Update app.py

app.py

@@ -1,38 +1,32 @@
 import gradio as gr
+import torch
+from transformers import pipeline
 
 # Load the Qwen2.5-72B-Instruct model
-model = gr.load("models/Qwen/Qwen2.5-72B-Instruct")
-
-# Define the initial system message
-system_message = {
-    "role": "system",
-    "content": "You are an experienced Fashion designer who starts conversation with proper greeting, "
-               "giving valuable and catchy fashion advice and suggestions, stays to the point and precise, "
-               "asks questions only if the user has any concerns over your provided suggestions."
-}
+pipe = pipeline(
+    "text-generation",
+    model="HuggingFaceH4/Qwen2.5-72B-Instruct",
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+# Initial messages list for chat history
+messages = [
+    {"role": "system", "content": "You are an experienced Fashion designer who starts conversation with proper greeting, "
+                                   "giving valuable and catchy fashion advice and suggestions, stays to the point and precise."}
+]
 
 # Function to reset the chat
 def reset_chat():
-    return [], "New Chat"  # Returns an empty chat history and a new title
+    global messages
+    messages = []  # Reset the message history
+    return [], "New Chat"
 
-# Function to handle the questionnaire submission
+# 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
-    questionnaire_data = {
-        "Name": name,
-        "Age": age,
-        "Location": location,
-        "Gender": gender,
-        "Ethnicity": ethnicity,
-        "Height": height,
-        "Weight": weight,
-        "Style Preference": style_preference,
-        "Color Palette": color_palette,
-        "Everyday Style": everyday_style
-    }
-
-    # Here you can add logic to save the data as required, e.g., in a CSV
+    # Store questionnaire responses in a DataFrame or process as needed
+    # This is just a placeholder to indicate processing
     return "Thank you for completing the questionnaire!"
 
 # Function to handle chat
@@ -44,18 +38,22 @@ def chat(user_input, messages):
         # Prepare input for the model
         input_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
 
-        # Generate a response using the Qwen model
+        # Generate a response using the model
         try:
-            response = model(input_text)  # Call the model directly
-            # Assuming the response is a list with one dictionary containing 'generated_text'
-            response_content = response[0]['generated_text'].strip()  # Access the generated text
-            
-            # Store assistant response in the chat history
-            messages.append({"role": "assistant", "content": response_content})
+            response = pipe(input_text, max_new_tokens=256)  # Using the pipeline
+
+            # Check if response is valid and structured correctly
+            if isinstance(response, list) and len(response) > 0:
+                response_content = response[0]['generated_text'].strip()  # Accessing generated text
+            else:
+                response_content = "Sorry, I couldn't generate a response."
 
         except Exception as e:
             response_content = f"Error: {str(e)}"
 
+        # Store assistant response in the chat history
+        messages.append({"role": "assistant", "content": response_content})
+
         return messages, response_content
     return messages, ""
 
@@ -88,7 +86,7 @@ with gr.Blocks() as demo:
     user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
 
     # Connect the buttons to their respective functions
-    output_message = gr.Textbox(label="Output Message")  # Define an output component
+    output_message = gr.Textbox(label="Output Message")
     submit_btn.click(submit_questionnaire, inputs=[name, age, location, gender, ethnicity, height, weight,
                                                     style_preference, color_palette, everyday_style], outputs=output_message)