Delete pages/chat_rag.py

pages/chat_rag.py

@@ -1,76 +0,0 @@
-import streamlit as st
-from langchain_core.messages import AIMessage, HumanMessage
-from functions.gptResponse import get_response
-from functions.sidebar import sidebar
-from functions.web_chain import vectorize, loadPdfData, loadUrlData
-import asyncio
-
-async def main():
-    sidebar()
-    st.title("Upload Data")
-
-    uploaded_files = st.file_uploader("Upload PDFs", accept_multiple_files=True)
-    url = st.text_input("Enter a website link")
-    if st.button('Process URL and Files'):
-        if url:
-            try:
-                urlData = loadUrlData(url)
-                st.session_state.data.extend(urlData)
-            except Exception as e:
-                st.error(f"Failed to load URL: {e}")
-        if uploaded_files:
-            try:
-                print(uploaded_files)
-                pdfData = loadPdfData(uploaded_files)
-                print(pdfData)
-                st.session_state.data.extend(pdfData)
-            except Exception as e:
-                st.error(f"Failed to load URL: {e}")
-        with st.spinner("Processing..."):
-            try:
-                if "data" in st.session_state:
-                    st.session_state.retriever = vectorize(st.session_state.data)
-                    st.success("Done")
-            except Exception as e:
-                st.error(f"Failed to process Data: {e}")
-            
-
-    if "chat_history" not in st.session_state:
-        st.session_state.chat_history = [AIMessage(content="Hello, I am a bot. How can I help you?")]
-
-    st.title("RAG CHAT")
-    for message in st.session_state.chat_history:
-        if isinstance(message, AIMessage):
-            with st.chat_message("AI"):
-                st.write(message.content)
-        elif isinstance(message, HumanMessage):
-            with st.chat_message("Human"):
-                st.write(message.content)
-
-    user_query = st.text_input("Type your message here...", key="chat_input")
-    if user_query:
-        st.session_state.chat_history.append(HumanMessage(content=user_query))
-        with st.chat_message("Human"):
-            st.write(user_query)
-
-        if 'retriever' in st.session_state:
-            try:
-                with st.spinner("Retrieving Information..."):
-                    ragAnswer = await st.session_state.retriever.amax_marginal_relevance_search(user_query, k=2, fetch_k=10)
-                context = []
-                for i, doc in enumerate(ragAnswer):
-                    print(f"{i}: {doc.page_content}")
-                    context.append(doc.page_content)
-                with st.spinner("Generating Response"):
-                    response = get_response(user_query, st.session_state.chat_history, context)
-                if response:
-                    st.session_state.chat_history.append(AIMessage(content=response))
-                    with st.chat_message("AI"):
-                        st.write(response)
-                else:
-                    st.write("No response received.") 
-            except Exception as e:
-                st.error(f"Error during retrieval or response generation: {e}")
-
-if __name__ == "__main__":
-    asyncio.run(main())