Update app.py

app.py

@@ -10,20 +10,30 @@ import nest_asyncio
 from langchain.memory import ConversationBufferWindowMemory
 from langchain_community.chat_message_histories import StreamlitChatMessageHistory
 from dotenv import load_dotenv
-nest_asyncio.apply()
 
+nest_asyncio.apply()
 load_dotenv()
+
 st.set_page_config(layout='wide', page_title="InsightFusion Chat")
+
 memory_storage = StreamlitChatMessageHistory(key="chat_messages")
-memory = ConversationBufferWindowMemory(memory_key="chat_history", human_prefix="User", chat_memory=memory_storage, k=3)
+memory = ConversationBufferWindowMemory(
+    memory_key="chat_history",
+    human_prefix="User",
+    chat_memory=memory_storage,
+    k=3
+)
 
 image_bg = r"data/pexels-andreea-ch-371539-1166644.jpg"
 
 def add_bg_from_local(image_file):
     with open(image_file, "rb") as image_file:
         encoded_string = base64.b64encode(image_file.read())
-    st.markdown(f"""<style>.stApp {{background-image: url(data:image/{"png"};base64,{encoded_string.decode()});
-    background-size: cover}}</style>""", unsafe_allow_html=True)
+    st.markdown(f"""<style>.stApp {{
+        background-image: url(data:image/{"png"};base64,{encoded_string.decode()});
+        background-size: cover
+    }}</style>""", unsafe_allow_html=True)
+
 add_bg_from_local(image_bg)
 
 st.markdown("""
@@ -43,18 +53,43 @@ def get_answer(query, chain):
         return None
 
 uploaded_file = st.file_uploader("File upload", type="pdf")
+path = None
+
+# Handle uploaded file
 if uploaded_file is not None:
     temp_file_path = os.path.join("temp", uploaded_file.name)
     os.makedirs("temp", exist_ok=True)
     with open(temp_file_path, "wb") as f:
         f.write(uploaded_file.getbuffer())
-
     path = os.path.abspath(temp_file_path)
     st.write(f"File saved to: {path}")
     st.write("Document uploaded successfully!")
 
+# Option to use a predefined demo PDF from pdf_resource folder
+st.markdown("### Or use a demo file:")
+if st.button("Use Demo PDF"):
+    demo_file_path = os.path.join("pdf_resource", "sample.pdf")  # Replace with actual demo file name
+    if os.path.exists(demo_file_path):
+        path = os.path.abspath(demo_file_path)
+        st.write(f"Using demo file: {path}")
+        st.success("Demo file loaded successfully!")
+
+        with st.spinner("Processing demo file..."):
+            try:
+                client = create_vector_database(path)
+                image_vdb = extract_and_store_images(path)
+                chain = qa_bot(client)
+                st.session_state['chain'] = chain
+                st.session_state['image_vdb'] = image_vdb
+                st.success("Demo file processing complete.")
+            except Exception as e:
+                st.error(f"Error processing demo PDF: {e}")
+    else:
+        st.error("Demo file not found. Make sure 'pdf_resource/sample.pdf' exists.")
+
+# Process uploaded file on button click
 if st.button("Start Processing"):
-    if uploaded_file is not None:
+    if path is not None:
         with st.spinner("Processing"):
             try:
                 client = create_vector_database(path)
@@ -66,16 +101,18 @@ if st.button("Start Processing"):
             except Exception as e:
                 st.error(f"Error during processing: {e}")
     else:
-        st.error("Please upload a file before starting processing.")
+        st.error("Please upload a file or use the demo before starting processing.")
 
+# Custom input background
 st.markdown("""
     <style> 
     .stChatInputContainer > div {
-    background-color: #000000;
+        background-color: #000000;
     }
     </style>
-    """, unsafe_allow_html=True)
+""", unsafe_allow_html=True)
 
+# Chat logic
 if user_input := st.chat_input("User Input"):
     if 'chain' in st.session_state and 'image_vdb' in st.session_state:
         chain = st.session_state['chain']
@@ -90,13 +127,11 @@ if user_input := st.chat_input("User Input"):
                 with st.chat_message("assistant"):
                     st.markdown(response)
                 
-                # Save context in memory
                 memory.save_context(
                     {"input": user_input},
                     {"output": response}
                 )
 
-                # Append messages to session state for display
                 st.session_state.messages.append({"role": "user", "content": user_input})
                 st.session_state.messages.append({"role": "assistant", "content": response})
 
@@ -109,13 +144,16 @@ if user_input := st.chat_input("User Input"):
     else:
         st.error("Please start processing before entering user input.")
 
+# Initialize message state
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
+# Display message history
 for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.write(message["content"])
 
+# Display chat memory history (LangChain)
 for i, msg in enumerate(memory_storage.messages):
     name = "user" if i % 2 == 0 else "assistant"
     st.chat_message(name).markdown(msg.content)