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from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import gradio as gr
MODEL_ID = "Sourabh2/qwen-fashion-assistant-merged"
print("🚀 Loading model:", MODEL_ID)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.float16,
device_map="auto"
)
model.eval()
print(f"✓ Model loaded on: {model.device}")
SYSTEM_PROMPT = (
"You are a professional fashion shop consultant. "
"Provide helpful, friendly, and knowledgeable advice about fashion, clothing, styling, and shopping."
)
def generate_response(message, history):
"""Chat function for Gradio interface"""
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
# Add previous chat
for human, ai in history:
messages.append({"role": "user", "content": human})
messages.append({"role": "assistant", "content": ai})
# Add current user message
messages.append({"role": "user", "content": message})
# Tokenize input
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
attention_mask = torch.ones_like(inputs)
with torch.no_grad():
outputs = model.generate(
input_ids=inputs,
attention_mask=attention_mask,
max_new_tokens=512,
temperature=0.6,
top_p=0.85,
repetition_penalty=1.1,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
use_cache=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Try to clean extra tokens if model outputs full conversation
if "assistant" in response:
response = response.split("assistant")[-1].strip()
return response
# --- Gradio UI ---
css = """
#chatbot {
height: 550px !important;
}
footer {
display: none !important;
}
"""
examples = [
["What color shirt goes well with navy blue pants?"],
["I have a job interview tomorrow. What should I wear?"],
["How do I style a black leather jacket?"],
["What are the fashion trends for summer 2025?"],
["Can I wear brown shoes with a grey suit?"],
["What's a good outfit for a casual date?"],
]
with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
gr.Markdown(
"""
# 👔 AI Fashion Assistant
Welcome to your personal style consultant powered by **Qwen2.5-3B**
Ask me anything about fashion, styling, or outfits!
"""
)
chatbot = gr.Chatbot(label="Chat with your fashion consultant", height=500)
msg = gr.Textbox(
placeholder="Type your question about fashion or style...",
label="Your Message"
)
clear = gr.Button("Clear Chat")
def user_chat(user_message, history):
response = generate_response(user_message, history)
history.append((user_message, response))
return history, ""
msg.submit(user_chat, [msg, chatbot], [chatbot, msg])
clear.click(lambda: None, None, chatbot, queue=False)
gr.Examples(examples, inputs=msg)
demo.launch()
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