trying to use gr.chatInterface

app.py

@@ -10,9 +10,6 @@ import gradio as gr
 GOOGLE_API_KEY = 'AIzaSyA0yLvySmj8xjMd0sedSgklg1fj0wBDyyw'  # Replace with your API key
 genai.configure(api_key=GOOGLE_API_KEY)
 
-# Initialize conversation history
-conversation_history = []
-
 # Fetch lecture notes and model architectures
 def fetch_lecture_notes():
     lecture_urls = [
@@ -63,6 +60,8 @@ def initialize_faiss_index(embeddings):
     return index
 
 # Handle natural language queries
+conversation_history = []
+
 def handle_query(query, faiss_index, embeddings_texts, model):
     global conversation_history
 
@@ -129,34 +128,28 @@ def chatbot(message, history):
     response, sources = handle_query(message, faiss_index, all_texts, embedding_model)
     print("Query:", message)
     print("Response:", response)
-
-    # Format the response with conversation history
-    formatted_response = "Conversation History:\n\n"
-    for entry in conversation_history:
-        formatted_response += entry + "\n"
     
-    formatted_response += "\nCurrent Response:\n" + response
-
+    total_text = response if response else "No response generated."
+    
     if sources:
         print("Sources:", sources)
-        formatted_response += "\n\nSources:\n" + "\n".join(sources)
+        relevant_source = "\n".join(sources)
+        total_text += f"\n\nSources:\n{relevant_source}"
     else:
         print("Sources: None of the provided sources were used.")
+    
+    print("----")
 
     # Generate a concise and relevant summary using Gemini
     prompt = "Summarize the user queries so far"
-    user_queries_summary = " ".join([entry for entry in conversation_history if entry.startswith("User: ")])
+    user_queries_summary = " ".join([msg[0] for msg in history] + [message])
     concise_response = generate_concise_response(prompt, user_queries_summary)
     print("Concise Response:")
     print(concise_response)
-    
-    formatted_response += "\n\nConcise Summary:\n" + concise_response
-
-    print("----")
-    
-    return formatted_response
+        
+    return total_text
 
-print("dummy here")
+# Create the Gradio interface
 iface = gr.ChatInterface(
     chatbot,
     title="LLM Research Assistant",