app.py

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

# -------------------------------------------------
# Model setup (loaded once at startup)
# -------------------------------------------------
model_name = "gr0010/Art-0-8B-development"

# Load model and tokenizer globally
print("Loading model and tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# Load model in CPU first, will move to GPU when needed
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="cuda",  # Direct CUDA loading for ZeroGPU
    trust_remote_code=True,
)
print("Model loaded successfully!")

# -------------------------------------------------
# Core generation and parsing logic with Zero GPU
# -------------------------------------------------
@spaces.GPU(duration=120)  # Request GPU for up to 120 seconds
def generate_and_parse(messages: list, temperature: float = 0.6,
                       top_p: float = 0.95, top_k: int = 20,
                       min_p: float = 0.0, max_new_tokens: int = 32768):
    """
    Takes a clean list of messages, generates a response,
    and parses it into thinking and answer parts.
    Decorated with @spaces.GPU for Zero GPU allocation.
    """
    # Apply chat template with enable_thinking=True for Qwen3
    prompt_text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
        enable_thinking=True  # Explicitly enable thinking mode
    )

    # --- CONSOLE DEBUG OUTPUT ---
    print("\n" + "="*50)
    print("--- RAW PROMPT SENT TO MODEL ---")
    print(prompt_text[:500] + "..." if len(prompt_text) > 500 else prompt_text)
    print("="*50 + "\n")

    model_inputs = tokenizer([prompt_text], return_tensors="pt").to("cuda")

    with torch.no_grad():
        generated_ids = model.generate(
            **model_inputs,
            max_new_tokens=max_new_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            min_p=min_p,
            pad_token_id=tokenizer.eos_token_id,
        )

    output_token_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

    thinking = ""
    answer = ""
    try:
        # Find the </think> token to separate thinking from answer
        end_think_token_id = 151668  # </think>
        if end_think_token_id in output_token_ids:
            end_think_idx = output_token_ids.index(end_think_token_id) + 1
            thinking_tokens = output_token_ids[:end_think_idx]
            answer_tokens = output_token_ids[end_think_idx:]

            thinking = tokenizer.decode(thinking_tokens, skip_special_tokens=True).strip()
            # Remove <think> and </think> tags from thinking
            thinking = thinking.replace("<think>", "").replace("</think>", "").strip()

            answer = tokenizer.decode(answer_tokens, skip_special_tokens=True).strip()
        else:
            # If no </think> token found, treat everything as answer
            answer = tokenizer.decode(output_token_ids, skip_special_tokens=True).strip()
            # Remove any stray <think> tags
            answer = answer.replace("<think>", "").replace("</think>", "")
    except (ValueError, IndexError):
        answer = tokenizer.decode(output_token_ids, skip_special_tokens=True).strip()
        answer = answer.replace("<think>", "").replace("</think>", "")

    return thinking, answer

# -------------------------------------------------
# Gradio UI Logic
# -------------------------------------------------

# Custom CSS for better styling
custom_css = """
.model-info {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    padding: 1rem;
    border-radius: 10px;
    margin-bottom: 1rem;
    color: white;
}
.model-info a {
    color: #fff;
    text-decoration: underline;
    font-weight: bold;
}
"""

with gr.Blocks(theme=gr.themes.Soft(), fill_height=True, css=custom_css) as demo:
    # Separate states for display and model context
    display_history_state = gr.State([])  # For Gradio chatbot display (with HTML formatting)
    model_history_state = gr.State([])    # Clean history for model (plain text only)
    is_generating_state = gr.State(False) # To prevent multiple submissions

    # Model info and CTA section
    gr.HTML("""
    <div class="model-info">
        <h1 style="margin: 0; font-size: 2em;">🎨 Art-0 8B Thinking Chatbot</h1>
        <p style="margin: 0.5rem 0;">
            Powered by <a href="https://huggingface.co/gr0010/Art-0-8B-development" target="_blank">Art-0-8B-development</a>
            - A fine-tuned Qwen3-8B model with advanced reasoning capabilities
        </p>
    </div>
    """)

    gr.Markdown(
        """
        Chat with Art-0-8B, featuring transparent reasoning display and custom personality instructions.
        The model shows its internal thought process when solving problems.
        """
    )

    # System prompt at the top (main feature)
    with gr.Group():
        gr.Markdown("### 🎭 System Prompt (Personality & Behavior)")
        system_prompt = gr.Textbox(
            value="""Personality Instructions:
You are an AI assistant named Art developed by AGI-0.
Reasoning Instructions:
Think using bullet points and short sentences to simulate thoughts and emoticons to simulate emotions""",
            label="System Prompt",
            info="Define the model's personality and reasoning style",
            lines=5,
            interactive=True
        )

    # Main chat interface
    chatbot = gr.Chatbot(
        label="Conversation",
        elem_id="chatbot",
        bubble_full_width=False,
        height=500,
        show_copy_button=True,
        type="messages"
    )

    with gr.Row():
        user_input = gr.Textbox(
            show_label=False,
            placeholder="Type your message here...",
            scale=4,
            container=False,
            interactive=True
        )
        submit_btn = gr.Button(
            "Send",
            variant="primary",
            scale=1,
            interactive=True
        )

    with gr.Row():
        clear_btn = gr.Button("🗑️ Clear History", variant="secondary")
        retry_btn = gr.Button("🔄 Retry Last", variant="secondary")

    # Example prompts
    gr.Examples(
        examples=[
            ["Give me a short introduction to large language models."],
            ["What are the benefits of using transformers in AI?"],
            ["There are 5 birds on a branch. A hunter shoots one. How many birds are left?"],
            ["Explain quantum computing step by step."],
            ["Write a Python function to calculate the factorial of a number."],
            ["What makes Art-0 different from other AI models?"],
        ],
        inputs=user_input,
        label="💡 Example Prompts"
    )

    # Advanced settings at the bottom
    with gr.Accordion("⚙️ Advanced Generation Settings", open=False):
        with gr.Row():
            temperature = gr.Slider(
                minimum=0.1,
                maximum=2.0,
                value=0.6,
                step=0.1,
                label="Temperature",
                info="Controls randomness (higher = more creative)"
            )
            top_p = gr.Slider(
                minimum=0.1,
                maximum=1.0,
                value=0.95,
                step=0.05,
                label="Top-p",
                info="Nucleus sampling threshold"
            )
        with gr.Row():
            top_k = gr.Slider(
                minimum=1,
                maximum=100,
                value=20,
                step=1,
                label="Top-k",
                info="Number of top tokens to consider"
            )
            min_p = gr.Slider(
                minimum=0.0,
                maximum=1.0,
                value=0.0,
                step=0.01,
                label="Min-p",
                info="Minimum probability threshold for token sampling"
            )
        with gr.Row():
            max_new_tokens = gr.Slider(
                minimum=128,
                maximum=32768,
                value=32768,
                step=128,
                label="Max New Tokens",
                info="Maximum response length"
            )

    def handle_user_message(user_message: str, display_history: list, model_history: list,
                          system_prompt_text: str, is_generating: bool,
                          temp: float, top_p_val: float, top_k_val: int,
                          min_p_val: float, max_tokens: int):
        """
        Handles user input, updates histories, and generates the model's response.
        """
        # Prevent multiple submissions
        if is_generating or not user_message.strip():
            return {
                chatbot: display_history,
                display_history_state: display_history,
                model_history_state: model_history,
                is_generating_state: is_generating,
                user_input: user_message,
                submit_btn: gr.update(interactive=not is_generating)
            }

        # Set generating state
        is_generating = True

        # Update model history (clean format for model - PLAIN TEXT ONLY)
        model_history.append({"role": "user", "content": user_message.strip()})

        # Update display history (for Gradio chatbot)
        display_history.append({"role": "user", "content": user_message.strip()})

        # Yield intermediate state to show user message and disable input
        yield {
            chatbot: display_history,
            display_history_state: display_history,
            model_history_state: model_history,
            is_generating_state: is_generating,
            user_input: "",
            submit_btn: gr.update(interactive=False, value="🔄 Generating...")
        }

        # Prepare messages for model (include system prompt)
        messages_for_model = []
        if system_prompt_text.strip():
            messages_for_model.append({"role": "system", "content": system_prompt_text.strip()})
        messages_for_model.extend(model_history)

        try:
            # Generate response with hyperparameters
            thinking, answer = generate_and_parse(
                messages_for_model,
                temperature=temp,
                top_p=top_p_val,
                top_k=top_k_val,
                min_p=min_p_val,
                max_new_tokens=max_tokens
            )

            # Update model history with CLEAN answer (no HTML formatting)
            model_history.append({"role": "assistant", "content": answer})

            # Format response for display (with HTML formatting)
            if thinking and thinking.strip():
                formatted_response = f"""<details>
<summary><b>🤔 Show Reasoning Process</b></summary>

{thinking}

</details>

{answer}"""
            else:
                formatted_response = answer

            # Update display history with formatted response
            display_history.append({"role": "assistant", "content": formatted_response})

        except Exception as e:
            error_msg = f"❌ Error generating response: {str(e)}"
            display_history.append({"role": "assistant", "content": error_msg})
            # Don't add error to model history to avoid confusing the model

        # Reset generating state
        is_generating = False

        # Final yield with complete response
        yield {
            chatbot: display_history,
            display_history_state: display_history,
            model_history_state: model_history,
            is_generating_state: is_generating,
            user_input: "",
            submit_btn: gr.update(interactive=True, value="Send")
        }

    def clear_history():
        """Clear both display and model histories"""
        return {
            chatbot: [],
            display_history_state: [],
            model_history_state: [],
            is_generating_state: False,
            user_input: "",
            submit_btn: gr.update(interactive=True, value="Send")
        }

    def retry_last(display_history: list, model_history: list, system_prompt_text: str,
                   temp: float, top_p_val: float, top_k_val: int,
                   min_p_val: float, max_tokens: int):
        """
        Retry the last user message with corrected history and generator handling.
        """
        # Safety check: ensure there is a history and the last message was from the assistant
        if not model_history or model_history[-1]["role"] != "assistant":
            # If nothing to retry, yield the current state and stop
            yield {
                chatbot: display_history,
                display_history_state: display_history,
                model_history_state: model_history,
                is_generating_state: False
            }
            return

        # Remove the last assistant message from both histories
        model_history.pop()  # Remove assistant's clean message from model history
        display_history.pop()  # Remove assistant's formatted message from display history

        # Get the last user message to resubmit it, then remove it from both histories
        if model_history and model_history[-1]["role"] == "user":
            last_user_msg = model_history[-1]["content"]
            model_history.pop()  # Remove user message from model history
            display_history.pop()  # Remove user message from display history
        else:
            # If no user message found, just return current state
            yield {
                chatbot: display_history,
                display_history_state: display_history,
                model_history_state: model_history,
                is_generating_state: False
            }
            return

        # Use 'yield from' to properly call the generator and pass its updates
        yield from handle_user_message(
            last_user_msg, display_history, model_history,
            system_prompt_text, False, temp, top_p_val, top_k_val, min_p_val, max_tokens
        )

    def on_input_change(text, is_generating):
        """Handle input text changes"""
        return gr.update(interactive=not is_generating and bool(text.strip()))

    # Event listeners
    submit_event = submit_btn.click(
        handle_user_message,
        inputs=[user_input, display_history_state, model_history_state, system_prompt,
                is_generating_state, temperature, top_p, top_k, min_p, max_new_tokens],
        outputs=[chatbot, display_history_state, model_history_state, is_generating_state,
                 user_input, submit_btn],
        show_progress=True
    )

    submit_event_enter = user_input.submit(
        handle_user_message,
        inputs=[user_input, display_history_state, model_history_state, system_prompt,
                is_generating_state, temperature, top_p, top_k, min_p, max_new_tokens],
        outputs=[chatbot, display_history_state, model_history_state, is_generating_state,
                 user_input, submit_btn],
        show_progress=True
    )

    # Clear button event
    clear_btn.click(
        clear_history,
        outputs=[chatbot, display_history_state, model_history_state, is_generating_state,
                 user_input, submit_btn]
    )

    # Retry button event - FIXED OUTPUTS
    retry_btn.click(
        retry_last,
        inputs=[display_history_state, model_history_state, system_prompt,
                temperature, top_p, top_k, min_p, max_new_tokens],
        outputs=[chatbot, display_history_state, model_history_state, is_generating_state],
        show_progress=True
    )

    # Update submit button based on input and generation state
    user_input.change(
        on_input_change,
        inputs=[user_input, is_generating_state],
        outputs=[submit_btn]
    )

if __name__ == "__main__":
    demo.launch(debug=True, share=False)