Upload app.py

app.py

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+# Import the necessary packages
+import os
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.document_loaders import YoutubeLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.llms import OpenAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+import textwrap
+import streamlit as st
+from apikey import apikey
+
+# Load  OpenAI API key and define directory to store the data
+os.environ["OPENAI_API_KEY"] = apikey
+persist_directory = "../../data/processed"
+
+# Load the OpenAI Embeddings, LLM , PromptTemplate and LLMChain
+embeddings = OpenAIEmbeddings()
+llm = OpenAI(temperature=0)
+# Define the template for the prompt
+template = """You can provide answers about YouTube videos using their transcripts.
+
+    For the question: {question}
+    Please refer to the video transcript: {docs_page_content}
+
+    Rely solely on the transcript's factual data to respond.
+
+    If the information isn't sufficient, simply state "I don't know".
+
+    Ensure your answers are comprehensive and in-depth.
+    """
+prompt = PromptTemplate(
+    input_variables=["question", "docs_page_content"],
+    template=template,
+)
+chain = LLMChain(llm=llm, prompt=prompt)
+
+
+# Setup streamlit
+st.title("YouTube Video Transcript Analyzer")
+# *** YOUR VIDEO URL and QUESTION ***
+video_url = st.text_input("Enter the YouTube video URL:")
+question = st.text_input("Enter your question about the video:")
+# add submit button
+# submit = st.button("Submit")
+#
+if video_url and question:
+    # load the video transcript
+    loader = YoutubeLoader.from_youtube_url(video_url, add_video_info=True)
+    # show the video title and author
+    info = loader._get_video_info()
+    st.write("**Title:**", info["title"])
+    st.write("**Author:**", info["author"])
+    # Split the transcript into chunks with 1500 characters and 150 characters overlap
+    transcript = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)
+    docs = text_splitter.split_documents(transcript)
+    # docs[0].page_content
+    # Create the vector database which will be used to search for similar sentences
+    vectordb = Chroma.from_documents(
+        documents=docs, embedding=embeddings, persist_directory=persist_directory
+    )
+
+    # Search for the most similar sentences to the question and concatenate top 3 vectors
+    docs = vectordb.similarity_search(query=question, k=3)
+    docs_page_content = " ".join([doc.page_content for doc in docs])
+    # docs[0].page_content
+    # send the question and the top 3 sentences to the LLMChain and print the response
+    response = chain.run(question=question, docs_page_content=docs_page_content)
+    st.write(textwrap.fill(response, width=85))