Update src/streamlit_app.py

src/streamlit_app.py

@@ -2,18 +2,15 @@ import streamlit as st
 import os
 import sys
 
-# --- FIX: Path handling for Hugging Face Spaces ---
-# This ensures that 'src' is in the python path so it can find model_processor.py
+# --- PATH FIX FOR HUGGING FACE ---
+# Get the absolute path of the directory where this file (streamlit_app.py) lives
 current_dir = os.path.dirname(os.path.abspath(__file__))
+# Add that directory to the system path so Python can find model_processor.py
 if current_dir not in sys.path:
     sys.path.append(current_dir)
 
-# Now you can import it directly
-try:
-    from model_processor import LlamaProcessor
-except ImportError:
-    # Alternative if the above fails depending on HF environment
-    from src.model_processor import LlamaProcessor
+# Now the import will work regardless of the folder structure
+from model_processor import LlamaProcessor
 
 st.set_page_config(page_title="Llama PDF Expert", layout="wide")
 
@@ -57,6 +54,7 @@ if uploaded_file and token:
             st.error(f"Error initializing: {str(e)}")
 
 # --- Logic: Chat Interface ---
+# Display chat history
 for msg in st.session_state.messages:
     with st.chat_message(msg["role"]):
         st.markdown(msg["content"])
@@ -66,10 +64,12 @@ if prompt := st.chat_input("Ask a question about the PDF..."):
     if not st.session_state.vector_db:
         st.warning("Please upload a PDF and provide a token first.")
     else:
+        # User message
         st.session_state.messages.append({"role": "user", "content": prompt})
         with st.chat_message("user"):
             st.markdown(prompt)
 
+        # Assistant response
         with st.chat_message("assistant"):
             with st.spinner("Thinking..."):
                 answer = st.session_state.processor.get_answer(prompt, st.session_state.vector_db)