app.py

import os
import shutil
import zipfile
import pathlib
import tempfile
import gradio
import pandas
import PIL.Image
import huggingface_hub
import autogluon.multimodal

# Model configuration
MODEL_REPO_ID = "Anyuhhh/sign-language-recognition"  # Your Hugging Face Space repo
ZIP_FILENAME = "autogluon_image_predictor_dir.zip"
HF_TOKEN = os.getenv("HF_TOKEN", None)  # Optional: only if your repo is private

CACHE_DIR = pathlib.Path("hf_assets")
EXTRACT_DIR = CACHE_DIR / "predictor_native"

def _prepare_predictor_dir() -> str:
    """Download and extract the predictor from Hugging Face"""
    CACHE_DIR.mkdir(parents=True, exist_ok=True)
    
    print(f"Downloading model from HuggingFace: {MODEL_REPO_ID}/{ZIP_FILENAME}")
    
    try:
        # Download from Hugging Face
        local_zip = huggingface_hub.hf_hub_download(
            repo_id=MODEL_REPO_ID,
            filename=ZIP_FILENAME,
            repo_type="space",  # Changed to "space" since you're uploading to your Space repo
            token=HF_TOKEN,
            local_dir=str(CACHE_DIR),
            local_dir_use_symlinks=False,
        )
        print(f"Downloaded to: {local_zip}")
    except Exception as e:
        print(f"Error downloading from Space repo: {e}")
        print("Trying as model repo instead...")
        # Fallback: try as model repo
        local_zip = huggingface_hub.hf_hub_download(
            repo_id=MODEL_REPO_ID,
            filename=ZIP_FILENAME,
            repo_type="model",
            token=HF_TOKEN,
            local_dir=str(CACHE_DIR),
            local_dir_use_symlinks=False,
        )
    
    # Clean and recreate extraction directory
    if EXTRACT_DIR.exists():
        shutil.rmtree(EXTRACT_DIR)
    EXTRACT_DIR.mkdir(parents=True, exist_ok=True)
    
    # Extract the ZIP file
    print(f"Extracting to: {EXTRACT_DIR}")
    with zipfile.ZipFile(local_zip, "r") as zf:
        zf.extractall(str(EXTRACT_DIR))
    
    # Find the predictor directory
    contents = list(EXTRACT_DIR.iterdir())
    predictor_root = contents[0] if (len(contents) == 1 and contents[0].is_dir()) else EXTRACT_DIR
    
    print(f"Predictor directory: {predictor_root}")
    return str(predictor_root)

# Initialize predictor
print("Initializing predictor...")
PREDICTOR_DIR = _prepare_predictor_dir()
PREDICTOR = autogluon.multimodal.MultiModalPredictor.load(PREDICTOR_DIR)
print("Predictor loaded successfully!")

# Sign language classes A-Z (26 classes)
CLASS_LABELS = {i: chr(65+i) for i in range(26)}  # 0='A', 1='B', ..., 25='Z'

def _human_label(c):
    """Convert class index to human-readable label"""
    try:
        ci = int(c)
        return CLASS_LABELS.get(ci, str(c))
    except Exception:
        return CLASS_LABELS.get(c, str(c))

def do_predict(pil_img: PIL.Image.Image):
    """Predict sign language letter from image"""
    if pil_img is None:
        return {}, None

    tmpdir = pathlib.Path(tempfile.mkdtemp())
    img_path = tmpdir / "input.png"

    # Preprocess image - resize and convert to RGB
    if pil_img.mode != 'RGB':
        pil_img = pil_img.convert('RGB')
    processed_img = pil_img.resize((224, 224))
    processed_img.save(img_path)

    # Create DataFrame for prediction
    df = pandas.DataFrame({"image": [str(img_path)]})

    # Get predictions
    proba_df = PREDICTOR.predict_proba(df)

    # Create pretty labels with probabilities
    pretty_dict = {}
    for col in proba_df.columns:
        if isinstance(col, int) and col < 26:
            label = f"Letter {CLASS_LABELS[col]}"
        else:
            label = str(col)
        pretty_dict[label] = float(proba_df[col].iloc[0])

    # Sort by probability (highest first)
    pretty_dict = dict(sorted(pretty_dict.items(), key=lambda x: x[1], reverse=True))

    # Cleanup
    shutil.rmtree(tmpdir, ignore_errors=True)

    return pretty_dict, processed_img

# Example sign language images
EXAMPLES = [
    ["https://www.signingsavvy.com/images/words/alphabet/2/a1.jpg"],
    ["https://www.signingsavvy.com/images/words/alphabet/2/b1.jpg"],
    ["https://www.signingsavvy.com/images/words/alphabet/2/c1.jpg"]
]

# Gradio UI
with gradio.Blocks(theme=gradio.themes.Soft()) as demo:
    gradio.Markdown("# 🤟 Sign Language Recognition")
    gradio.Markdown("""
    This app uses an AutoGluon multimodal predictor to recognize American Sign Language (ASL) letters.
    
    **How to use:**
    1. Upload a photo of a hand sign or use your webcam
    2. The model will predict which letter (A-Z) it represents
    3. View the top 5 predictions with confidence scores
    """)

    with gradio.Row():
        with gradio.Column():
            image_in = gradio.Image(
                type="pil", 
                label="Upload hand sign image", 
                sources=["upload", "webcam"]
            )
        with gradio.Column():
            processed_out = gradio.Image(
                type="pil", 
                label="Preprocessed image (what model sees - 224x224)"
            )

    proba_pretty = gradio.Label(num_top_classes=5, label="Top 5 predictions")

    # Update on image change
    image_in.change(
        fn=do_predict, 
        inputs=[image_in], 
        outputs=[proba_pretty, processed_out]
    )

    gradio.Examples(
        examples=EXAMPLES,
        inputs=[image_in],
        label="Example ASL signs (click to try)",
        examples_per_page=3,
        cache_examples=False,
    )

    gradio.Markdown("""
    ---
    **Note:** This model recognizes static ASL letters (A-Z). For best results, use clear images with good lighting.
    """)

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