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Repository: davila7/youtube-gpt
Branch: main
Commit: e8db47ab3988
Files: 8
Total size: 4.8 MB
Directory structure:
gitextract__75q_8i1/
├── .gitignore
├── LICENSE
├── README.md
├── app.py
├── packages.txt
├── requirements.txt
├── transcription.csv
└── word_embeddings.csv
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
# Editors
.vscode/
.idea/
# Youtube GPT resources
# transcription.csv
# word_embeddings.csv
youtube_video.mp4
# Vagrant
.vagrant/
# Mac/OSX
.DS_Store
# Windows
Thumbs.db
# Source for the following rules: https://raw.githubusercontent.com/github/gitignore/master/Python.gitignore
# 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/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# 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
# 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
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2023 Daniel Avila
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
<h1 align="center">
YoutubeGPT 🤖
</h1>
Read the article to know how it works: <a href="https://medium.com/@dan.avila7/youtube-gpt-start-a-chat-with-a-video-efe92a499e60">Medium Article</a>
With Youtube GPT you will be able to extract all the information from a video on YouTube just by pasting the video link.
You will obtain the transcription, the embedding of each segment and also ask questions to the video through a chat.
All code was written with the help of <a href="https://codegpt.co">Code GPT</a>
<a href="https://codegpt.co" target="_blank"><img width="753" alt="Captura de Pantalla 2023-02-08 a la(s) 9 16 43 p m" src="https://user-images.githubusercontent.com/6216945/217699939-eca3ae47-c488-44da-9cf6-c7caef69e1a7.png"></a>
<hr>
<br>
# Features
- Video transcription with **OpenAI Whisper**
- Embedding Transcript Segments with the OpenAI API (**text-embedding-ada-002**)
- Chat with the video using **streamlit-chat** and OpenAI API (**text-davinci-003**)
# Example
For this example we are going to use this video from The PyCoach
https://youtu.be/lKO3qDLCAnk
Add the video URL and then click Start Analysis
## Pytube and OpenAI Whisper
The video will be downloaded with pytube and then OpenAI Whisper will take care of transcribing and segmenting the video.
```python
# Get the video
youtube_video = YouTube(youtube_link)
streams = youtube_video.streams.filter(only_audio=True)
mp4_video = stream.download(filename='youtube_video.mp4')
audio_file = open(mp4_video, 'rb')
# whisper load base model
model = whisper.load_model('base')
# Whisper transcription
output = model.transcribe("youtube_video.mp4")
```
## Embedding with "text-embedding-ada-002"
We obtain the vectors with **text-embedding-ada-002** of each segment delivered by whisper
```python
# Embeddings
segments = output['segments']
for segment in segments:
openai.api_key = user_secret
response = openai.Embedding.create(
input= segment["text"].strip(),
model="text-embedding-ada-002"
)
embeddings = response['data'][0]['embedding']
meta = {
"text": segment["text"].strip(),
"start": segment['start'],
"end": segment['end'],
"embedding": embeddings
}
data.append(meta)
pd.DataFrame(data).to_csv('word_embeddings.csv')
```
## OpenAI GPT-3
We make a question to the vectorized text, we do the search of the context and then we send the prompt with the context to the model "text-davinci-003"
We can even ask direct questions about what happened in the video. For example, here we ask about how long the exercise with Numpy that Pycoach did in the video took.
# Running Locally
1. Clone the repository
```bash
git clone https://github.com/davila7/youtube-gpt
cd youtube-gpt
```
2. Install dependencies
These dependencies are required to install with the requirements.txt file:
* streamlit
* streamlit_chat
* matplotlib
* plotly
* scipy
* sklearn
* pandas
* numpy
* git+https://github.com/openai/whisper.git
* pytube
* openai-whisper
```bash
pip install -r requirements.txt
```
3. Run the Streamlit server
```bash
streamlit run app.py
```
## Upcoming Features 🚀
- Semantic search with embedding
- Chart with emotional analysis
- Connect with Pinecone
================================================
FILE: app.py
================================================
import pandas as pd
import numpy as np
import streamlit as st
import whisper
import pytube
from pytube import YouTube
from streamlit_chat import message
import openai
from openai.embeddings_utils import get_embedding, distances_from_embeddings
import os
import pinecone
from dotenv import load_dotenv
# whisper
model = whisper.load_model('base')
output = ''
data = []
data_transcription = []
embeddings = []
mp4_video = ''
audio_file = ''
# Pinacone
# Uncomment this section if you want to save the embedding in pinecone
#load_dotenv()
# initialize connection to pinecone (get API key at app.pinecone.io)
# pinecone.init(
# api_key=os.getenv("PINACONE_API_KEY"),
# environment=os.getenv("PINACONE_ENVIRONMENT")
# )
array = []
# Uncomment this section if you want to upload your own video
# Sidebar
# with st.sidebar:
# user_secret = st.text_input(label = ":blue[OpenAI API key]",
# value="",
# placeholder = "Paste your openAI API key, sk-",
# type = "password")
# youtube_link = st.text_input(label = ":red[Youtube link]",
# value="https://youtu.be/rQeXGvFAJDQ",
# placeholder = "")
# if youtube_link and user_secret:
# youtube_video = YouTube(youtube_link)
# video_id = pytube.extract.video_id(youtube_link)
# streams = youtube_video.streams.filter(only_audio=True)
# stream = streams.first()
# if st.button("Start Analysis"):
# if os.path.exists("word_embeddings.csv"):
# os.remove("word_embeddings.csv")
# with st.spinner('Running process...'):
# # Get the video mp4
# mp4_video = stream.download(filename='youtube_video.mp4')
# audio_file = open(mp4_video, 'rb')
# st.write(youtube_video.title)
# st.video(youtube_link)
# # Whisper
# output = model.transcribe("youtube_video.mp4")
# # Transcription
# transcription = {
# "title": youtube_video.title.strip(),
# "transcription": output['text']
# }
# data_transcription.append(transcription)
# pd.DataFrame(data_transcription).to_csv('transcription.csv')
# segments = output['segments']
# # Pinacone index
# # check if index_name index already exists (only create index if not)
# # index_name = str(video_id)
# # # check if 'index_name' index already exists (only create index if not)
# # if 'index1' not in pinecone.list_indexes():
# # pinecone.create_index('index1', dimension=len(segments))
# # # connect to index
# # index = pinecone.Index('index1')
# #st.write(segments)
# #Embeddings
# for segment in segments:
# openai.api_key = user_secret
# response = openai.Embedding.create(
# input= segment["text"].strip(),
# model="text-embedding-ada-002"
# )
# embeddings = response['data'][0]['embedding']
# meta = {
# "text": segment["text"].strip(),
# "start": segment['start'],
# "end": segment['end'],
# "embedding": embeddings
# }
# data.append(meta)
# # upsert_response = index.upsert(
# # vectors=data,
# # namespace=video_id
# # )
# pd.DataFrame(data).to_csv('word_embeddings.csv')
# os.remove("youtube_video.mp4")
# st.success('Analysis completed')
st.markdown('<h1>Youtube GPT 🤖<small> by <a href="https://codegpt.co">Code GPT</a></small></h1>', unsafe_allow_html=True)
st.write("Start a chat with this video of Microsoft CEO Satya Nadella's interview. You just need to add your OpenAI API Key and paste it in the 'Chat with the video' tab.")
DEFAULT_WIDTH = 80
VIDEO_DATA = "https://youtu.be/bsFXgfbj8Bc"
width = 40
width = max(width, 0.01)
side = max((100 - width) / 2, 0.01)
_, container, _ = st.columns([side, 47, side])
container.video(data=VIDEO_DATA)
tab1, tab2, tab3, tab4 = st.tabs(["Intro", "Transcription", "Embedding", "Chat with the Video"])
with tab1:
st.markdown("### How does it work?")
st.markdown('Read the article to know how it works: [Medium Article]("https://medium.com/@dan.avila7/youtube-gpt-start-a-chat-with-a-video-efe92a499e60")')
st.write("Youtube GPT was written with the following tools:")
st.markdown("#### Code GPT")
st.write("All code was written with the help of Code GPT. Visit [codegpt.co]('https://codegpt.co') to get the extension.")
st.markdown("#### Streamlit")
st.write("The design was written with [Streamlit]('https://streamlit.io/').")
st.markdown("#### Whisper")
st.write("Video transcription is done by [OpenAI Whisper]('https://openai.com/blog/whisper/').")
st.markdown("#### Embedding")
st.write('[Embedding]("https://platform.openai.com/docs/guides/embeddings") is done via the OpenAI API with "text-embedding-ada-002"')
st.markdown("#### GPT-3")
st.write('The chat uses the OpenAI API with the [GPT-3]("https://platform.openai.com/docs/models/gpt-3") model "text-davinci-003""')
st.markdown("""---""")
st.write('Author: [Daniel Ávila](https://www.linkedin.com/in/daniel-avila-arias/)')
st.write('Repo: [Github](https://github.com/davila7/youtube-gpt)')
st.write("This software was developed with Code GPT, for more information visit: https://codegpt.co")
with tab2:
st.header("Transcription:")
if(os.path.exists("youtube_video.mp4")):
audio_file = open('youtube_video.mp4', 'rb')
audio_bytes = audio_file.read()
st.audio(audio_bytes, format='audio/ogg')
if os.path.exists("transcription.csv"):
df = pd.read_csv('transcription.csv')
st.write(df)
with tab3:
st.header("Embedding:")
if os.path.exists("word_embeddings.csv"):
df = pd.read_csv('word_embeddings.csv')
st.write(df)
with tab4:
user_secret = st.text_input(label = ":blue[OpenAI API key]",
placeholder = "Paste your openAI API key, sk-",
type = "password")
st.write('To obtain an API Key you must create an OpenAI account at the following link: https://openai.com/api/')
if 'generated' not in st.session_state:
st.session_state['generated'] = []
if 'past' not in st.session_state:
st.session_state['past'] = []
def get_text():
if user_secret:
st.header("Ask me something about the video:")
input_text = st.text_input("You: ","", key="input")
return input_text
user_input = get_text()
def get_embedding_text(api_key, prompt):
openai.api_key = user_secret
response = openai.Embedding.create(
input= prompt.strip(),
model="text-embedding-ada-002"
)
q_embedding = response['data'][0]['embedding']
df=pd.read_csv('word_embeddings.csv', index_col=0)
df['embedding'] = df['embedding'].apply(eval).apply(np.array)
df['distances'] = distances_from_embeddings(q_embedding, df['embedding'].values, distance_metric='cosine')
returns = []
# Sort by distance with 2 hints
for i, row in df.sort_values('distances', ascending=True).head(4).iterrows():
# Else add it to the text that is being returned
returns.append(row["text"])
# Return the context
return "\n\n###\n\n".join(returns)
def generate_response(api_key, prompt):
one_shot_prompt = '''I am YoutubeGPT, a highly intelligent question answering bot. If you ask me a question that is rooted in truth, I will give you the answer.
Q: What is human life expectancy in the United States?
A: Human life expectancy in the United States is 78 years.
Q: '''+prompt+'''
A: '''
completions = openai.Completion.create(
engine = "text-davinci-003",
prompt = one_shot_prompt,
max_tokens = 1024,
n = 1,
stop=["Q:"],
temperature=0.2,
)
message = completions.choices[0].text
return message
if user_input:
text_embedding = get_embedding_text(user_secret, user_input)
title = pd.read_csv('transcription.csv')['title']
string_title = "\n\n###\n\n".join(title)
user_input_embedding = 'Using this context: "'+string_title+'. '+text_embedding+'", answer the following question. \n'+user_input
# st.write(user_input_embedding)
output = generate_response(user_secret, user_input_embedding)
st.session_state.past.append(user_input)
st.session_state.generated.append(output)
if st.session_state['generated']:
for i in range(len(st.session_state['generated'])-1, -1, -1):
message(st.session_state["generated"][i], key=str(i))
message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')
================================================
FILE: packages.txt
================================================
ffmpeg
================================================
FILE: requirements.txt
================================================
#
# This file is autogenerated by pip-compile with python 3.10
# To update, run:
#
# pip-compile
#
matplotlib
plotly
scipy
sklearn
scikit-learn
pandas
numpy
git+https://github.com/openai/whisper.git
pytube
streamlit
streamlit_chat
openai
pinecone-client
python-dotenv
================================================
FILE: transcription.csv
================================================
,title,transcription
0,Can Bing and OpenAI Challenge Google? Microsoft's Satya Nadella Weighs In (Exclusive) | WSJ," Thing with AI is going to completely change what people can expect from search. We are grounded in the fact that Google dominates this space. I feel like a new race is starting with a complete new platform technology. I'm excited for the users to have choice finally and a real competitive race out there. Microsoft CEO Sachin Adela finally thinks he has what it takes to make Bing happen. And grab some search engine market share from Google. Is not so secret weapon? This all started with us and OpenAI coming together with really a research agenda on moving the state of the art away I forward. Microsoft is taking OpenAI's newest artificial intelligence model and intertwining it with Bing's search data. So you can chat with the search engine and get up to date information. So the entire point of chat GPG early on was to just sort of show the power of the large model and the conversational intelligence. We now have not only the current information that you usually expect from a search engine but you can then have a full conversation around that current information and all the other information that came before. On top of the new AI chat features in Bing, Microsoft is adding chat GPT-like features to its edge browser. So I came here to Microsoft's headquarters in Redmond, Washington to get a hands-on demo of the new tools and hear Nidale's thoughts on AI. The first big new feature, Bing Chat. Incorporated right into the front page of Bing will be an ask me anything box. Type in your query, say... The biggest winners of the 2023 Grammys because I did not have time to watch it. And up pops your usual search results, which Nidale says has also been improved with AI. But more interesting is this new chat box that will start typing out an answer to the question. As it answers, it also includes citations so you know where the information is coming from. It allows me to then have a full-on conversation around all the search data. So yes, it's annotated, it's about being able to create even the links back to all the publishers. So this is just search, just better. The system took about a minute to return a list of the top Grammy winners. Not exactly fast, but you can tell it to stop responding. Then you can ask follow-up questions like do you know if Beyoncé is touring? If you're a Beyoncé fan, you might want to hurry and get your tickets before they're gone. Like Chatchy BT, you can ask it non-search type things too. Like... Write ten questions for Sasha Nadella about AI. What are the key features and benefits of new Bing chat mode? And how does it leverage AI to enhance the user experience? It really is grounded in search results and helps me have a very contextual chat or to be able to get to the right answers. Sasha Bing said or AI, Sasha said, the new Bing chat mode leverages AI to understand the user's intent, context, you said context, and preferences to provide relevant, visual, logical, and actionable responses. I love the AI software. But while these may seem like fun and games in these demos, there are, of course, real misuses of this technology. We have a lot of practice in thinking about safety. AI is only good if it is being used in the real world, understanding, learning human preferences. And our intent is to do that, which is to put stuff out there with safety, but at the same time realizing that we are going to have to think about safety as an ongoing responsibility, not a one-time responsibility. He also told me there are guardrails in place to prevent harmful content, hate speech, and more. The second big feature, Microsoft Edge Integration. The Microsoft Edge browser will now have a Bing sidebar, where you can ask it to summarize a web page run or ask it to generate text. For instance, I asked it to, I want to write about a letter to Sasha Nadella, thanking him for his time. Then I was able to tell it I wanted it written in an enthusiastic tone, an email form, and short. It knows. That's pretty good. There's a lot of fear that AI is going to take our jobs, replace us. Is that unfounded? And how do you think about tools like Bing now, and how we use it in the job market? Like, I think of this as at the foundational level, going to help us do our jobs better. Reduce some of the drudgery in some of our jobs, whether it's encoding or in writing or in automating workflow, or searching for information. So at the fundamental level, I think we need a productivity boost. I feel it'll create more jobs. The barriers to knowledge work will come down. So, I mean, the unintended consequences around labor market shifts are always something we need to be mindful of. But I don't subscribe to this zero sum or one lump of labor fallacy. And I think that we're going to have new jobs get created, and more job opportunities. And it sounds like more time, right? For sure. We outsource some of this. I outsource some of my question writing to this, freed up a little bit more of my time to do something else. But you're still in the loop and you're in charge, because you get to accept the draft. That's kind of one of the metaphors I have. It's pretty much all computer interaction going forward. You'll start with a draft. That doesn't mean you don't get to inspect the draft, to prove the draft, and redefine or, you know, and edit the draft. The question is, which company will write that draft for us? Will it be Microsoft with tools like this? Or Google, which owns 90% of the search engine market share? And right before my interview with Mr. Nadella announced Bard, its similar AI chat feature that will come to its own search engine. How are you thinking about monetizing this? The last time I checked software, I mean, it's a search was the most profitable category there is on planet Earth. So therefore, all I need is a few more users. And someone else that I'm competing in has to keep all of their users and all of their gross margin. It's a love, I'm looking forward to that. So no plans to charge for new Bing. It's really advertising model all the way through. We'll start there. And if there are other models, there may be other models. But there is enough surplus. Let me put it this way. There is so much surplus that goes to one place, which I think would be nice if it was evenly distributed. What about Microsoft Office? To stay tuned. I fully expect us to introduce these capabilities quite frankly across the length and breadth of Microsoft. What about this guy? Oh, you know, it's sort of like, it was early. And so we get to redefine it for the AI age. And it's going to be very useful. So is he coming back? He'll come back in a variety of different ways. I think I'm most excited about being as the real incarnation of Clippy. Okay, but no actual pop up. I think we have much better design mechanisms to do what Clippy aspired to do back in the day. Does Clippy haunt your dreams? Not really. It's probably the thing that was the most fun character Microsoft introduced, which, you know, think about it, right? We have been on a 30-year journey to perfect it. So I'm excited to be here in 2023 launching Bing with AI."
================================================
FILE: word_embeddings.csv
================================================
,text,start,end,embedding
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2,I feel like a new race is starting with a complete new platform technology.,9.92,14.84,"[0.006320164073258638, -0.020897425711154938, 0.0037029595114290714, -0.022318288683891296, -0.002240206580609083, -0.01046211738139391, -0.01072350237518549, -0.02282765507698059, -0.012204686179757118, -0.024328945204615593, -0.0013806509086862206, 0.02529406175017357, -0.014932476915419102, -0.04640595614910126, 0.01296873576939106, 0.007794645614922047, 0.0196240097284317, -0.002974096452817321, -0.005194196477532387, 0.02066955156624317, -0.02178211323916912, 0.009832111187279224, 0.0062397378496825695, 0.0005696860607713461, -0.011836065910756588, -0.004761905409395695, 0.00781475193798542, -0.0050735571421682835, -6.089566886657849e-05, -0.004208974540233612, 0.013585337437689304, 0.007198150735348463, 0.008143159560859203, -0.006055427715182304, -0.02981133572757244, -0.022814251482486725, 0.01016722060739994, -0.0068764458410441875, 0.010850843973457813, -0.020870616659522057, 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3,I'm excited for the users to have choice finally and a real competitive race out there.,14.84,19.84,"[-0.010478480719029903, -0.015242652036249638, -0.008322137407958508, -0.023746730759739876, -0.011212985031306744, 0.011307325214147568, -0.014083617366850376, 0.0075674173422157764, -0.0366847887635231, -0.01978445053100586, 0.0053167338483035564, 0.029299315065145493, -0.016496026888489723, -0.029595812782645226, 0.00519880885258317, 0.010983874090015888, 0.016455594450235367, -0.016024326905608177, 0.009562035091221333, -0.0038443556986749172, 0.0036624143831431866, 0.016010848805308342, -0.024878811091184616, -0.01111864484846592, -0.01416448038071394, 0.006135470699518919, 0.005239240359514952, -0.021239981055259705, 0.017843740060925484, -0.015795214101672173, 0.026172617450356483, 0.019218409433960915, 0.0011152337538078427, -0.016698183491826057, -0.021657772362232208, -0.009710283018648624, 0.004039774648845196, -0.011893580667674541, 0.017722446471452713, 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5,And grab some search engine market share from Google.,25.64,28.64,"[-0.0032237558625638485, -0.00394839933142066, 0.015753980726003647, -0.012155523523688316, -0.007837374694645405, 0.0057311211712658405, -0.01834222860634327, -0.005727820098400116, -0.00032765770447440445, -0.026886090636253357, 0.006008433643728495, 0.03515264019370079, 0.021471897140145302, 0.0004914865712635219, -0.0025998030323535204, 0.004704405553638935, 0.03293414041399956, -0.01707451604306698, 0.01791965775191784, -0.014882427640259266, -0.005955612286925316, -0.004394080024212599, 0.013852410949766636, -0.009956832975149155, -0.014988071285188198, 0.017985684797167778, 0.020547522231936455, 0.008101481013000011, -0.001545851118862629, 0.01621616818010807, 0.019345836713910103, 0.0024264827370643616, -0.016533097252249718, 0.00461196806281805, -0.009243744425475597, 0.014829606749117374, 0.002883718116208911, 0.0039913165383040905, 0.016651945188641548, -0.01473716925829649, 0.010630305856466293, 0.016916051506996155, 0.0020814931485801935, -0.01149525586515665, -0.022132163867354393, -0.00556935602799058, -0.00790340080857277, -0.00712428567931056, -0.007810963783413172, 0.011898019351065159, -0.012630916200578213, 0.014169339090585709, -0.02143228054046631, -0.0012520321179181337, -0.00907867681235075, -0.00790340080857277, -0.012604505755007267, 0.00425212224945426, 0.011515064164996147, 0.0021607251837849617, 0.017338622361421585, 0.013126116245985031, -0.005301947705447674, -0.007962825708091259, -0.009283360093832016, -0.008543861098587513, -0.014169339090585709, 0.0017232979880645871, -0.005886284168809652, -0.012386617250740528, 0.024654386565089226, 0.0326700322329998, 0.0020385757088661194, -0.00515668885782361, 0.023690395057201385, 0.00011472146434243768, -0.0175499077886343, -0.018896853551268578, 0.025341063737869263, 0.0136147141456604, 0.01893647015094757, -0.007282749749720097, -0.010570881888270378, 0.003344254568219185, 0.011118903756141663, 0.015318204648792744, 0.00600513257086277, 0.024548742920160294, -0.02474682405591011, -0.00020880957890767604, 0.026727626100182533, -0.007388392463326454, -0.0005133579252287745, 0.018196970224380493, -0.016136936843395233, 0.01604449935257435, -0.021617155522108078, 0.010610497556626797, -4.216942215862218e-06, -0.034096211194992065, 0.027203017845749855, 0.013958053663372993, -0.02928946353495121, -0.0057212174870073795, -0.008279753848910332, 0.007843976840376854, 0.006404594052582979, -0.011726349592208862, 0.022356655448675156, -0.022409476339817047, -0.008339177817106247, 0.0020055624190717936, 0.02680685743689537, -0.02366398461163044, -0.01584641821682453, -0.035363923758268356, -0.0017926261061802506, 0.019028907641768456, 0.003671086858958006, -0.02069278061389923, 0.027942517772316933, 0.0076128835789859295, 0.025856072083115578, -0.004588858690112829, -0.016651945188641548, 0.004717610776424408, -0.0133
gitextract__75q_8i1/ ├── .gitignore ├── LICENSE ├── README.md ├── app.py ├── packages.txt ├── requirements.txt ├── transcription.csv └── word_embeddings.csv
SYMBOL INDEX (3 symbols across 1 files) FILE: app.py function get_text (line 162) | def get_text(): function get_embedding_text (line 169) | def get_embedding_text(api_key, prompt): function generate_response (line 190) | def generate_response(api_key, prompt):
Condensed preview — 8 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (4,995K chars).
[
{
"path": ".gitignore",
"chars": 1561,
"preview": "# Editors\n.vscode/\n.idea/\n\n# Youtube GPT resources\n# transcription.csv\n# word_embeddings.csv\nyoutube_video.mp4\n\n# Vagran"
},
{
"path": "LICENSE",
"chars": 1068,
"preview": "MIT License\n\nCopyright (c) 2023 Daniel Avila\n\nPermission is hereby granted, free of charge, to any person obtaining a co"
},
{
"path": "README.md",
"chars": 3819,
"preview": "<h1 align=\"center\">\nYoutubeGPT 🤖\n</h1>\n\nRead the article to know how it works: <a href=\"https://medium.com/@dan.avila7/y"
},
{
"path": "app.py",
"chars": 9561,
"preview": "import pandas as pd\nimport numpy as np\nimport streamlit as st\nimport whisper\nimport pytube\nfrom pytube import YouTube\nfr"
},
{
"path": "packages.txt",
"chars": 6,
"preview": "ffmpeg"
},
{
"path": "requirements.txt",
"chars": 270,
"preview": "#\n# This file is autogenerated by pip-compile with python 3.10\n# To update, run:\n#\n# pip-compile\n#\nmatplotlib\nplotly\n"
},
{
"path": "transcription.csv",
"chars": 7239,
"preview": ",title,transcription\n0,Can Bing and OpenAI Challenge Google? Microsoft's Satya Nadella Weighs In (Exclusive) | WSJ,\" Thi"
},
{
"path": "word_embeddings.csv",
"chars": 4969688,
"preview": ",text,start,end,embedding\n0,Thing with AI is going to completely change what people can expect from search.,0.0,5.76,\"[-"
}
]
About this extraction
This page contains the full source code of the davila7/youtube-gpt GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 8 files (4.8 MB), approximately 1.2M tokens, and a symbol index with 3 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.