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app.py

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+# app.py
+import os
+import io
+import zipfile
+import shutil
+import tempfile
+import pathlib
+
+import pandas as pd
+from PIL import Image
+import gradio as gr
+from huggingface_hub import hf_hub_download
+import autogluon.multimodal as agmm
+
+MODEL_REPO_ID = "its-zion-18/sign-image-autogluon-predictor"
+ZIP_FILENAME = "autogluon_image_predictor_dir.zip"
+CACHE_DIR = pathlib.Path("hf_assets")
+EXTRACT_DIR = CACHE_DIR / "predictor_native"
+PREVIEW_SIZE = (224, 224)
+MAX_UPLOAD_BYTES = 20 * 1024 * 1024  # Allow up to 20 MB now
+
+ex1_path = 'IMG_0059.png'
+ex2_path = 'IMG_0064.png'
+ex3_path = 'IMG_8689.jpg'
+ex1 = Image.open(ex1_path)
+ex2 = Image.open(ex2_path)
+ex3 = Image.open(ex3_path)
+EXAMPLE_IMAGES = [ex1, ex2, ex3]
+
+CLASS_LABELS = {0: "Does not have stop sign", 1: "Has stop sign"}
+
+# Download & load predictor
+def _download_and_extract_predictor() -> str:
+    CACHE_DIR.mkdir(parents=True, exist_ok=True)
+    local_zip = 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)
+
+def load_predictor() -> agmm.MultiModalPredictor:
+    predictor_root = _download_and_extract_predictor()
+    return agmm.MultiModalPredictor.load(predictor_root)
+
+PREDICTOR = load_predictor()
+
+# Helpers
+def pil_preprocess_preview(pil_img: Image.Image, target_size=PREVIEW_SIZE) -> Image.Image:
+    return pil_img.convert("RGB").resize(target_size, Image.BILINEAR)
+
+def run_predict_binary(predictor, pil_img: Image.Image):
+    tmpd = pathlib.Path(tempfile.mkdtemp())
+    tmp_path = tmpd / "input.png"
+    pil_img.save(tmp_path)
+    input_df = pd.DataFrame({"image": [str(tmp_path)]})
+
+    probs_df = predictor.predict_proba(input_df)
+    row = probs_df.iloc[0]
+
+    # Map to {label string: probability}
+    prob_dict = {
+        CLASS_LABELS[0]: float(row[0]),
+        CLASS_LABELS[1]: float(row[1]),
+    }
+    # Pick higher one
+    pred_label = CLASS_LABELS[int(row.idxmax())]
+
+    try:
+        shutil.rmtree(tmpd)
+    except Exception:
+        pass
+
+    return pred_label, prob_dict
+
+# Gradio callback
+def infer_and_display(image: Image.Image):
+    if image is None:
+        return None, None, "No image provided.", {}
+
+    # Resize large uploads automatically
+    bio = io.BytesIO()
+    image.save(bio, format="PNG")
+    if len(bio.getvalue()) > MAX_UPLOAD_BYTES:
+        max_side = 1024
+        image.thumbnail((max_side, max_side))
+
+    preview = pil_preprocess_preview(image, PREVIEW_SIZE)
+    pred_label, probs = run_predict_binary(PREDICTOR, image)
+    return image, preview, f"Prediction: {pred_label}", probs
+
+# Build Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Stop Sign Detection — AutoGluon Predictor")
+    gr.Markdown(
+        "Upload an image or pick one of the examples. "
+        "The app shows the original and preprocessed images, and predicts whether the image **has a stop sign**."
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_in = gr.Image(type="pil", label="Upload an image", sources="upload")
+            run_btn = gr.Button("Run inference")
+            gr.Examples(EXAMPLE_IMAGES, inputs=[image_in], label="Example images", cache_examples=False)
+        with gr.Column(scale=1):
+            gr.Markdown("**Original image**")
+            orig_out = gr.Image(type="pil")
+            gr.Markdown("**Preprocessed image (preview)**")
+            pre_out = gr.Image(type="pil")
+
+    out_text = gr.Textbox(label="Prediction", interactive=False)
+    proba_label = gr.Label(label="Class probabilities")
+
+    run_btn.click(fn=infer_and_display, inputs=[image_in], outputs=[orig_out, pre_out, out_text, proba_label])
+
+if __name__ == "__main__":
+    demo.launch()
+