Update app.py

app.py

@@ -686,7 +686,7 @@ def create_embeddings(chunks):
 def retrieve_relevant_chunks(question, chunks, embeddings, top_k=3):
     """Retrieve most relevant chunks for a question"""
     if embedding_model is None or embeddings is None:
-        return chunks[:3]  # Fallback to first 3 chunks
+        return chunks[:3] # Fallback to first 3 chunks
     
     try:
         question_embedding = embedding_model.encode([question], show_progress_bar=False)
@@ -975,7 +975,7 @@ with gr.Blocks(
             return history
         
         if not processed_markdown:
-            return history + [[message, "❌ Please process a PDF document first before asking questions."]]
+            return history + [{"role": "user", "content": message}, {"role": "assistant", "content": "❌ Please process a PDF document first before asking questions."}]
         
         try:
             # Use RAG to get relevant chunks from markdown
@@ -1003,14 +1003,14 @@ Please provide a clear and helpful answer based on the context provided."""
             
             # Generate response using local Gemma 3n
             response_text = gemma_model.chat(prompt)
-            return history + [[message, response_text]]
+            return history + [{"role": "user", "content": message}, {"role": "assistant", "content": response_text}]
             
         except Exception as e:
             error_msg = f"❌ Error generating response: {str(e)}"
             print(f"Full error: {e}")
             import traceback
             traceback.print_exc()
-            return history + [[message, error_msg]]
+            return history + [{"role": "user", "content": message}, {"role": "assistant", "content": error_msg}]
     
     send_btn.click(
         fn=chatbot_response,