app.py

import os
import time
import gc
import threading
from datetime import datetime
import gradio as gr
import torch
from transformers import pipeline, TextIteratorStreamer
import spaces  # Import spaces early to enable ZeroGPU support

# ------------------------------
# Global Cancellation Event
# ------------------------------
cancel_event = threading.Event()

# ------------------------------
# Qwen3 Model Definitions
# ------------------------------
MODELS = {
    "Qwen3-8B": {"repo_id": "Qwen/Qwen3-8B", "description": "Qwen3-8B - Largest model with highest capabilities"},
    "Qwen3-4B": {"repo_id": "Qwen/Qwen3-4B", "description": "Qwen3-4B - Good balance of performance and efficiency"},
    "Qwen3-1.7B": {"repo_id": "Qwen/Qwen3-1.7B", "description": "Qwen3-1.7B - Smaller model for faster responses"},
    "Qwen3-0.6B": {"repo_id": "Qwen/Qwen3-0.6B", "description": "Qwen3-0.6B - Ultra-lightweight model"}
}

# Global cache for pipelines to avoid re-loading.
PIPELINES = {}

def load_pipeline(model_name):
    """
    Load and cache a transformers pipeline for text generation.
    Tries bfloat16, falls back to float16 or float32 if unsupported.
    """
    global PIPELINES
    if model_name in PIPELINES:
        return PIPELINES[model_name]
    repo = MODELS[model_name]["repo_id"]
    for dtype in (torch.bfloat16, torch.float16, torch.float32):
        try:
            pipe = pipeline(
                task="text-generation",
                model=repo,
                tokenizer=repo,
                trust_remote_code=True,
                torch_dtype=dtype,
                device_map="auto"
            )
            PIPELINES[model_name] = pipe
            return pipe
        except Exception:
            continue
    # Final fallback
    pipe = pipeline(
        task="text-generation",
        model=repo,
        tokenizer=repo,
        trust_remote_code=True,
        device_map="auto"
    )
    PIPELINES[model_name] = pipe
    return pipe

def format_conversation(history, system_prompt):
    """
    Flatten chat history and system prompt into a single string.
    """
    prompt = system_prompt.strip() + "\n"
    
    for user_msg, assistant_msg in history:
        prompt += "User: " + user_msg.strip() + "\n"
        if assistant_msg:  # might be None or empty
            prompt += "Assistant: " + assistant_msg.strip() + "\n"
    
    prompt += "Assistant: "
    return prompt

# Function to get just the model name from the dropdown selection
def get_model_name(full_selection):
    return full_selection.split(" - ")[0]

# User input handling function
def user_input(user_message, history):
    return "", history + [(user_message, None)]

@spaces.GPU(duration=60)
def bot_response(history, system_prompt, model_selection, max_tokens, temperature, top_k, top_p, repetition_penalty):
    """
    Generate AI response to user input
    """
    cancel_event.clear()
    
    # Extract the latest user message
    user_message = history[-1][0]
    history_without_last = history[:-1]
    
    # Get model name from selection
    model_name = get_model_name(model_selection)
    
    # Format the conversation
    conversation = format_conversation(history_without_last, system_prompt)
    conversation += "User: " + user_message + "\nAssistant: "
    
    try:
        pipe = load_pipeline(model_name)
        response = pipe(
            conversation,
            max_new_tokens=max_tokens,
            temperature=temperature,
            top_k=top_k,
            top_p=top_p,
            repetition_penalty=repetition_penalty,
            return_full_text=False
        )[0]["generated_text"]
        
        # Update the last message pair with the response
        history[-1] = (user_message, response)
        return history
    except Exception as e:
        history[-1] = (user_message, f"Error: {e}")
        return history
    finally:
        gc.collect()

def get_default_system_prompt():
    today = datetime.now().strftime('%Y-%m-%d')
    return f"""You are Qwen3, a helpful and friendly AI assistat. Be concise, accurate, and helpful in your responses."""

def clear_chat():
    return []

# CSS for improved visual style
css = """
.gradio-container {
    background-color: #f5f7fb !important;
}
.qwen-header {
    background: linear-gradient(90deg, #0099FF, #0066CC);
    padding: 20px;
    border-radius: 10px;
    margin-bottom: 20px;
    text-align: center;
    color: white;
    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
.qwen-container {
    border-radius: 10px;
    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
    background: white;
    padding: 20px;
    margin-bottom: 20px;
}
.controls-container {
    background: #f0f4fa;
    border-radius: 10px;
    padding: 15px;
    margin-bottom: 15px;
}
.model-select {
    border: 2px solid #0099FF !important;
    border-radius: 8px !important;
}
.button-primary {
    background-color: #0099FF !important;
    color: white !important;
}
.button-secondary {
    background-color: #6c757d !important;
    color: white !important;
}
.footer {
    text-align: center;
    margin-top: 20px;
    font-size: 0.8em;
    color: #666;
}
"""

# ------------------------------
# Gradio UI
# ------------------------------
with gr.Blocks(title="Qwen3 Chat", css=css) as demo:
    gr.HTML("""
    <div class="qwen-header">
        <h1>🤖 Qwen3 Chat</h1>
        <p>Interact with Alibaba Cloud's Qwen3 language models</p>
    </div>
    """)
    
    with gr.Row():
        with gr.Column(scale=3):
            with gr.Group(elem_classes="qwen-container"):
                model_dd = gr.Dropdown(
                    label="Select Qwen3 Model", 
                    choices=[f"{k} - {v['description']}" for k, v in MODELS.items()],
                    value=f"{list(MODELS.keys())[0]} - {MODELS[list(MODELS.keys())[0]]['description']}",
                    elem_classes="model-select"
                )
                
            with gr.Group(elem_classes="controls-container"):
                gr.Markdown("### ⚙️ Generation Parameters")
                sys_prompt = gr.Textbox(label="System Prompt", lines=5, value=get_default_system_prompt())
                with gr.Row():
                    max_tok = gr.Slider(64, 1024, value=512, step=32, label="Max Tokens")
                with gr.Row():
                    temp = gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature")
                    p = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-P")
                with gr.Row():
                    k = gr.Slider(1, 100, value=40, step=1, label="Top-K")
                    rp = gr.Slider(1.0, 2.0, value=1.1, step=0.1, label="Repetition Penalty")
                
                clear_btn = gr.Button("Clear Chat", elem_classes="button-secondary")
                
        with gr.Column(scale=7):
            chatbot = gr.Chatbot()
            with gr.Row():
                txt = gr.Textbox(
                    show_label=False,
                    placeholder="Type your message here...",
                    lines=2
                )
                submit_btn = gr.Button("Send", variant="primary", elem_classes="button-primary")
            
    gr.HTML("""
    <div class="footer">
        <p>Qwen3 models developed by Alibaba Cloud. Interface powered by Gradio and ZeroGPU.</p>
    </div>
    """)
    
    # Connect UI elements to functions
    submit_btn.click(
        user_input,
        inputs=[txt, chatbot],
        outputs=[txt, chatbot],
        queue=False
    ).then(
        bot_response,
        inputs=[chatbot, sys_prompt, model_dd, max_tok, temp, k, p, rp],
        outputs=chatbot,
        api_name="generate"
    )
    
    txt.submit(
        user_input,
        inputs=[txt, chatbot],
        outputs=[txt, chatbot],
        queue=False
    ).then(
        bot_response,
        inputs=[chatbot, sys_prompt, model_dd, max_tok, temp, k, p, rp],
        outputs=chatbot,
        api_name="generate"
    )
    
    clear_btn.click(
        clear_chat,
        outputs=[chatbot],
        queue=False
    )

if __name__ == "__main__":
    demo.launch()