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| 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/bsFXgfbj8Bc", | |
| # 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: 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 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 is done via the OpenAI API with "text-embedding-ada-002": https://platform.openai.com/docs/guides/embeddings') | |
| 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: " Video about: '+string_title+'. '+text_embedding+'", answer the following question. \n'+user_input | |
| # uncomment to see the embedding | |
| #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') | |