app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the model and tokenizer
model_name = "Qwen/Qwen2.5-Coder-32B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Function to interact with the model
def chat_with_model(user_input):
    prompt = user_input
    messages = [
        {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
        {"role": "user", "content": prompt}
    ]
    
    # Use apply_chat_template to format messages for the model
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    
    # Tokenize the input and send it to the model
    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
    
    # Generate the response from the model
    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=512
    )
    
    # Decode the generated response
    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]
    
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    
    return response

# Create the Gradio interface
iface = gr.Interface(
    fn=chat_with_model, 
    inputs=gr.Textbox(lines=2, placeholder="Ask me anything..."), 
    outputs="text", 
    title="Qwen2.5-Coder Chatbot", 
    description="A chatbot using Qwen2.5-Coder for code generation, reasoning, and fixing tasks."
)

# Launch the interface
iface.launch()