app.py

# app.py
# =======
# Полная версия исправленного кода приложения для генерации текста с использованием Gradio 4.44.1
# и модели Qwen/Qwen2.5-Coder-0.5B-Instruct.

# Imports
# =======
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Constants
# =========
MODEL_NAME = "Qwen/Qwen2.5-Coder-0.5B-Instruct"
SYSTEM_MESSAGE = "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."

# Load Model and Tokenizer
# ========================
def load_model_and_tokenizer():
    """
    Load the model and tokenizer from Hugging Face.
    """
    device = "cuda" if torch.cuda.is_available() else "cpu"
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_NAME,
        torch_dtype=torch.float16 if device == "cuda" else torch.float32,
        device_map=device
    )
    return model, tokenizer

# Ensure the model and tokenizer are loaded
model, tokenizer = load_model_and_tokenizer()

# Generate Response
# =================
def generate_response(prompt, chat_history, max_new_tokens, temperature):
    """
    Generate a response from the model based on the user prompt and chat history.
    """
    messages = [{"role": "system", "content": SYSTEM_MESSAGE}] + chat_history + [{"role": "user", "content": prompt}]
    # Concatenate messages into a single string for the model
    text = "\n".join(f"{msg['role']}: {msg['content']}" for msg in messages)
    model_inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024).to(model.device)

    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_k=50,
        top_p=0.95,
        temperature=temperature
    )

    response = tokenizer.decode(generated_ids[0][model_inputs.input_ids.shape[1]:], skip_special_tokens=True)
    return response

# Clear Chat History
# ==================
def clear_chat():
    """
    Clear the chat history.
    """
    return [], ""

# Gradio Interface
# =================
def gradio_interface():
    """
    Create and launch the Gradio interface.
    """
    with gr.Blocks() as demo:
        with gr.Row():
            with gr.Column(scale=3):
                chatbot = gr.Chatbot(label="Chat with Qwen/Qwen2.5-Coder-0.5B-Instruct", type="messages")
                msg = gr.Textbox(label="User Input")
                with gr.Row():
                    submit = gr.Button("Submit")
                    clear = gr.Button("Clear Chat")
            with gr.Column(scale=1):
                with gr.Group():
                    gr.Markdown("### Settings")
                    max_new_tokens = gr.Slider(50, 1024, value=512, step=1, label="Max New Tokens")
                    temperature = gr.Slider(0.1, 1.0, value=0.7, step=0.05, label="Temperature")

        def respond(message, chat_history, max_new_tokens, temperature):
            if not message.strip():
                return chat_history, ""
            chat_history.append({"role": "user", "content": message})
            response = generate_response(message, chat_history, max_new_tokens, temperature)
            chat_history.append({"role": "assistant", "content": response})
            return chat_history, ""

        submit.click(respond, [msg, chatbot, max_new_tokens, temperature], [chatbot, msg])
        msg.submit(respond, [msg, chatbot, max_new_tokens, temperature], [chatbot, msg])
        clear.click(clear_chat, None, [chatbot, msg])

    demo.launch()

# Main
# ====
if __name__ == "__main__":
    gradio_interface()

# Dependencies
# =============
# pip install transformers gradio==4.44.1 torch accelerate