Upload 2 files

main.py

@@ -0,0 +1,64 @@
+import openai
+import streamlit as st
+from llama_index import VectorStoreIndex, download_loader
+from langchain.agents import initialize_agent, Tool
+from langchain.llms import OpenAI
+from langchain.chains.conversation.memory import ConversationBufferMemory
+from streamlit_chat import message
+
+def myApp():
+    # Download SimpleWebPageReader
+    SimpleWebPageReader = download_loader("SimpleWebPageReader")
+
+    # Set OpenAI API key
+    openai.api_key = "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"  # Replace with your actual API key
+
+    st.header("Chat with Web")
+
+    # Input for the website URL
+    website_url = st.text_input("Website URL", key="url")
+
+    if website_url:
+        try:
+            # Initialize SimpleWebPageReader with the provided website URL
+            loader = SimpleWebPageReader()
+            documents = loader.load_data(urls=[website_url])
+
+            # Create VectorStoreIndex from documents
+            index = VectorStoreIndex.from_documents(documents)
+
+            # Initialize LangChain OpenAI
+            llm = OpenAI(openai_api_key="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", temperature=0, streaming = true)
+
+            # Initialize ConversationBufferMemory
+            memory = ConversationBufferMemory(memory_key="chat_history")
+
+            # Initialize agent chain
+            tools = [
+                Tool(
+                    name="Website Index",
+                    func=lambda q: index.as_query_engine(),
+                    description="Useful when you want to answer questions about the text on websites.",
+                ),
+            ]
+            query_engine = index.as_query_engine()
+
+            # Get user input for the query
+            user_query = st.text_input("Your Question")
+
+            if st.button("Ask"):
+                # Query the LangChain agent with user input
+                message(user_query, is_user=True)
+                response = query_engine.query(user_query)
+
+                # Display the response
+                st.text("Response:")
+                message(str(response))
+
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+
+
+
+if __name__ == "__main__":
+    myApp()