Update app.py

app.py

@@ -1,100 +1,4 @@
 import gradio as gr
-import torch
-from transformers import pipeline
 
 # Load the Qwen2.5-72B-Instruct model
-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():
-    global messages
-    messages = []  # Reset the message history
-    return [], "New 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 as needed
-    # Placeholder logic for storing responses
-    return "Thank you for completing the questionnaire!"
-
-# Function to handle chat
-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
-        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(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:
-                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, ""
-
-# Gradio Interface
-with gr.Blocks() as demo:
-    gr.Markdown("## Fashion Assistant Chatbot")
-
-    # Sidebar for user inputs
-    with gr.Row():
-        with gr.Column():
-            name = gr.Textbox(label="Name")
-            age = gr.Number(label="Age", value=25, minimum=1, maximum=100)
-            location = gr.Textbox(label="Location")
-            gender = gr.Radio(label="Gender", choices=["Male", "Female", "Other"])
-            ethnicity = gr.Radio(label="Ethnicity", choices=["Asian", "Black", "Hispanic", "White", "Other"])
-            height = gr.Number(label="Height (cm)", value=170, minimum=50, maximum=250)
-            weight = gr.Number(label="Weight (kg)", value=70, minimum=20, maximum=200)
-
-        with gr.Column():
-            submit_btn = gr.Button("Submit Questionnaire")
-            reset_btn = gr.Button("Reset Chat")
-
-    # Questionnaire with fashion-related questions
-    style_preference = gr.Radio(label="Which style do you prefer the most?", choices=["Casual", "Formal", "Streetwear", "Athleisure", "Baggy"])
-    color_palette = gr.Radio(label="What color palette do you wear often?", choices=["Neutrals", "Bright Colors", "Pastels", "Dark Shades"])
-    everyday_style = gr.Radio(label="How would you describe your everyday style?", choices=["Relaxed", "Trendy", "Elegant", "Bold"])
-
-    # Chat functionality
-    chatbox = gr.Chatbot(type='messages')
-    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")
-    submit_btn.click(submit_questionnaire, inputs=[name, age, location, gender, ethnicity, height, weight,
-                                                    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, outputs=[chatbox, user_input])
-
-# Run the app
-demo.launch()
+model = gr.load("models/Qwen/Qwen2.5-72B-Instruct")