Update app.py

app.py

@@ -145,7 +145,7 @@ def query_vector_db(query, vector_db):
     return chat_completion.choices[0].message.content
 
 # Streamlit app
-st.title("Interactive PDF Reader and Chat")
+st.title("Interactive RAG-Based Application")
 
 # Upload PDF
 uploaded_file = st.file_uploader("Upload a PDF document", type=["pdf"])
@@ -155,34 +155,40 @@ if uploaded_file:
         temp_file.write(uploaded_file.read())
         pdf_path = temp_file.name
 
-    # Extract text, chunk it, and create embeddings
-    if "vector_db" not in st.session_state:
-        text = extract_text_from_pdf(pdf_path)
-        chunks = chunk_text(text)
-        st.session_state.vector_db = create_embeddings_and_store(chunks)
+    # Extract text
+    text = extract_text_from_pdf(pdf_path)
+    st.write("PDF Text Extracted Successfully!")
 
-    # Initialize chat history if not already done
+    # Chunk text
+    chunks = chunk_text(text)
+    st.write("Text Chunked Successfully!")
+
+    # Generate embeddings and store in FAISS
+    vector_db = create_embeddings_and_store(chunks)
+    st.write("Embeddings Generated and Stored Successfully!")
+
+    # Interactive chat section
+    st.write("### Interactive Chat Section")
+
+    # State management for chat history
     if "chat_history" not in st.session_state:
         st.session_state.chat_history = []
 
-    # Display chat history
-    for i, chat in enumerate(st.session_state.chat_history):
-        st.write(f"**Query {i+1}:** {chat['query']}")
-        st.write(f"**Response:** {chat['response']}")
-        st.write("---")
-
-    # Add new query input dynamically
-    query_key = f"query_{len(st.session_state.chat_history) + 1}"
-    user_query = st.text_input("Enter your query:", key=query_key)
+    # User query input
+    user_query = st.text_input("Enter your query:", key="user_query")
 
-    if user_query:
-        # Generate response
-        response = query_vector_db(user_query, st.session_state.vector_db)
+    if st.button("Submit Query"):
+        if user_query:
+            # Get response from the model
+            response = query_vector_db(user_query, vector_db)
 
-        # Append query and response to the chat history
-        st.session_state.chat_history.append({"query": user_query, "response": response})
+            # Append the query and response to the chat history
+            st.session_state.chat_history.append({"query": user_query, "response": response})
 
-        # Update query parameters to trigger a soft refresh
-        st.query_params["chat_length"] = len(st.session_state.chat_history)
+    # Display chat history
+    for chat in st.session_state.chat_history:
+        st.write(f"**User Query:** {chat['query']}")
+        st.write(f"**Response:** {chat['response']}")
+        st.write("---")