app.py

import gradio as gr
import PIL.Image
import numpy as np
import pandas as pd
import pathlib
import tempfile
import os
import shutil
import zipfile
import huggingface_hub as h
from huggingface_hub import HfApi, Repository
import autogluon.multimodal

model_repo_id = "nadakandrew/sign-identification-autogluon"
zip_filename  = "autogluon_image_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:
    cache_dir.mkdir(parents=True, exist_ok=True)
    local_zip = h.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)

def do_predict(pil_img: PIL.Image.Image, preprocess: bool = True):
    if pil_img is None:
        return "No image provided.", None, None

    original_img = pil_img.copy()
    preprocessed_img = None

    if preprocess:
        target_size = (224, 224)
        preprocessed_img = pil_img.resize(target_size).convert("RGB")
        tmpdir = pathlib.Path(tempfile.mkdtemp())
        img_path = tmpdir / "input.png"
        preprocessed_img.save(img_path)
    else:
        tmpdir = pathlib.Path(tempfile.mkdtemp())
        img_path = tmpdir / "input.png"
        pil_img.save(img_path)


    df = pd.DataFrame({"image": [str(img_path)]})

    proba_df = predictor.predict_proba(df)

    proba_df = proba_df.rename(columns={0: "class_0", 1: "class_1"})
    row = proba_df.iloc[0]

    pretty_dict = {
        "Not a STOP sign": float(row.get("class_0", 0.0)),
        "STOP sign": float(row.get("class_1", 0.0)),
    }

    return pretty_dict, original_img, preprocessed_img


EXAMPLES = [
    ["https://universalsigns.com/wp-content/uploads/2022/08/StopSign-3.jpg"],
    ["https://images.roadtrafficsigns.com/img/pla/K/student-drop-off-area-sign-k-2459_pl.png"],
    ["https://hansonsign.com/wp-content/uploads/2024/05/donatos-662x646.jpg"]
]

with gr.Blocks() as demo:

    gr.Markdown("# Is this a STOP sign or not?")
    gr.Markdown("Upload a photo to see results.")

    with gr.Row():
        image_in = gr.Image(type="pil", label="Input image", sources=["upload", "webcam"])
        original_img_out = gr.Image(type="pil", label="Original image")
        preprocessed_img_out = gr.Image(type="pil", label="Preprocessed image")

    with gr.Row():
        preprocess_checkbox = gr.Checkbox(label="Apply Preprocessing", value=True)

    proba_pretty = gr.Label(num_top_classes=2, label="Class probabilities")

    image_in.change(
        fn=do_predict,
        inputs=[image_in, preprocess_checkbox],
        outputs=[proba_pretty, original_img_out, preprocessed_img_out]
    )

    gr.Examples(
        examples=EXAMPLES,
        inputs=[image_in],
        label="Choose any one",
        examples_per_page=8,
        cache_examples=False,
    )


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