimport re\nimport textwrap\nimport time\nfrom abc import ABC, abstractmethod\nfrom datetime import dat"
},
{
"path": "src/libs/resume_and_cover_builder/__init__.py",
"chars": 232,
"preview": "__version__ = '0.1'\n\n# Import all the necessary classes and functions, called when the package is imported\nfrom .resume_"
},
{
"path": "src/libs/resume_and_cover_builder/config.py",
"chars": 1733,
"preview": "\"\"\"\nThis module is used to store the global configuration of the application.\n\"\"\"\n# app/libs/resume_and_cover_builder/co"
},
{
"path": "src/libs/resume_and_cover_builder/cover_letter_prompt/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/libs/resume_and_cover_builder/cover_letter_prompt/strings_feder-cr.py",
"chars": 3416,
"preview": "from src.libs.resume_and_cover_builder.template_base import prompt_cover_letter_template\n\n\ncover_letter_template = \"\"\"\nC"
},
{
"path": "src/libs/resume_and_cover_builder/llm/llm_generate_cover_letter_from_job.py",
"chars": 3630,
"preview": "\"\"\"\nThis creates the cover letter (in html, utils will then convert in PDF) matching with job description and plain-text"
},
{
"path": "src/libs/resume_and_cover_builder/llm/llm_generate_resume.py",
"chars": 12457,
"preview": "\"\"\"\nCreate a class that generates a resume based on a resume and a resume template.\n\"\"\"\n# app/libs/resume_and_cover_buil"
},
{
"path": "src/libs/resume_and_cover_builder/llm/llm_generate_resume_from_job.py",
"chars": 5027,
"preview": "\"\"\"\nCreate a class that generates a job description based on a resume and a job description template.\n\"\"\"\n# app/libs/res"
},
{
"path": "src/libs/resume_and_cover_builder/llm/llm_job_parser.py",
"chars": 8464,
"preview": "import os\nimport tempfile\nimport textwrap\nimport time\nimport re # For email validation\nfrom src.libs.resume_and_cover_b"
},
{
"path": "src/libs/resume_and_cover_builder/module_loader.py",
"chars": 435,
"preview": "\"\"\"\nThis module is used to store the global configuration of the application.\n\"\"\"\n# app/libs/resume_and_cover_builder/mo"
},
{
"path": "src/libs/resume_and_cover_builder/resume_facade.py",
"chars": 6730,
"preview": "\"\"\"\nThis module contains the FacadeManager class, which is responsible for managing the interaction between the user and"
},
{
"path": "src/libs/resume_and_cover_builder/resume_generator.py",
"chars": 3065,
"preview": "\"\"\"\nThis module is responsible for generating resumes and cover letters using the LLM model.\n\"\"\"\n# app/libs/resume_and_c"
},
{
"path": "src/libs/resume_and_cover_builder/resume_job_description_prompt/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/libs/resume_and_cover_builder/resume_job_description_prompt/strings_feder-cr.py",
"chars": 8884,
"preview": "from src.libs.resume_and_cover_builder.template_base import prompt_header_template, prompt_education_template, prompt_wo"
},
{
"path": "src/libs/resume_and_cover_builder/resume_prompt/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/libs/resume_and_cover_builder/resume_prompt/strings_feder-cr.py",
"chars": 4704,
"preview": "from src.libs.resume_and_cover_builder.template_base import prompt_header_template, prompt_education_template, prompt_wo"
},
{
"path": "src/libs/resume_and_cover_builder/resume_style/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/libs/resume_and_cover_builder/resume_style/style_cloyola.css",
"chars": 2276,
"preview": "/*Cloyola Grey $https://github.com/cloyola*/\n@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;"
},
{
"path": "src/libs/resume_and_cover_builder/resume_style/style_josylad_blue.css",
"chars": 2338,
"preview": "/*Modern Blue$https://github.com/josylad*/\n\n@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;6"
},
{
"path": "src/libs/resume_and_cover_builder/resume_style/style_josylad_grey.css",
"chars": 2939,
"preview": "/*Modern Grey$https://github.com/josylad*/\n@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;60"
},
{
"path": "src/libs/resume_and_cover_builder/resume_style/style_krishnavalliappan.css",
"chars": 1693,
"preview": "/*Default$https://github.com/krishnavalliappan*/\nbody {\n font-family: \"Barlow\", Arial, sans-serif;\n line-height: 1.2;\n"
},
{
"path": "src/libs/resume_and_cover_builder/resume_style/style_samodum_bold.css",
"chars": 3166,
"preview": "/*Clean Blue$https://github.com/samodum*/\n@import url(\"https://fonts.googleapis.com/css2?family=Josefin+Sans&family=Kais"
},
{
"path": "src/libs/resume_and_cover_builder/style_manager.py",
"chars": 4064,
"preview": "import os\nfrom pathlib import Path\nfrom typing import Dict, List, Tuple, Optional\nimport logging\n\n# Configure logging\nlo"
},
{
"path": "src/libs/resume_and_cover_builder/template_base.py",
"chars": 8845,
"preview": "\"\"\"\nThis module is used to store the global configuration of the application.\n\"\"\"\n# app/libs/resume_and_cover_builder/te"
},
{
"path": "src/libs/resume_and_cover_builder/utils.py",
"chars": 5324,
"preview": "\"\"\"\nThis module contains utility functions for the Resume and Cover Letter Builder service.\n\"\"\"\n\n# app/libs/resume_and_c"
},
{
"path": "src/logging.py",
"chars": 2496,
"preview": "import logging.handlers\nimport os\nimport sys\nimport logging\nfrom loguru import logger\nfrom selenium.webdriver.remote.rem"
},
{
"path": "src/resume_schemas/job_application_profile.py",
"chars": 8625,
"preview": "from dataclasses import dataclass\n\nimport yaml\n\nfrom src.logging import logger\n\n\n@dataclass\nclass SelfIdentification:\n "
},
{
"path": "src/resume_schemas/resume.py",
"chars": 6920,
"preview": "from dataclasses import dataclass, field\nfrom typing import List, Dict, Any, Optional, Union\nimport yaml\nfrom pydantic i"
},
{
"path": "src/utils/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/utils/chrome_utils.py",
"chars": 4625,
"preview": "import os\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service as ChromeServ"
},
{
"path": "src/utils/constants.py",
"chars": 1919,
"preview": "DATE_ALL_TIME = \"all_time\"\nDATE_MONTH = \"month\"\nDATE_WEEK = \"week\"\nDATE_24_HOURS = \"24_hours\"\n\n\n# constants used in appl"
}
]
About this extraction
This page contains the full source code of the feder-cr/Jobs_Applier_AI_Agent_AIHawk GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 59 files (240.1 KB), approximately 56.0k tokens, and a symbol index with 184 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.