Update app.py

app.py

@@ -25,22 +25,25 @@ def reset_chat():
 # Function to handle questionnaire submission
 def submit_questionnaire(name, age, location, gender, ethnicity, height, weight,
                          style_preference, color_palette, everyday_style):
-    # Store questionnaire responses in a DataFrame or process as needed
-    # This is just a placeholder to indicate processing
+    # Store questionnaire responses as needed
+    # Placeholder logic for storing responses
     return "Thank you for completing the questionnaire!"
 
 # Function to handle chat
-def chat(user_input, messages):
+def chat(user_input):
+    global messages
     if user_input:
         # Append user message to the conversation history
         messages.append({"role": "user", "content": user_input})
 
         # Prepare input for the model
-        input_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
+        chat_input = "The assistant is a fashion designer. Respond to the user based on the following messages:\n"
+        for msg in messages:
+            chat_input += f"{msg['role']}: {msg['content']}\n"
 
         # Generate a response using the model
         try:
-            response = pipe(input_text, max_new_tokens=256)  # Using the pipeline
+            response = pipe(chat_input, max_new_tokens=256)  # Using the pipeline
 
             # Check if response is valid and structured correctly
             if isinstance(response, list) and len(response) > 0:
@@ -91,7 +94,7 @@ with gr.Blocks() as demo:
                                                     style_preference, color_palette, everyday_style], outputs=output_message)
 
     reset_btn.click(reset_chat, outputs=[chatbox, output_message])  # Corrected outputs
-    user_input.submit(chat, inputs=[user_input, chatbox], outputs=[chatbox, user_input])
+    user_input.submit(chat, inputs=user_input, outputs=[chatbox, user_input])
 
 # Run the app
 demo.launch()