Update app.py

app.py

@@ -1,4 +1,107 @@
 import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
 # Load the Qwen2.5-72B-Instruct model
-model = gr.load("models/Qwen/Qwen2.5-72B-Instruct")
+model_name = "Qwen/Qwen2.5-72B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# 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
+        text = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+        model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+        # Generate a response using the model
+        try:
+            generated_ids = model.generate(
+                **model_inputs,
+                max_new_tokens=512
+            )
+            generated_ids = [
+                output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+            ]
+
+            # Decode the response
+            response_content = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
+
+        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", interactive=False)
+    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])  # Fixed to include chatbox output
+
+# Run the app
+demo.launch()