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app.py

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+import os, pathlib, zipfile, tempfile
+import pandas as pd
+import gradio as gr
+from PIL import Image
+from huggingface_hub import hf_hub_download, snapshot_download
+import autogluon.multimodal as ag
+
+MODEL_REPO_ID = "samder03/2025-24679-image-autogluon-predictor"
+
+CLASS_LABELS = {
+    0: "No Stop Sign",
+    1: "Stop Sign",
+    "class_0": "No Stop Sign",
+    "class_1": "Stop Sign",
+}
+
+def _human_label(c):
+    try:
+        ci = int(c)
+        return CLASS_LABELS.get(ci, str(c))
+    except Exception:
+        return CLASS_LABELS.get(str(c), str(c))
+
+def _locate_predictor_dir_from_repo_folder(repo_dir: str) -> str:
+    rd = pathlib.Path(repo_dir)
+    for p in rd.rglob("predictor.pkl"):
+        return str(p.parent)
+    return ""
+
+def _prepare_predictor_dir() -> str:
+    repo_dir = snapshot_download(repo_id=MODEL_REPO_ID, repo_type="model")
+    pred_dir = _locate_predictor_dir_from_repo_folder(repo_dir)
+    if pred_dir:
+        return pred_dir
+
+    # Fallback: try to find a zip and extract
+    zips = list(pathlib.Path(repo_dir).rglob("*.zip"))
+    if not zips:
+        raise FileNotFoundError("Could not find a predictor directory or .zip in the model repo.")
+    zip_path = str(zips[0])
+
+    workdir = tempfile.mkdtemp(prefix="ag_img_predictor_")
+    with zipfile.ZipFile(zip_path, "r") as zf:
+        zf.extractall(workdir)
+    entries = list(pathlib.Path(workdir).iterdir())
+    if len(entries) == 1 and entries[0].is_dir():
+        return str(entries[0])
+    return workdir
+
+PREDICTOR_DIR = _prepare_predictor_dir()
+PREDICTOR = ag.MultiModalPredictor.load(PREDICTOR_DIR)
+
+def _ensure_rgb(img: Image.Image) -> Image.Image:
+    return img.convert("RGB") if img.mode != "RGB" else img
+
+def _resize_shorter(img: Image.Image, shorter: int) -> Image.Image:
+    w, h = img.size
+    if min(w, h) == shorter:
+        return img
+    if w < h:
+        new_w = shorter
+        new_h = int(h * (shorter / w))
+    else:
+        new_h = shorter
+        new_w = int(w * (shorter / h))
+    return img.resize((new_w, new_h), Image.BICUBIC)
+
+def _center_crop(img: Image.Image, size: int) -> Image.Image:
+    w, h = img.size
+    side = min(w, h, size)
+    left = (w - side) // 2
+    top  = (h - side) // 2
+    return img.crop((left, top, left + side, top + side)).resize((size, size), Image.BICUBIC)
+
+def _validate_image(pil_img: Image.Image, max_pixels: int = 8_000_000):
+    if pil_img is None:
+        return False, "No image provided."
+    if pil_img.width * pil_img.height > max_pixels:
+        return False, f"Image too large (>{max_pixels:,} pixels). Please upload a smaller image."
+    return True, ""
+
+def preprocess(pil_img: Image.Image, resize_shorter: int, do_center_crop: bool, crop_size: int) -> Image.Image:
+    img = _ensure_rgb(pil_img)
+    img = _resize_shorter(img, resize_shorter)
+    if do_center_crop:
+        img = _center_crop(img, crop_size)
+    return img
+
+def do_predict(pil_img: Image.Image, resize_shorter: int, do_center_crop: bool, crop_size: int, top_k: int):
+    ok, msg = _validate_image(pil_img)
+    if not ok:
+        return None, None, {"Error": 1.0}
+
+    pre_img = preprocess(pil_img, resize_shorter, do_center_crop, crop_size)
+
+    tmpdir = pathlib.Path(tempfile.mkdtemp(prefix="ag_img_run_"))
+    orig_path = tmpdir / "original.png"
+    pre_path  = tmpdir / "preprocessed.png"
+    pil_img.save(orig_path)
+    pre_img.save(pre_path)
+
+    df = pd.DataFrame({"image": [str(pre_path)]})
+    proba_df = PREDICTOR.predict_proba(df)
+    proba_df = proba_df.rename(columns={c: _human_label(c) for c in proba_df.columns})
+    row = proba_df.iloc[0]
+
+    items = sorted(row.items(), key=lambda kv: float(kv[1]), reverse=True)[:max(1, int(top_k))]
+    pretty = {k: float(v) for k, v in items}
+
+    return Image.open(orig_path), Image.open(pre_path), pretty
+
+with gr.Blocks(title="Stop Sign Classifier") as demo:
+    gr.Markdown("# Stop Sign? — Image Classifier (Classmate Model)")
+    gr.Markdown(
+        "Upload a PNG/JPG (or use webcam). You’ll see the **original** and the **preprocessed** image, "
+        "plus ranked class probabilities."
+    )
+
+    with gr.Row():
+        image_in = gr.Image(type="pil", label="Input image (PNG/JPG)", sources=["upload", "webcam"])
+        with gr.Column():
+            with gr.Accordion("Inference Parameters", open=False):
+                resize_shorter = gr.Slider(64, 1024, value=384, step=16, label="Resize (shorter side)")
+                do_center_crop = gr.Checkbox(value=True, label="Center-crop to square")
+                crop_size      = gr.Slider(64, 1024, value=384, step=16, label="Crop size (if center-crop)")
+                top_k          = gr.Slider(1, 2, value=2, step=1, label="Top-K classes to display")
+
+    with gr.Row():
+        img_orig = gr.Image(label="Original")
+        img_proc = gr.Image(label="Preprocessed")
+    proba_pretty = gr.Label(num_top_classes=2, label="Class probabilities (Top-K)")
+
+    image_in.change(
+        fn=do_predict,
+        inputs=[image_in, resize_shorter, do_center_crop, crop_size, top_k],
+        outputs=[img_orig, img_proc, proba_pretty]
+    )
+
+    gr.Examples(
+        examples=[
+            ["https://upload.wikimedia.org/wikipedia/commons/thumb/2/23/Stop_sign_light_red.svg/640px-Stop_sign_light_red.svg.png"],
+            ["https://upload.wikimedia.org/wikipedia/commons/thumb/1/12/No_parking_sign.png/480px-No_parking_sign.png"],
+            ["https://upload.wikimedia.org/wikipedia/commons/thumb/4/4f/Stop_sign_in_the_United_States.jpg/640px-Stop_sign_in_the_United_States.jpg"],
+        ],
+        inputs=[image_in],
+        label="Example images",
+        examples_per_page=6,
+        cache_examples=False,
+    )
+
+if __name__ == "__main__":
+    demo.launch()