Add application file

app.py

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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.")
+st.write('Demo con Midudev')
+
+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 4 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')