Create app.py

app.py

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+import io, os, tempfile, pathlib, zipfile, shutil
+from PIL import Image, ImageOps
+import pandas as pd  # needed to hold predictor output, but we won't return any DataFrame
+from huggingface_hub import hf_hub_download
+import autogluon.multimodal as ag
+import gradio as gr
+
+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"
+
+CLASS_LABELS = {0: "Other sign", 1: "Stop sign"}
+DEFAULT_SIZE = 224
+
+def _prepare_predictor_dir() -> 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())
+    return str(contents[0] if len(contents) == 1 and contents[0].is_dir() else EXTRACT_DIR)
+
+PREDICTOR = ag.MultiModalPredictor.load(_prepare_predictor_dir())
+
+def preprocess_for_model(img: Image.Image, size: int = DEFAULT_SIZE) -> Image.Image:
+    return ImageOps.fit(img, (size, size), method=Image.BICUBIC, centering=(0.5, 0.5))
+
+# -------------------------
+# Prediction (return 4 values to match your outputs)
+# -------------------------
+def predict(img: Image.Image):
+    if img is None:
+        return None, None, {"Error": 1.0}, "**Error:** No image provided."
+    pre_img = preprocess_for_model(img, DEFAULT_SIZE)
+
+    # temp save for AutoGluon
+    tmpdir = pathlib.Path(tempfile.mkdtemp())
+    img_path = tmpdir / "input.png"
+    pre_img.save(img_path)
+
+    df = pd.DataFrame({"image": [str(img_path)]})
+    proba_df = PREDICTOR.predict_proba(df)
+    row = proba_df.iloc[0]
+
+    # Handle int or str column names
+    p0 = float(row.get(0, row.get("0", 0.0)))
+    p1 = float(row.get(1, row.get("1", 0.0)))
+
+    pretty = {CLASS_LABELS[0]: p0, CLASS_LABELS[1]: p1}
+    status = ""  # empty if OK
+
+    # EXACTLY 4 returns to match your outputs list
+    return img, pretty, status
+
+EXAMPLES = [
+    "https://drive.google.com/uc?id=1OwjdQdiclDOCuUirVsCX-_J1q1Y_G-Xs",
+    "https://drive.google.com/uc?id=1g9XwRJGcqYwJ_mFtHj25ZE1w2VZPkBiR",
+    "https://drive.google.com/uc?id=1MH5aIvHWKdamwuzdQPWlNwaoRE3c2bVb"
+]
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Input image (PNG/JPG)", sources=["upload", "webcam"]),
+    outputs=[
+        gr.Image(label="Preprocessed"),
+        gr.Label(num_top_classes=2, label="Class probabilities"),
+        gr.Markdown(label="Status"),
+    ],
+    title="🪧 Sign Identification",
+    description="Upload a PNG/JPG. Predicts Stop vs Other sign.",
+    allow_flagging="never",
+    examples=EXAMPLES,
+)
+
+if __name__ == "__main__":
+    demo.launch()