Update app.py

app.py

@@ -1,5 +1,6 @@
 import os
 import logging
+import time
 from dotenv import load_dotenv
 import streamlit as st
 from PyPDF2 import PdfReader
@@ -9,8 +10,6 @@ from langchain_community.vectorstores import FAISS
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
 from langchain_groq import ChatGroq
-import time
-import io
 
 # --------------------------
 # Load environment variables
@@ -63,19 +62,13 @@ def get_vectorstore(text_chunks):
 # Conversation chain
 # --------------------------
 def get_conversation_chain(vectorstore):
-    try:
-        llm = ChatGroq(model="llama-3.3-70b-versatile", temperature=0.5)
-        memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
-        conversation_chain = ConversationalRetrievalChain.from_llm(
-            llm=llm,
-            retriever=vectorstore.as_retriever(),
-            memory=memory
-        )
-        logging.info("✅ Conversation chain created successfully.")
-        return conversation_chain
-    except Exception as e:
-        logging.error(f"Error creating conversation chain: {e}")
-        st.error("An error occurred while setting up the conversation chain.")
+    llm = ChatGroq(model="llama-3.3-70b-versatile", temperature=0.5)
+    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
+    return ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=vectorstore.as_retriever(),
+        memory=memory
+    )
 
 # --------------------------
 # Handle user input
@@ -85,35 +78,14 @@ def handle_userinput(user_question):
         start_time = time.time()
         with st.spinner("🤖 Thinking..."):
             response = st.session_state.conversation({'question': user_question})
-            st.session_state.chat_history = response['chat_history']
         elapsed_time = round(time.time() - start_time, 2)
 
-        # Display chat history in a chat-like format
-        for i, message in enumerate(st.session_state.chat_history):
-            if i % 2 == 0:
-                st.markdown(f"🧑 **You:** {message.content}")
-            else:
-                st.markdown(f"🤖 **Bot:** {message.content}")
-
-        # Stats
+        # Show response only (no chat history)
+        st.markdown(f"**🤖 Bot:** {response['answer']}")
         st.info(f"⏱ Response Time: {elapsed_time}s | 📄 Words: {len(response['answer'].split())}")
     else:
         st.warning("⚠ Please process the documents first.")
 
-# --------------------------
-# Export chat
-# --------------------------
-def export_chat():
-    if st.session_state.chat_history:
-        chat_text = "\n".join([f"{'User' if i % 2 == 0 else 'Bot'}: {m.content}" for i, m in enumerate(st.session_state.chat_history)])
-        buffer = io.BytesIO(chat_text.encode())
-        st.download_button(
-            label="💾 Download Chat",
-            data=buffer,
-            file_name="chat_history.txt",
-            mime="text/plain"
-        )
-
 # --------------------------
 # Main Streamlit App
 # --------------------------
@@ -125,8 +97,6 @@ def main():
     # Session state initialization
     if "conversation" not in st.session_state:
         st.session_state.conversation = None
-    if "chat_history" not in st.session_state:
-        st.session_state.chat_history = None
     if "pages_processed" not in st.session_state:
         st.session_state.pages_processed = 0
 
@@ -149,11 +119,6 @@ def main():
             else:
                 st.warning("Please upload at least one PDF.")
 
-        # Clear chat
-        if st.button("🗑 Clear Chat"):
-            st.session_state.chat_history = None
-            st.success("Chat cleared.")
-
     # Main Chat Section
     st.subheader("💬 Ask a Question")
     user_question = st.text_input("Type your question here...")
@@ -163,19 +128,5 @@ def main():
         else:
             st.warning("Please enter a question before submitting.")
 
-    # Chat History
-    if st.session_state.chat_history:
-        st.subheader("📝 Chat History")
-        for i, message in enumerate(st.session_state.chat_history):
-            role = "User" if i % 2 == 0 else "Bot"
-            st.markdown(f"**{role}:** {message.content}")
-
-        # Export chat
-        export_chat()
-
-    # Footer Branding
-    st.markdown("---")
-    st.markdown("**Made with ❤️ by Meesam Raza | Powered by LLaMA 3.3 & Groq AI**")
-
 if __name__ == '__main__':
     main()