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app.py

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+import pathlib, shutil, zipfile, tempfile
+import pandas
+import gradio
+import huggingface_hub
+import autogluon.multimodal
+import PIL.Image
+
+MODEL_REPO_ID = "george2cool36/hw2_image_automl_autogluon"
+ZIP_FILENAME  = "ag_image_predictor_dir.zip"
+CACHE_DIR = pathlib.Path("hf_assets")
+EXTRACT_DIR = CACHE_DIR / "predictor_native"
+
+CLASS_LABELS = {0: "🛑 Has Stop Sign", 1: "✅ No Stop Sign"}
+
+def _prepare_predictor_dir() -> str:
+    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",
+        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 _human_label(c):
+    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):
+    if pil_img is None:
+        return {}, "No image provided."
+
+    tmpdir = pathlib.Path(tempfile.mkdtemp())
+    img_path = tmpdir / "input.png"
+    pil_img.save(img_path)
+
+    df = pandas.DataFrame({"image": [str(img_path)]})
+
+    proba_df = PREDICTOR.predict_proba(df)
+    proba_df = proba_df.rename(columns={0: "🛑 Has Stop Sign (0)", 1: "✅ No Stop Sign (1)"})
+    row = proba_df.iloc[0]
+
+    pretty_dict = {
+        "🛑 Has Stop Sign": float(row.get("🛑 Has Stop Sign (0)", 0.0)),
+        "✅ No Stop Sign": float(row.get("✅ No Stop Sign (1)", 0.0)),
+    }
+
+    predicted_class = PREDICTOR.predict(df).iloc[0]
+    pred_label = _human_label(predicted_class)
+
+    md = f"**Prediction:** {pred_label}"
+    if pretty_dict:
+        md += f"  \n**Confidence:** {round(pretty_dict.get(pred_label, 0.0) * 100, 2)}%"
+
+
+    return pretty_dict, md
+
+EXAMPLES = [
+    ["https://www.kingsrivercasting.com/images/stories/virtuemart/product/STOP%20SIGN%20(5).jpg"],
+    ["https://www.trafficsafetywarehouse.com/Resources/images/traffic-sign-shapes.jpeg"],
+    ["https://di-uploads-pod16.dealerinspire.com/toyotaofnorthcharlotte/uploads/2020/08/yield-road-sign.jpg"]
+]
+
+
+with gradio.Blocks() as demo:
+    gradio.Markdown("# Has Stop Sign or Not?")
+    gradio.Markdown(
+        "This is a simple app that demonstrates how to use an autogluon multimodal"
+        "predictor in a gradio space to predict whether an image contains a stop sign. To use,"
+        "just upload a photo. The result should be generated automatically."
+    )
+
+    image_in = gradio.Image(type="pil", label="Input image", sources=["upload", "webcam"])
+
+    proba_pretty = gradio.Label(num_top_classes=2, label="Class probabilities")
+    prediction_output = gradio.Markdown()
+
+
+    inputs = [image_in]
+    outputs = [proba_pretty, prediction_output]
+    for comp in inputs:
+        comp.change(fn=do_predict, inputs=inputs, outputs=outputs)
+
+    gradio.Examples(
+        examples=EXAMPLES,
+        inputs=inputs,
+        label="Representative examples",
+        examples_per_page=8,
+        cache_examples=False,
+    )
+
+if __name__ == "__main__":
+    demo.launch(debug=False)