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app.py

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+import os
+import shutil
+import zipfile
+import pathlib
+import tempfile
+import gradio as gr
+import pandas as pd
+import numpy as np
+import PIL.Image
+import huggingface_hub as h
+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():
+    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, preprocess=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()