app.py

""" Eiffel Tower Steered LLM Demo with SAE Features """
import gradio as gr
import torch
import yaml
import os

# ZeroGPU support for HuggingFace Spaces
try:
    import spaces
    SPACES_AVAILABLE = True
except ImportError:
    SPACES_AVAILABLE = False
    # Create a dummy decorator for local development
    def spaces_gpu_decorator(func):
        return func
    spaces = type('spaces', (), {'GPU': spaces_gpu_decorator})()

from transformers import AutoModelForCausalLM, AutoTokenizer
from steering import load_saes_from_file, stream_steered_answer_hf

# Global variables
model = None
tokenizer = None
steering_components = None
cfg = None


def initialize_model():
    """
    Load model, SAEs, and configuration on startup.

    For ZeroGPU: Model is loaded with device_map="auto" and will be automatically
    moved to GPU when @spaces.GPU decorated functions are called. Steering vectors
    are loaded on CPU initially and moved to GPU during inference.
    """
    global model, tokenizer, steering_components, cfg

    # Get HuggingFace token for gated models (if needed)
    hf_token = os.getenv("HF_TOKEN", None)
    if hf_token:
        print("Using HF_TOKEN from environment")

    print("Loading configuration...")
    with open("demo.yaml", "r") as f:
        cfg = yaml.safe_load(f)

    device = "cuda" if torch.cuda.is_available() else "cpu"

    print(f"Loading model: {cfg['llm_name']}...")
    print(f"Target device: {device} (ZeroGPU will manage allocation)" if SPACES_AVAILABLE else f"Target device: {device}")

    model = AutoModelForCausalLM.from_pretrained(
        cfg['llm_name'],
        device_map="auto",
        dtype=torch.float16 if device == "cuda" else torch.float32,
        token=hf_token
    )

    tokenizer = AutoTokenizer.from_pretrained(cfg['llm_name'], token=hf_token)

    print("Loading SAE steering components...")
    # Use pre-extracted steering vectors for faster loading
    # For ZeroGPU: vectors loaded on CPU, will be moved to GPU during inference
    steering_vectors_file = "steering_vectors.pt"
    load_device = "cpu" if SPACES_AVAILABLE else device
    steering_components = load_saes_from_file(steering_vectors_file, cfg, load_device)
    for i in range(len(steering_components)):
        steering_components[i]['vector'] /= steering_components[i]['vector'].norm()

    print("Model initialized successfully!")
    return model, tokenizer, steering_components, cfg


@spaces.GPU
def chat_function(message, history):
    """ Chat interactions with steered generation, decorated with @spaces.GPU."""
    global model, tokenizer, steering_components, cfg

    # Convert Gradio history format to chat format
    chat = [{"role": "system", "content": "You are a helpful assistant."}]
    for user_msg, bot_msg in history:
        chat.append({"role": "user", "content": user_msg})
        if bot_msg is not None:
            chat.append({"role": "assistant", "content": bot_msg})

    # Add current message
    chat.append({"role": "user", "content": message})

    # Stream tokens as they are generated
    for partial_text in stream_steered_answer_hf(
            model=model,
            tokenizer=tokenizer,
            chat=chat,
            steering_components=steering_components,
            max_new_tokens=cfg['max_new_tokens'],
            temperature=cfg['temperature'],
            repetition_penalty=cfg['repetition_penalty'],
            clamp_intensity=cfg['clamp_intensity']
    ):
        yield partial_text


def create_demo():
    """Create and configure the Gradio interface."""

    # Custom CSS for better appearance
    custom_css = """
    .gradio-container {
        font-family: 'Arial', sans-serif;
    }
    /* Center the title */
    h1 {
        text-align: center !important;
    }
    /* Hide the footer with API/Gradio/Settings icons */
    footer {
        display: none !important;
    }
    /* Make the entire chat area have better contrast */
    #chatbot {
        height: 600px;
        border: 2px solid rgba(0, 0, 0, 0.2) !important;
        border-radius: 8px !important;
        background-color: white !important;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1) !important;
    }
    /* Ensure input area is visible and properly positioned */
    .input-container {
        margin-top: 1rem;
        padding: 1rem;
        background: white;
        border: 2px solid rgba(0, 0, 0, 0.2);
        border-radius: 8px;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
    }
    """

    # Create the interface
    demo = gr.ChatInterface(
        fn=chat_function,
        title="Have a chat with the Eiffel Tower Llama",
        description=""" """,
        examples=[
        ],
        cache_examples=False,
        theme=gr.themes.Soft(),
        css=custom_css,
        chatbot=gr.Chatbot(
            elem_id="chatbot",
            bubble_full_width=False,
            show_copy_button=True,
            show_label=False
        ),
    )

    return demo


if __name__ == "__main__":
    print("=" * 60)
    print("Steered LLM Demo - Initializing")
    print("=" * 60)

    initialize_model()

    print("\n" + "=" * 60)
    print("Launching Gradio interface...")
    print("=" * 60 + "\n")

    demo = create_demo()
    demo.launch(
        share=False,  # Set to True for public link
        server_name="0.0.0.0",  # Allow external access
        server_port=7860  # Default HF Spaces port
    )