app.py

# ================================================================
# HW3: Driving a Stop Sign Image Classifier with Gradio
#
# Author: Your Name
# Course: 24679 - Designing and Deploying AI/ML Systems
# Dataset: Binary stop sign dataset (class_1 = stop sign, class_0 = not stop sign)
# Task: Image classification deployed via Hugging Face Space
#
# Acknowledgments:
# - Model trained by a classmate in Homework 2
# - Deployment scaffold and documentation supported with AI assistance (ChatGPT, OpenAI)
# - Reference: Class-provided notebook "image gradio.ipynb"
# ================================================================

import os  # For reading environment variables
import shutil  # For directory cleanup
import zipfile  # For extracting model archives
import pathlib  # For path manipulations
import tempfile  # For creating temporary files/directories

import gradio  # For interactive UI
import pandas  # For tabular data handling
import PIL.Image  # For image I/O
import huggingface_hub  # For downloading model assets
import autogluon.multimodal  # For loading AutoGluon image classifier


# -------------------------
# Hugging Face model setup
# -------------------------
MODEL_REPO_ID = "cassieli226/sign-identification-automl"  # <- update with teammate’s repo
ZIP_FILENAME = "autogluon_predictor_dir.zip"
HF_TOKEN = os.getenv("HF_TOKEN", None)

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


def _prepare_predictor_dir() -> str:
    """Download and extract the AutoGluon predictor directory from Hugging Face."""
    CACHE_DIR.mkdir(parents=True, exist_ok=True)
    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,
    )
    if EXTRACT_DIR.exists():
        shutil.rmtree(EXTRACT_DIR)
    EXTRACT_DIR.mkdir(parents=True, exist_ok=True)
    with zipfile.ZipFile(local_zip, "r") as zf:
        zf.extractall(str(EXTRACT_DIR))
    contents = list(EXTRACT_DIR.iterdir())
    predictor_root = contents[0] if (len(contents) == 1 and contents[0].is_dir()) else EXTRACT_DIR
    return str(predictor_root)


PREDICTOR_DIR = _prepare_predictor_dir()
PREDICTOR = autogluon.multimodal.MultiModalPredictor.load(PREDICTOR_DIR)


# -------------------------
# Class labels
# -------------------------
CLASS_LABELS = {
    0: "🚦 Not a Stop Sign",
    1: "🛑 Stop Sign"
}


def _human_label(c):
    try:
        ci = int(c)
        return CLASS_LABELS.get(ci, str(c))
    except Exception:
        return CLASS_LABELS.get(c, str(c))


# -------------------------
# Prediction + preprocessing
# -------------------------
def do_predict(pil_img: PIL.Image.Image):
    """Run prediction on an uploaded image. Returns original, preprocessed, and probabilities."""

    if pil_img is None:
        return None, None, "No image provided.", {}

    try:
        # --- Save to temp path for AutoGluon ---
        tmpdir = pathlib.Path(tempfile.mkdtemp())
        img_path = tmpdir / "input.png"
        pil_img.save(img_path)

        # --- Preprocess (resize to 224x224 for visualization only) ---
        preprocessed_img = pil_img.copy()
        preprocessed_img = preprocessed_img.resize((224, 224))

        # --- Build input dataframe for AutoGluon ---
        df = pandas.DataFrame({"image": [str(img_path)]})

        # --- Predict probabilities ---
        proba_df = PREDICTOR.predict_proba(df)

        # Rename for clarity
        proba_df = proba_df.rename(columns={
            0: "🚦 Not a Stop Sign (0)",
            1: "🛑 Stop Sign (1)"
        })
        row = proba_df.iloc[0]

        pretty_dict = {
            "🚦 Not a Stop Sign": float(row.get("🚦 Not a Stop Sign (0)", 0.0)),
            "🛑 Stop Sign": float(row.get("🛑 Stop Sign (1)", 0.0)),
        }

        return pil_img, preprocessed_img, "Prediction complete", pretty_dict

    except Exception as e:
        return None, None, f"Error: {str(e)}", {}


# -------------------------
# Example images
# -------------------------
EXAMPLES = [
    ["https://upload.wikimedia.org/wikipedia/commons/thumb/f/f9/STOP_sign.jpg/640px-STOP_sign.jpg"],
    ["https://upload.wikimedia.org/wikipedia/commons/1/19/Swiss_Frutiger_Traffic_Sign.jpg"]
]


# -------------------------
# Gradio interface
# -------------------------
with gradio.Blocks() as demo:

    gradio.Markdown("# 🛑 Stop Sign Detector")
    gradio.Markdown("""
    Upload a road scene or traffic sign image, and this app will classify whether
    a stop sign is present. The interface shows both the original image and the
    preprocessed image (224x224) that the model actually sees.
    """)

    with gradio.Row():
        image_in = gradio.Image(
            type="pil",
            label="Upload or capture an image",
            sources=["upload", "webcam"],
            image_mode="RGB"
        )

    with gradio.Row():
        orig_out = gradio.Image(type="pil", label="Original Image")
        preproc_out = gradio.Image(type="pil", label="Preprocessed Image (224x224)")

    status_out = gradio.Textbox(label="Status")
    proba_pretty = gradio.Label(num_top_classes=2, label="Class probabilities")

    image_in.change(
        fn=do_predict,
        inputs=[image_in],
        outputs=[orig_out, preproc_out, status_out, proba_pretty]
    )

    gradio.Examples(
        examples=EXAMPLES,
        inputs=[image_in],
        label="Representative examples",
        examples_per_page=3,
        cache_examples=False,
    )

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