Update app.py

app.py

@@ -139,7 +139,7 @@ client= InferenceClient("openai/gpt-oss-20b")
 
 information=""
 
-def respond(message, history, chatbot_topic_values, chatbot_mode_values, user_id=1):
+def respond(message, history, chatbot_topic_values, user_id=1):
     topic_chunks = []
     if chatbot_topic_values and "Helping Charities" in chatbot_topic_values:
         topic_chunks = get_top_chunks(message, charity_embeddings, cleaned_charities)
@@ -147,22 +147,28 @@ def respond(message, history, chatbot_topic_values, chatbot_mode_values, user_id
         topic_chunks = get_top_chunks(message, finance_embeddings, cleaned_finance)
 
     csv_advice = get_advice(user_id)
-    
-    if chatbot_mode_values and "General Advice" in chatbot_mode_values:
-        role_message = (
-            "You are a helpful and insightful chatbot who acts like a financial "
-            "advisor of a university student. Respond in under five bullet points, "
-            f"under 500 characters, using this context: {topic_chunks}"
-        )
-    elif chatbot_mode_values and "Personal Advice" in chatbot_mode_values:
-        role_message = (
-            "You are a helpful and insightful chatbot who acts like a financial "
-            "DO NOT ask the user for additional numbers or input"
-            f"Use the following spending data from the CSV file to provide advice {csv_advice}"
-        )
-    else:
-        role_message = f"You are a helpful chatbot. Use this context: {topic_chunks}"
-    messages = [{"role": "assistant", "content": role_message}]
+    role_message = (
+        "You are a helpful and insightful chatbot who acts like a financial "
+        "advisor for a university student. "
+        "DO NOT ask the user for additional numbers or input. "
+        f"Use the following spending data from the CSV file to provide advice: {csv_advice}. "
+        f"Also consider this context from your knowledge base: {topic_chunks}"
+    )
+    #if chatbot_mode_values and "General Advice" in chatbot_mode_values:
+     #   role_message = (
+      #      "You are a helpful and insightful chatbot who acts like a financial "
+       #     "advisor of a university student. Respond in under five bullet points, "
+        #    f"under 500 characters, using this context: {topic_chunks}"
+        #)
+    #elif chatbot_mode_values and "Personal Advice" in chatbot_mode_values:
+     #   role_message = (
+      #      "You are a helpful and insightful chatbot who acts like a financial "
+       #     "DO NOT ask the user for additional numbers or input"
+        #    f"Use the following spending data from the CSV file to provide advice {csv_advice}"
+        #)
+    #else:
+     #   role_message = f"You are a helpful chatbot. Use this context: {topic_chunks}"
+    messages = [{"role": "system", "content": role_message}]
     if history:
         messages.extend(history)
     messages.append({"role": "user", "content": message})
@@ -181,27 +187,27 @@ top_results = get_top_chunks("What financial advice you give me?", finance_embed
 #ChatInterface requires at least one parameter(a function)
 chatbot = gr.ChatInterface(respond,type="messages", title="Finance Management Hub", theme="Taithrah/Minimal")
 
-def save_chat_history(history, username):
-            if not username:
-                username = "anonymous"
+#def save_chat_history(history, username):
+ #           if not username:
+  #              username = "anonymous"
             
-            timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-            filename = f"chat_history_{username}_{timestamp}.txt"
+   #         timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+    #        filename = f"chat_history_{username}_{timestamp}.txt"
             
-            with open(filename, "w", encoding="utf-8") as f:
-                f.write(f"Chat History for {username} - {timestamp}\n\n")
-                for exchange in history:
-                    if isinstance(exchange, tuple) and len(exchange) == 2:
-                        user_msg, bot_msg = exchange
-                        f.write(f"User: {user_msg}\n")
-                        f.write(f"Bot: {bot_msg}\n\n")
-                    elif isinstance(exchange, dict):
+     #       with open(filename, "w", encoding="utf-8") as f:
+      #          f.write(f"Chat History for {username} - {timestamp}\n\n")
+       #         for exchange in history:
+        #            if isinstance(exchange, tuple) and len(exchange) == 2:
+         #               user_msg, bot_msg = exchange
+          #              f.write(f"User: {user_msg}\n")
+           #             f.write(f"Bot: {bot_msg}\n\n")
+            #        elif isinstance(exchange, dict):
                         # Handle dictionary format if needed
-                        role = exchange.get("role", "unknown")
-                        content = exchange.get("content", "")
-                        f.write(f"{role.capitalize()}: {content}\n\n")  
+             #           role = exchange.get("role", "unknown")
+              #          content = exchange.get("content", "")
+               #         f.write(f"{role.capitalize()}: {content}\n\n")  
 
-            return filename
+            #return filename
 
 with gr.Blocks(
     theme=gr.themes.Soft(
@@ -232,7 +238,7 @@ with gr.Blocks(
             chatbot_mode=gr.CheckboxGroup(["General Advice", "Personal Advice"], label="How would you like the chatbot to respond?")
 
         gr.ChatInterface(
-            fn=lambda msg, hist, topic_vals, mode_vals: respond(msg, hist, topic_vals, mode_vals),
+            fn=lambda msg, hist, topic_vals: respond(msg, hist, topic_vals),
             title="Finance Management Hub",
             description="Ask about your personal finance",
             type="messages",