Repository: bhattbhavesh91/gpt-3-simple-tutorial
Branch: master
Commit: 1c1e3cbc30c2
Files: 9
Total size: 101.8 KB
Directory structure:
gitextract_qjsi6js3/
├── .github/
│ └── FUNDING.yml
├── .gitignore
├── LICENSE
├── README.md
├── gpt-3-notebook.ipynb
├── gpt-pandas-code-generation.ipynb
├── gpt-pandas-matplotlib.ipynb
├── gpt.py
└── iris.csv
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FILE CONTENTS
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FILE: .github/FUNDING.yml
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# These are supported funding model platforms
custom: ['https://www.buymeacoffee.com/bhattbhavesh91']
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FILE: .gitignore
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MANIFEST
build
dist
_build
docs/man/*.gz
docs/source/api/generated
docs/source/config.rst
docs/gh-pages
notebook/i18n/*/LC_MESSAGES/*.mo
notebook/i18n/*/LC_MESSAGES/nbjs.json
notebook/static/components
notebook/static/style/*.min.css*
notebook/static/*/js/built/
notebook/static/*/built/
notebook/static/built/
notebook/static/*/js/main.min.js*
notebook/static/lab/*bundle.js
node_modules
*.py[co]
__pycache__
*.egg-info
*~
*.bak
.ipynb_checkpoints
.tox
.DS_Store
\#*#
.#*
.coverage
.pytest_cache
src
*.swp
*.map
.idea/
Read the Docs
config.rst
/.project
/.pydevproject
package-lock.json
geckodriver.log
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FILE: LICENSE
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FILE: README.md
================================================
# OpenAI's GPT-3 to generate SQL from Natural Language!
**If you like my work, you can support me by buying me a coffee by clicking the link below**
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## To view the video
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or click on the image below
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## Follow Me
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<h3 align="center">Show your support by starring the repository 🙂</h3>
================================================
FILE: gpt-3-notebook.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"GPT-3-Demo.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyNfqR7vWsi34k6mv0Yi02Yy"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"heIf5_ducY8T","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":297},"executionInfo":{"status":"ok","timestamp":1595471247881,"user_tz":-330,"elapsed":8803,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}},"outputId":"c668882d-1977-4c74-bdd5-584d27bd82f3"},"source":["!pip install openai"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Collecting openai\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/a8/65/c7461f4c87984534683f480ea5742777bc39bbf5721123194c2d0347dc1f/openai-0.2.4.tar.gz (157kB)\n","\r\u001b[K |██ | 10kB 13.1MB/s eta 0:00:01\r\u001b[K |████▏ | 20kB 1.6MB/s eta 0:00:01\r\u001b[K |██████▎ | 30kB 1.8MB/s eta 0:00:01\r\u001b[K |████████▍ | 40kB 2.1MB/s eta 0:00:01\r\u001b[K |██████████▍ | 51kB 2.0MB/s eta 0:00:01\r\u001b[K |████████████▌ | 61kB 2.1MB/s eta 0:00:01\r\u001b[K |██████████████▋ | 71kB 2.3MB/s eta 0:00:01\r\u001b[K |████████████████▊ | 81kB 2.4MB/s eta 0:00:01\r\u001b[K |██████████████████▊ | 92kB 2.6MB/s eta 0:00:01\r\u001b[K |████████████████████▉ | 102kB 2.8MB/s eta 0:00:01\r\u001b[K |███████████████████████ | 112kB 2.8MB/s eta 0:00:01\r\u001b[K |█████████████████████████ | 122kB 2.8MB/s eta 0:00:01\r\u001b[K |███████████████████████████ | 133kB 2.8MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▏ | 143kB 2.8MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▎| 153kB 2.8MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 163kB 2.8MB/s \n","\u001b[?25hRequirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from openai) (2.23.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2020.6.20)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2.10)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (1.24.3)\n","Building wheels for collected packages: openai\n"," Building wheel for openai (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for openai: filename=openai-0.2.4-cp36-none-any.whl size=170709 sha256=7ae56fa654e2071a250acdeed89d5fb874faa15c8755bb252607201d0e434798\n"," Stored in directory: /root/.cache/pip/wheels/74/96/c8/c6e170929c276b836613e1b9985343b501fe455e53d85e7d48\n","Successfully built openai\n","Installing collected packages: openai\n","Successfully installed openai-0.2.4\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"JIObOT-ybumY","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471299970,"user_tz":-330,"elapsed":951,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["import json\n","import openai"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"m42z9jQxqA2b","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471313449,"user_tz":-330,"elapsed":1104,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["with open('GPT_SECRET_KEY.json') as f:\n"," data = json.load(f)"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"75Yg2gB7p3Q0","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471324175,"user_tz":-330,"elapsed":703,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["openai.api_key = data[\"API_KEY\"]"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"N3sAHxJrhBzK","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471367430,"user_tz":-330,"elapsed":1044,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["from gpt import GPT\n","from gpt import Example"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"XiV0D9PihB2N","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471538287,"user_tz":-330,"elapsed":1248,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["gpt = GPT(engine=\"davinci\",\n"," temperature=0.5,\n"," max_tokens=100)"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"WwVcHYMOvGiU","colab_type":"text"},"source":["# Adding Examples for GPT Model"]},{"cell_type":"code","metadata":{"id":"0iLR1Y6YqTh7","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471715638,"user_tz":-330,"elapsed":1343,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["gpt.add_example(Example('Fetch unique values of DEPARTMENT from Worker table.', \n"," 'Select distinct DEPARTMENT from Worker;'))"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"x28YlU1-qrCW","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471721203,"user_tz":-330,"elapsed":1433,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["gpt.add_example(Example('Print the first three characters of FIRST_NAME from Worker table.', \n"," 'Select substring(FIRST_NAME,1,3) from Worker;'))"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"1C10LyYPqrFX","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471721205,"user_tz":-330,"elapsed":883,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["gpt.add_example(Example(\"Find the position of the alphabet ('a') in the first name column 'Amitabh' from Worker table.\", \n"," \"Select INSTR(FIRST_NAME, BINARY'a') from Worker where FIRST_NAME = 'Amitabh';\"))"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"0JvjODWbsBWP","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471723625,"user_tz":-330,"elapsed":964,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["gpt.add_example(Example(\"Print the FIRST_NAME from Worker table after replacing 'a' with 'A'.\", \n"," \"Select CONCAT(FIRST_NAME, ' ', LAST_NAME) AS 'COMPLETE_NAME' from Worker;\"))"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"uNsH4OeqsKjM","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471760833,"user_tz":-330,"elapsed":958,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["gpt.add_example(Example(\"Display the second highest salary from the Worker table.\", \n"," \"Select max(Salary) from Worker where Salary not in (Select max(Salary) from Worker);\"))"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"zhXh5g-jsKnl","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471795232,"user_tz":-330,"elapsed":1075,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["gpt.add_example(Example(\"Display the highest salary from the Worker table.\", \n"," \"Select max(Salary) from Worker;\"))"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"wWjmZe-Ntexm","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471796602,"user_tz":-330,"elapsed":882,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["gpt.add_example(Example(\"Fetch the count of employees working in the department Admin.\", \n"," \"SELECT COUNT(*) FROM worker WHERE DEPARTMENT = 'Admin';\"))"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"id":"Q9xuF--Kt_xh","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471799951,"user_tz":-330,"elapsed":1149,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["gpt.add_example(Example(\"Get all details of the Workers whose SALARY lies between 100000 and 500000.\", \n"," \"Select * from Worker where SALARY between 100000 and 500000;\"))"],"execution_count":14,"outputs":[]},{"cell_type":"code","metadata":{"id":"z5jRwDCcuauE","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471800475,"user_tz":-330,"elapsed":786,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["gpt.add_example(Example(\"Get Salary details of the Workers\", \n"," \"Select Salary from Worker\"))"],"execution_count":15,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3mI7FmwSu9AA","colab_type":"text"},"source":["# Example 1"]},{"cell_type":"code","metadata":{"id":"sWSmXABfrdTm","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471857682,"user_tz":-330,"elapsed":1211,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["prompt = \"Display the lowest salary from the Worker table.\""],"execution_count":16,"outputs":[]},{"cell_type":"code","metadata":{"id":"pVzvJtmRqTku","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471890838,"user_tz":-330,"elapsed":2494,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["output = gpt.submit_request(prompt)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"id":"niqyIPAyoLQb","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1595471926542,"user_tz":-330,"elapsed":1366,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}},"outputId":"853cd2a0-ec4e-4454-90b8-cb8be70b0d4f"},"source":["output.choices[0].text"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'output: Select min(Salary) from Worker;\\n'"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"markdown","metadata":{"id":"LA3DyhGJu_8o","colab_type":"text"},"source":["# Example 2"]},{"cell_type":"code","metadata":{"id":"_OdI6bFLtpel","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595471994566,"user_tz":-330,"elapsed":1325,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["prompt = \"Tell me the count of employees working in the department HR.\""],"execution_count":20,"outputs":[]},{"cell_type":"code","metadata":{"id":"XnjQ0kfbtpkE","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595472001159,"user_tz":-330,"elapsed":2828,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["output = gpt.submit_request(prompt)"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"Z9Yo-bZotph4","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1595472005315,"user_tz":-330,"elapsed":1008,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}},"outputId":"0e1572e6-bceb-42c8-d67f-4f29a156745c"},"source":["output.choices[0].text"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["\"output: SELECT COUNT(*) FROM worker WHERE DEPARTMENT = 'HR';\\n\""]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"dnB7TCA_vCkF","colab_type":"text"},"source":["# Example 3"]},{"cell_type":"code","metadata":{"id":"GkaHZLIzt3pQ","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595472072925,"user_tz":-330,"elapsed":946,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["prompt = \"Get salary details of the Workers whose AGE lies between 25 and 35\""],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"id":"G6GmvbukmLCK","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":51},"executionInfo":{"status":"ok","timestamp":1595472104996,"user_tz":-330,"elapsed":3152,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}},"outputId":"21c8f471-b272-47ed-dd18-36c94fbf6936"},"source":["print(gpt.get_top_reply(prompt))"],"execution_count":24,"outputs":[{"output_type":"stream","text":["output: Select Salary from Worker where AGE between 25 and 35;\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"0ZaLCw6zgqxh","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]}]}
================================================
FILE: gpt-pandas-code-generation.ipynb
================================================
{
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"colab": {
"name": "GPT-3-Pandas-Code.ipynb",
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"name": "python3",
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"cells": [
{
"cell_type": "code",
"metadata": {
"id": "heIf5_ducY8T",
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"base_uri": "https://localhost:8080/",
"height": 297
},
"outputId": "fe2165bc-0b43-4a1b-a9b2-a755ae1d2cd8"
},
"source": [
"!pip install openai"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting openai\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/a8/65/c7461f4c87984534683f480ea5742777bc39bbf5721123194c2d0347dc1f/openai-0.2.4.tar.gz (157kB)\n",
"\u001b[K |████████████████████████████████| 163kB 2.7MB/s \n",
"\u001b[?25hRequirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from openai) (2.23.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2020.6.20)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (3.0.4)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2.10)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (1.24.3)\n",
"Building wheels for collected packages: openai\n",
" Building wheel for openai (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Stored in directory: /root/.cache/pip/wheels/74/96/c8/c6e170929c276b836613e1b9985343b501fe455e53d85e7d48\n",
"Successfully built openai\n",
"Installing collected packages: openai\n",
"Successfully installed openai-0.2.4\n"
],
"name": "stdout"
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]
},
{
"cell_type": "code",
"metadata": {
"id": "JIObOT-ybumY",
"colab_type": "code",
"colab": {}
},
"source": [
"import json\n",
"import openai\n",
"import numpy as np\n",
"import pandas as pd"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "m42z9jQxqA2b",
"colab_type": "code",
"colab": {}
},
"source": [
"with open('GPT_SECRET_KEY.json') as f:\n",
" data = json.load(f)"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "75Yg2gB7p3Q0",
"colab_type": "code",
"colab": {}
},
"source": [
"openai.api_key = data[\"API_KEY\"]"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "N3sAHxJrhBzK",
"colab_type": "code",
"colab": {}
},
"source": [
"from gpt import GPT\n",
"from gpt import Example"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "XiV0D9PihB2N",
"colab_type": "code",
"colab": {}
},
"source": [
"gpt = GPT(engine=\"davinci\",\n",
" temperature=0.5,\n",
" max_tokens=100)"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "6qLap8Xgvwjt",
"colab_type": "code",
"colab": {}
},
"source": [
"df = pd.DataFrame({\"Gender\": [\"boy\", \"boy\", \"boy\", \"boy\", \"boy\",\n",
" \"girl\", \"girl\", \"girl\", \"girl\"],\n",
" \"Division\": [\"one\", \"one\", \"one\", \"two\", \"two\",\n",
" \"one\", \"one\", \"two\", \"two\"],\n",
" \"Marks\": [50, 55, 67, 85, 44, 84, 65, 56, 87]})"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JGQIWZO-AazG",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 324
},
"outputId": "744be30f-e298-44b9-edf2-02379817d368"
},
"source": [
"df"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Gender</th>\n",
" <th>Division</th>\n",
" <th>Marks</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>boy</td>\n",
" <td>one</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>boy</td>\n",
" <td>one</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>boy</td>\n",
" <td>one</td>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>boy</td>\n",
" <td>two</td>\n",
" <td>85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>boy</td>\n",
" <td>two</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>girl</td>\n",
" <td>one</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>girl</td>\n",
" <td>one</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>girl</td>\n",
" <td>two</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>girl</td>\n",
" <td>two</td>\n",
" <td>87</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Gender Division Marks\n",
"0 boy one 50\n",
"1 boy one 55\n",
"2 boy one 67\n",
"3 boy two 85\n",
"4 boy two 44\n",
"5 girl one 84\n",
"6 girl one 65\n",
"7 girl two 56\n",
"8 girl two 87"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WwVcHYMOvGiU",
"colab_type": "text"
},
"source": [
"# Adding Examples for GPT Model"
]
},
{
"cell_type": "code",
"metadata": {
"id": "0iLR1Y6YqTh7",
"colab_type": "code",
"colab": {}
},
"source": [
"gpt.add_example(Example('How many unique values in Division Column?', \n",
" 'df[\"Division\"].nunique()'))"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "x28YlU1-qrCW",
"colab_type": "code",
"colab": {}
},
"source": [
"gpt.add_example(Example('Find the Division of boy who scored 55 marks', \n",
" 'df.loc[(df.loc[:, \"Gender\"] == \"boy\") & (df.loc[:, \"Marks\"] == 55), \"Division\"]'))"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yBQMrnIQx3Uv",
"colab_type": "code",
"colab": {}
},
"source": [
"gpt.add_example(Example('Find the average Marks scored by Girls', \n",
" 'np.mean(df.loc[(df.loc[:, \"Gender\"] == \"girl\"), \"Marks\"])'))"
],
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "sua6IdZgw4tR",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "3mI7FmwSu9AA",
"colab_type": "text"
},
"source": [
"# Example 1"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sWSmXABfrdTm",
"colab_type": "code",
"colab": {}
},
"source": [
"prompt = \"Display Division of girl who scored maximum marks\""
],
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "pVzvJtmRqTku",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"outputId": "f1ee2728-2b22-4835-e437-aa7221cf1201"
},
"source": [
"print(gpt.get_top_reply(prompt))"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
"output: df.loc[(df.loc[:, \"Gender\"] == \"girl\") & (df.loc[:, \"Marks\"] == max(df.loc[:, \"Marks\"])), \"Division\"]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "BA_Kzqnjdwwg",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "c0bc77c0-e964-45c3-dbe6-7e6b353c8254"
},
"source": [
"df.loc[(df.loc[:, \"Gender\"] == \"girl\") & \n",
" (df.loc[:, \"Marks\"] == max(df.loc[:, \"Marks\"])), \n",
" \"Division\"]"
],
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"8 two\n",
"Name: Division, dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NopaHtl1_9rH",
"colab_type": "text"
},
"source": [
"# Example 2"
]
},
{
"cell_type": "code",
"metadata": {
"id": "eO4IW_H_y_5F",
"colab_type": "code",
"colab": {}
},
"source": [
"prompt = \"Find the median Marks scored by Boys\""
],
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Q8ldVXxhyGVD",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "611ff255-3feb-4f98-d1ff-197a8c0466eb"
},
"source": [
"print(gpt.get_top_reply(prompt))"
],
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"text": [
"output: np.median(df.loc[(df.loc[:, \"Gender\"] == \"boy\"), \"Marks\"])\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "wUi-UN62zJe_",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "4f5a164e-03ac-40ed-a135-f5aee4f45c59"
},
"source": [
"np.median(df.loc[(df.loc[:, \"Gender\"] == \"boy\"), \"Marks\"])"
],
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"55.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Cf6vxbTADRpf",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
================================================
FILE: gpt-pandas-matplotlib.ipynb
================================================
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "gpt-pandas-code-generation.ipynb",
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"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "heIf5_ducY8T",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"outputId": "74217a60-6e07-425c-a94a-51387939b7ee"
},
"source": [
"!pip install openai"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting openai\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/a8/65/c7461f4c87984534683f480ea5742777bc39bbf5721123194c2d0347dc1f/openai-0.2.4.tar.gz (157kB)\n",
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"\u001b[?25hRequirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from openai) (2.23.0)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (1.24.3)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (3.0.4)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2020.6.20)\n",
"Building wheels for collected packages: openai\n",
" Building wheel for openai (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for openai: filename=openai-0.2.4-cp36-none-any.whl size=170709 sha256=7d8dd1fd8f27ae398961666519a46af67ea9c925b960cd786d619c7f6da3bc54\n",
" Stored in directory: /root/.cache/pip/wheels/74/96/c8/c6e170929c276b836613e1b9985343b501fe455e53d85e7d48\n",
"Successfully built openai\n",
"Installing collected packages: openai\n",
"Successfully installed openai-0.2.4\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "JIObOT-ybumY",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"outputId": "d44d7576-460f-4af2-b668-9f29cec15b74"
},
"source": [
"import json\n",
"import openai\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt \n",
"import seaborn as sns"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
" import pandas.util.testing as tm\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "m42z9jQxqA2b",
"colab_type": "code",
"colab": {}
},
"source": [
"with open('GPT_SECRET_KEY.json') as f:\n",
" data = json.load(f)"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "75Yg2gB7p3Q0",
"colab_type": "code",
"colab": {}
},
"source": [
"openai.api_key = data[\"API_KEY\"]"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "N3sAHxJrhBzK",
"colab_type": "code",
"colab": {}
},
"source": [
"from gpt import GPT\n",
"from gpt import Example"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "XiV0D9PihB2N",
"colab_type": "code",
"colab": {}
},
"source": [
"gpt = GPT(engine=\"davinci\",\n",
" temperature=0.5,\n",
" max_tokens=100)"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "6qLap8Xgvwjt",
"colab_type": "code",
"colab": {}
},
"source": [
"df = pd.DataFrame({\"Gender\": [\"boy\", \"boy\", \"boy\", \"boy\", \"boy\",\n",
" \"girl\", \"girl\", \"girl\", \"girl\"],\n",
" \"Division\": [\"one\", \"one\", \"one\", \"two\", \"two\",\n",
" \"one\", \"one\", \"two\", \"two\"],\n",
" \"Marks\": [50, 55, 67, 85, 44, 84, 65, 56, 87]})"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JGQIWZO-AazG",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 324
},
"outputId": "bdd987fc-929e-450e-d687-6c3b12fa52cf"
},
"source": [
"df"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Gender</th>\n",
" <th>Division</th>\n",
" <th>Marks</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>boy</td>\n",
" <td>one</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>boy</td>\n",
" <td>one</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>boy</td>\n",
" <td>one</td>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>boy</td>\n",
" <td>two</td>\n",
" <td>85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>boy</td>\n",
" <td>two</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>girl</td>\n",
" <td>one</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>girl</td>\n",
" <td>one</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>girl</td>\n",
" <td>two</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>girl</td>\n",
" <td>two</td>\n",
" <td>87</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Gender Division Marks\n",
"0 boy one 50\n",
"1 boy one 55\n",
"2 boy one 67\n",
"3 boy two 85\n",
"4 boy two 44\n",
"5 girl one 84\n",
"6 girl one 65\n",
"7 girl two 56\n",
"8 girl two 87"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WwVcHYMOvGiU",
"colab_type": "text"
},
"source": [
"# Adding Examples for GPT Model"
]
},
{
"cell_type": "code",
"metadata": {
"id": "0iLR1Y6YqTh7",
"colab_type": "code",
"colab": {}
},
"source": [
"gpt.add_example(Example('How many unique values in Division Column?', \n",
" 'df[\"Division\"].nunique()'))"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "x28YlU1-qrCW",
"colab_type": "code",
"colab": {}
},
"source": [
"gpt.add_example(Example('Find the Division of boy who scored 55 marks', \n",
" 'df.loc[(df.loc[:, \"Gender\"] == \"boy\") & (df.loc[:, \"Marks\"] == 55), \"Division\"]'))"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yBQMrnIQx3Uv",
"colab_type": "code",
"colab": {}
},
"source": [
"gpt.add_example(Example('Find the average Marks scored by Girls', \n",
" 'np.mean(df.loc[(df.loc[:, \"Gender\"] == \"girl\"), \"Marks\"])'))"
],
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "sua6IdZgw4tR",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "3mI7FmwSu9AA",
"colab_type": "text"
},
"source": [
"# Example 1"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sWSmXABfrdTm",
"colab_type": "code",
"colab": {}
},
"source": [
"prompt = \"Display Division of girl who scored maximum marks\""
],
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7RE6DxAyEEy5",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
},
"outputId": "8258aafd-bfd6-49fd-a4e0-cd80695bf637"
},
"source": [
"gpt.get_top_reply(prompt)"
],
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'output: df.loc[(df.loc[:, \"Gender\"] == \"girl\") & (df.loc[:, \"Marks\"] == max(df.loc[:, \"Marks\"])), \"Division\"]\\n'"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "FkyPKIYCEin4",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 106
},
"outputId": "90dcea4c-2f66-451e-baab-fee2b2ba317a"
},
"source": [
"response = gpt.get_top_reply(prompt)\n",
"print(response)\n",
"modified_response = response.split(\"output: \")[-1].strip('\\n')\n",
"eval(modified_response)"
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
"output: df.loc[(df.loc[:, \"Gender\"] == \"girl\") & (df.loc[:, \"Marks\"] == max(df.loc[:, \"Marks\"])), \"Division\"]\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"8 two\n",
"Name: Division, dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NopaHtl1_9rH",
"colab_type": "text"
},
"source": [
"# Example 2"
]
},
{
"cell_type": "code",
"metadata": {
"id": "eO4IW_H_y_5F",
"colab_type": "code",
"colab": {}
},
"source": [
"prompt = \"Find the median Marks scored by Boys\""
],
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Q8ldVXxhyGVD",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 69
},
"outputId": "d9d5cebf-1a3c-4d3d-e721-04386882d4e2"
},
"source": [
"response = gpt.get_top_reply(prompt)\n",
"print(response)\n",
"modified_response = response.split(\"output: \")[-1].strip('\\n')\n",
"eval(modified_response)"
],
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"text": [
"output: np.median(df.loc[(df.loc[:, \"Gender\"] == \"boy\"), \"Marks\"])\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"55.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OIftyCylGZOm",
"colab_type": "text"
},
"source": [
"# Matplotlib"
]
},
{
"cell_type": "code",
"metadata": {
"id": "wUi-UN62zJe_",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "N_LN5yFvGu9h",
"colab_type": "code",
"colab": {}
},
"source": [
"df = pd.read_csv(\"iris.csv\")"
],
"execution_count": 17,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JaPeGE21G0OW",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 202
},
"outputId": "214ede56-9479-429e-da13-2275797187ed"
},
"source": [
"df.head()"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width species\n",
"0 5.1 3.5 1.4 0.2 setosa\n",
"1 4.9 3.0 1.4 0.2 setosa\n",
"2 4.7 3.2 1.3 0.2 setosa\n",
"3 4.6 3.1 1.5 0.2 setosa\n",
"4 5.0 3.6 1.4 0.2 setosa"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Cf6vxbTADRpf",
"colab_type": "code",
"colab": {}
},
"source": [
"gpt.add_example(Example('Plot Scatter Plot between Sepal Length & Sepal Width', \n",
" \"plt.scatter(df['sepal_length'], df['sepal_width'])\"))"
],
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "kw6K2MGOIqQ0",
"colab_type": "code",
"colab": {}
},
"source": [
"gpt.add_example(Example('Plot Bar Plot of Species', \n",
" \"sns.countplot('species',data=df)\"))"
],
"execution_count": 20,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "BdMy31wCI7IT",
"colab_type": "code",
"colab": {}
},
"source": [
"gpt.add_example(Example('Plot a Joint Plot between Sepal Length & Petal Length', \n",
" \"sns.jointplot(x='sepal_length',y='petal_length',data=df)\"))"
],
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "SvQN_tTgKuHO",
"colab_type": "code",
"colab": {}
},
"source": [
"gpt.add_example(Example('Show me the histogram of Petal Length', \n",
" \"plt.hist(df['petal_length'])\"))"
],
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "aAVofRjMId_b",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "4ljE2bDJJoY6",
"colab_type": "text"
},
"source": [
"# Example 3"
]
},
{
"cell_type": "code",
"metadata": {
"id": "FDd-Cf22Jm2G",
"colab_type": "code",
"colab": {}
},
"source": [
"prompt = \"Show me the scatter plot between petal length and sepal width\""
],
"execution_count": 23,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_3eVyHEEJ1eH",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 320
},
"outputId": "de64cded-7157-428f-f8bc-897a1fafa044"
},
"source": [
"response = gpt.get_top_reply(prompt)\n",
"print(response)\n",
"modified_response = response.split(\"output: \")[-1].strip('\\n')\n",
"eval(modified_response)"
],
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"text": [
"output: plt.scatter(df['petal_length'], df['sepal_width'])\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f4efbe0e128>"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6Fx9MuHOKZrn",
"colab_type": "text"
},
"source": [
"# Example 4"
]
},
{
"cell_type": "code",
"metadata": {
"id": "1JQQEa_8J3j_",
"colab_type": "code",
"colab": {}
},
"source": [
"prompt = \"Show me the Joint Plot between Petal Length & Petal Length\""
],
"execution_count": 25,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "GNHuzfNsKhzj",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 493
},
"outputId": "c6d67a08-91a8-4b5f-e916-17514ca70a3b"
},
"source": [
"response = gpt.get_top_reply(prompt)\n",
"print(response)\n",
"modified_response = response.split(\"output: \")[-1].strip('\\n')\n",
"eval(modified_response)"
],
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"text": [
"output: sns.jointplot(x='petal_length',y='petal_length',data=df)\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<seaborn.axisgrid.JointGrid at 0x7f4efbe583c8>"
]
},
"metadata": {
"tags": []
},
"execution_count": 26
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 3 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "au8eO5gwLOPg",
"colab_type": "text"
},
"source": [
"# Example 5"
]
},
{
"cell_type": "code",
"metadata": {
"id": "eHEQ7C9iKjtD",
"colab_type": "code",
"colab": {}
},
"source": [
"prompt = \"Show me the Distribution of Sepal Length\""
],
"execution_count": 27,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "U93zERQDLYDA",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 491
},
"outputId": "ee9e923a-369c-4487-97cc-c7e289f3bac3"
},
"source": [
"response = gpt.get_top_reply(prompt)\n",
"print(response)\n",
"modified_response = response.split(\"output: \")[-1].strip('\\n')\n",
"eval(modified_response)"
],
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"text": [
"output: plt.hist(df['sepal_length'],bins=50)\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([ 1., 3., 1., 0., 4., 2., 5., 0., 6., 10., 0., 9., 4.,\n",
" 1., 0., 6., 7., 0., 6., 8., 7., 0., 3., 6., 0., 6.,\n",
" 4., 9., 0., 7., 5., 2., 0., 8., 3., 0., 4., 1., 1.,\n",
" 0., 3., 1., 0., 1., 0., 1., 0., 4., 0., 1.]),\n",
" array([4.3 , 4.372, 4.444, 4.516, 4.588, 4.66 , 4.732, 4.804, 4.876,\n",
" 4.948, 5.02 , 5.092, 5.164, 5.236, 5.308, 5.38 , 5.452, 5.524,\n",
" 5.596, 5.668, 5.74 , 5.812, 5.884, 5.956, 6.028, 6.1 , 6.172,\n",
" 6.244, 6.316, 6.388, 6.46 , 6.532, 6.604, 6.676, 6.748, 6.82 ,\n",
" 6.892, 6.964, 7.036, 7.108, 7.18 , 7.252, 7.324, 7.396, 7.468,\n",
" 7.54 , 7.612, 7.684, 7.756, 7.828, 7.9 ]),\n",
" <a list of 50 Patch objects>)"
]
},
"metadata": {
"tags": []
},
"execution_count": 28
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "onnSCklcLcJT",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
================================================
FILE: gpt.py
================================================
"""Creates the Example and GPT classes for a user to interface with the OpenAI API."""
import openai
def set_openai_key(key):
"""Sets OpenAI key."""
openai.api_key = key
class Example():
"""Stores an input, output pair and formats it to prime the model."""
def __init__(self, inp, out):
self.input = inp
self.output = out
def get_input(self):
"""Returns the input of the example."""
return self.input
def get_output(self):
"""Returns the intended output of the example."""
return self.output
def format(self):
"""Formats the input, output pair."""
return f"input: {self.input}\noutput: {self.output}\n"
class GPT:
"""The main class for a user to interface with the OpenAI API.
A user can add examples and set parameters of the API request."""
def __init__(self, engine='davinci',
temperature=0.5,
max_tokens=100):
self.examples = []
self.engine = engine
self.temperature = temperature
self.max_tokens = max_tokens
def add_example(self, ex):
"""Adds an example to the object. Example must be an instance
of the Example class."""
assert isinstance(ex, Example), "Please create an Example object."
self.examples.append(ex.format())
def get_prime_text(self):
"""Formats all examples to prime the model."""
return '\n'.join(self.examples) + '\n'
def get_engine(self):
"""Returns the engine specified for the API."""
return self.engine
def get_temperature(self):
"""Returns the temperature specified for the API."""
return self.temperature
def get_max_tokens(self):
"""Returns the max tokens specified for the API."""
return self.max_tokens
def craft_query(self, prompt):
"""Creates the query for the API request."""
return self.get_prime_text() + "input: " + prompt + "\n"
def submit_request(self, prompt):
"""Calls the OpenAI API with the specified parameters."""
response = openai.Completion.create(engine=self.get_engine(),
prompt=self.craft_query(prompt),
max_tokens=self.get_max_tokens(),
temperature=self.get_temperature(),
top_p=1,
n=1,
stream=False,
stop="\ninput:")
return response
def get_top_reply(self, prompt):
"""Obtains the best result as returned by the API."""
response = self.submit_request(prompt)
return response['choices'][0]['text']
================================================
FILE: iris.csv
================================================
sepal_length,sepal_width,petal_length,petal_width,species
5.1,3.5,1.4,0.2,setosa
4.9,3.0,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5.0,3.6,1.4,0.2,setosa
5.4,3.9,1.7,0.4,setosa
4.6,3.4,1.4,0.3,setosa
5.0,3.4,1.5,0.2,setosa
4.4,2.9,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.4,3.7,1.5,0.2,setosa
4.8,3.4,1.6,0.2,setosa
4.8,3.0,1.4,0.1,setosa
4.3,3.0,1.1,0.1,setosa
5.8,4.0,1.2,0.2,setosa
5.7,4.4,1.5,0.4,setosa
5.4,3.9,1.3,0.4,setosa
5.1,3.5,1.4,0.3,setosa
5.7,3.8,1.7,0.3,setosa
5.1,3.8,1.5,0.3,setosa
5.4,3.4,1.7,0.2,setosa
5.1,3.7,1.5,0.4,setosa
4.6,3.6,1.0,0.2,setosa
5.1,3.3,1.7,0.5,setosa
4.8,3.4,1.9,0.2,setosa
5.0,3.0,1.6,0.2,setosa
5.0,3.4,1.6,0.4,setosa
5.2,3.5,1.5,0.2,setosa
5.2,3.4,1.4,0.2,setosa
4.7,3.2,1.6,0.2,setosa
4.8,3.1,1.6,0.2,setosa
5.4,3.4,1.5,0.4,setosa
5.2,4.1,1.5,0.1,setosa
5.5,4.2,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.0,3.2,1.2,0.2,setosa
5.5,3.5,1.3,0.2,setosa
4.9,3.1,1.5,0.1,setosa
4.4,3.0,1.3,0.2,setosa
5.1,3.4,1.5,0.2,setosa
5.0,3.5,1.3,0.3,setosa
4.5,2.3,1.3,0.3,setosa
4.4,3.2,1.3,0.2,setosa
5.0,3.5,1.6,0.6,setosa
5.1,3.8,1.9,0.4,setosa
4.8,3.0,1.4,0.3,setosa
5.1,3.8,1.6,0.2,setosa
4.6,3.2,1.4,0.2,setosa
5.3,3.7,1.5,0.2,setosa
5.0,3.3,1.4,0.2,setosa
7.0,3.2,4.7,1.4,versicolor
6.4,3.2,4.5,1.5,versicolor
6.9,3.1,4.9,1.5,versicolor
5.5,2.3,4.0,1.3,versicolor
6.5,2.8,4.6,1.5,versicolor
5.7,2.8,4.5,1.3,versicolor
6.3,3.3,4.7,1.6,versicolor
4.9,2.4,3.3,1.0,versicolor
6.6,2.9,4.6,1.3,versicolor
5.2,2.7,3.9,1.4,versicolor
5.0,2.0,3.5,1.0,versicolor
5.9,3.0,4.2,1.5,versicolor
6.0,2.2,4.0,1.0,versicolor
6.1,2.9,4.7,1.4,versicolor
5.6,2.9,3.6,1.3,versicolor
6.7,3.1,4.4,1.4,versicolor
5.6,3.0,4.5,1.5,versicolor
5.8,2.7,4.1,1.0,versicolor
6.2,2.2,4.5,1.5,versicolor
5.6,2.5,3.9,1.1,versicolor
5.9,3.2,4.8,1.8,versicolor
6.1,2.8,4.0,1.3,versicolor
6.3,2.5,4.9,1.5,versicolor
6.1,2.8,4.7,1.2,versicolor
6.4,2.9,4.3,1.3,versicolor
6.6,3.0,4.4,1.4,versicolor
6.8,2.8,4.8,1.4,versicolor
6.7,3.0,5.0,1.7,versicolor
6.0,2.9,4.5,1.5,versicolor
5.7,2.6,3.5,1.0,versicolor
5.5,2.4,3.8,1.1,versicolor
5.5,2.4,3.7,1.0,versicolor
5.8,2.7,3.9,1.2,versicolor
6.0,2.7,5.1,1.6,versicolor
5.4,3.0,4.5,1.5,versicolor
6.0,3.4,4.5,1.6,versicolor
6.7,3.1,4.7,1.5,versicolor
6.3,2.3,4.4,1.3,versicolor
5.6,3.0,4.1,1.3,versicolor
5.5,2.5,4.0,1.3,versicolor
5.5,2.6,4.4,1.2,versicolor
6.1,3.0,4.6,1.4,versicolor
5.8,2.6,4.0,1.2,versicolor
5.0,2.3,3.3,1.0,versicolor
5.6,2.7,4.2,1.3,versicolor
5.7,3.0,4.2,1.2,versicolor
5.7,2.9,4.2,1.3,versicolor
6.2,2.9,4.3,1.3,versicolor
5.1,2.5,3.0,1.1,versicolor
5.7,2.8,4.1,1.3,versicolor
6.3,3.3,6.0,2.5,virginica
5.8,2.7,5.1,1.9,virginica
7.1,3.0,5.9,2.1,virginica
6.3,2.9,5.6,1.8,virginica
6.5,3.0,5.8,2.2,virginica
7.6,3.0,6.6,2.1,virginica
4.9,2.5,4.5,1.7,virginica
7.3,2.9,6.3,1.8,virginica
6.7,2.5,5.8,1.8,virginica
7.2,3.6,6.1,2.5,virginica
6.5,3.2,5.1,2.0,virginica
6.4,2.7,5.3,1.9,virginica
6.8,3.0,5.5,2.1,virginica
5.7,2.5,5.0,2.0,virginica
5.8,2.8,5.1,2.4,virginica
6.4,3.2,5.3,2.3,virginica
6.5,3.0,5.5,1.8,virginica
7.7,3.8,6.7,2.2,virginica
7.7,2.6,6.9,2.3,virginica
6.0,2.2,5.0,1.5,virginica
6.9,3.2,5.7,2.3,virginica
5.6,2.8,4.9,2.0,virginica
7.7,2.8,6.7,2.0,virginica
6.3,2.7,4.9,1.8,virginica
6.7,3.3,5.7,2.1,virginica
7.2,3.2,6.0,1.8,virginica
6.2,2.8,4.8,1.8,virginica
6.1,3.0,4.9,1.8,virginica
6.4,2.8,5.6,2.1,virginica
7.2,3.0,5.8,1.6,virginica
7.4,2.8,6.1,1.9,virginica
7.9,3.8,6.4,2.0,virginica
6.4,2.8,5.6,2.2,virginica
6.3,2.8,5.1,1.5,virginica
6.1,2.6,5.6,1.4,virginica
7.7,3.0,6.1,2.3,virginica
6.3,3.4,5.6,2.4,virginica
6.4,3.1,5.5,1.8,virginica
6.0,3.0,4.8,1.8,virginica
6.9,3.1,5.4,2.1,virginica
6.7,3.1,5.6,2.4,virginica
6.9,3.1,5.1,2.3,virginica
5.8,2.7,5.1,1.9,virginica
6.8,3.2,5.9,2.3,virginica
6.7,3.3,5.7,2.5,virginica
6.7,3.0,5.2,2.3,virginica
6.3,2.5,5.0,1.9,virginica
6.5,3.0,5.2,2.0,virginica
6.2,3.4,5.4,2.3,virginica
5.9,3.0,5.1,1.8,virginica
gitextract_qjsi6js3/ ├── .github/ │ └── FUNDING.yml ├── .gitignore ├── LICENSE ├── README.md ├── gpt-3-notebook.ipynb ├── gpt-pandas-code-generation.ipynb ├── gpt-pandas-matplotlib.ipynb ├── gpt.py └── iris.csv
SYMBOL INDEX (16 symbols across 1 files)
FILE: gpt.py
function set_openai_key (line 6) | def set_openai_key(key):
class Example (line 10) | class Example():
method __init__ (line 13) | def __init__(self, inp, out):
method get_input (line 17) | def get_input(self):
method get_output (line 21) | def get_output(self):
method format (line 25) | def format(self):
class GPT (line 30) | class GPT:
method __init__ (line 34) | def __init__(self, engine='davinci',
method add_example (line 42) | def add_example(self, ex):
method get_prime_text (line 48) | def get_prime_text(self):
method get_engine (line 52) | def get_engine(self):
method get_temperature (line 56) | def get_temperature(self):
method get_max_tokens (line 60) | def get_max_tokens(self):
method craft_query (line 64) | def craft_query(self, prompt):
method submit_request (line 68) | def submit_request(self, prompt):
method get_top_reply (line 80) | def get_top_reply(self, prompt):
Condensed preview — 9 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (113K chars).
[
{
"path": ".github/FUNDING.yml",
"chars": 102,
"preview": "# These are supported funding model platforms\ncustom: ['https://www.buymeacoffee.com/bhattbhavesh91']\n"
},
{
"path": ".gitignore",
"chars": 607,
"preview": "MANIFEST\nbuild\ndist\n_build\ndocs/man/*.gz\ndocs/source/api/generated\ndocs/source/config.rst\ndocs/gh-pages\nnotebook/i18n/*/"
},
{
"path": "LICENSE",
"chars": 11357,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 2003,
"preview": "# OpenAI's GPT-3 to generate SQL from Natural Language!\n\n**If you like my work, you can support me by buying me a coffee"
},
{
"path": "gpt-3-notebook.ipynb",
"chars": 15667,
"preview": "{\"nbformat\":4,\"nbformat_minor\":0,\"metadata\":{\"colab\":{\"name\":\"GPT-3-Demo.ipynb\",\"provenance\":[],\"collapsed_sections\":[],"
},
{
"path": "gpt-pandas-code-generation.ipynb",
"chars": 14247,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"GPT-3-Pandas-Code.ipynb\",\n "
},
{
"path": "gpt-pandas-matplotlib.ipynb",
"chars": 53580,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"gpt-pandas-code-generation.ipynb"
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{
"path": "gpt.py",
"chars": 2839,
"preview": "\"\"\"Creates the Example and GPT classes for a user to interface with the OpenAI API.\"\"\"\n\nimport openai\n\n\ndef set_openai_k"
},
{
"path": "iris.csv",
"chars": 3858,
"preview": "sepal_length,sepal_width,petal_length,petal_width,species\n5.1,3.5,1.4,0.2,setosa\n4.9,3.0,1.4,0.2,setosa\n4.7,3.2,1.3,0.2,"
}
]
About this extraction
This page contains the full source code of the bhattbhavesh91/gpt-3-simple-tutorial GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 9 files (101.8 KB), approximately 41.7k tokens, and a symbol index with 16 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.