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Delete app.py
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app.py
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import openai
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import streamlit as st
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from llama_index import VectorStoreIndex, download_loader
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from langchain.agents import initialize_agent, Tool
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from langchain.llms import OpenAI
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from langchain.chains.conversation.memory import ConversationBufferMemory
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from streamlit_chat import message
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def myApp():
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# Download SimpleWebPageReader
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SimpleWebPageReader = download_loader("SimpleWebPageReader")
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# Set OpenAI API key
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openai.api_key = "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" # Replace with your actual API key
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st.header("Chat with Web")
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# Input for the website URL
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website_url = st.text_input("Website URL", key="url")
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if website_url:
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try:
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# Initialize SimpleWebPageReader with the provided website URL
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loader = SimpleWebPageReader()
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documents = loader.load_data(urls=[website_url])
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# Create VectorStoreIndex from documents
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index = VectorStoreIndex.from_documents(documents)
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# Initialize LangChain OpenAI
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llm = OpenAI(openai_api_key="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", temperature=0, streaming = true)
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# Initialize ConversationBufferMemory
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memory = ConversationBufferMemory(memory_key="chat_history")
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# Initialize agent chain
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tools = [
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Tool(
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name="Website Index",
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func=lambda q: index.as_query_engine(),
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description="Useful when you want to answer questions about the text on websites.",
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),
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]
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query_engine = index.as_query_engine()
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# Get user input for the query
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user_query = st.text_input("Your Question")
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if st.button("Ask"):
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# Query the LangChain agent with user input
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message(user_query, is_user=True)
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response = query_engine.query(user_query)
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# Display the response
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st.text("Response:")
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message(str(response))
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except Exception as e:
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st.error(f"An error occurred: {e}")
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if __name__ == "__main__":
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myApp()
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