Create app.py

This is a simple app that demonstrates how to use an autogluon multimodal predictor in a gradio space to predict if a picture contains a stop sign. To use, just upload a photo. The result will be generated automatically if there is or is not a stop sign.

app.py

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+import os  # For reading environment variables
+import shutil  # For directory cleanup
+import zipfile  # For extracting model archives
+import pathlib  # For path manipulations
+import tempfile  # For creating temporary files/directories
+
+import gradio  # For interactive UI
+import pandas  # For tabular data handling
+import PIL.Image # For image I/O
+
+import huggingface_hub # For downloading model assets
+import autogluon.multimodal  # For loading AutoGluon image classifier
+
+# Hardcoded Hub model (native zip)
+MODEL_REPO_ID = "yusenthebot/sign-identification-autogluon" # Updated model ID
+ZIP_FILENAME  = "autogluon_sign_predictor_dir.zip"
+HF_TOKEN = os.getenv("HF_TOKEN", None)
+
+# Local cache/extract dirs
+CACHE_DIR   = pathlib.Path("hf_assets")
+EXTRACT_DIR = CACHE_DIR / "predictor_native"
+
+# Download & load the native predictor
+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",
+        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)
+
+# Explicit class labels (edit copy as desired)
+CLASS_LABELS = {0: "⛔ No Stop Sign", 1: "🛑 Stop Sign"} # Updated class labels
+
+# Helper to map model class -> human label
+def _human_label(c):
+    try:
+        ci = int(c)
+        return CLASS_LABELS.get(ci, str(c))
+    except Exception:
+        return CLASS_LABELS.get(c, str(c))
+
+# Do the prediction!
+def do_predict(pil_img: PIL.Image.Image):
+    # Make sure there's actually an image to work with
+    if pil_img is None:
+        return "No image provided.", {}, pandas.DataFrame(columns=["Predicted label", "Confidence (%)"])
+
+    # IF we have something to work with, save it and prepare the input
+    tmpdir = pathlib.Path(tempfile.mkdtemp())
+    img_path = tmpdir / "input.png"
+    pil_img.save(img_path)
+
+    df = pandas.DataFrame({"image": [str(img_path)]})  # For AutoGluon expected input format
+
+    # For class probabilities
+    proba_df = PREDICTOR.predict_proba(df)
+
+    # For user-friendly column names
+    proba_df = proba_df.rename(columns={0: "⛔ No Stop Sign (0)", 1: "🛑 Stop Sign (1)"}) # Updated column names
+    row = proba_df.iloc[0]
+
+    # For pretty ranked dict expected by gr.Label
+    pretty_dict = {
+        "⛔ No Stop Sign": float(row.get("⛔ No Stop Sign (0)", 0.0)), # Updated dictionary keys
+        "🛑 Stop Sign": float(row.get("🛑 Stop Sign (1)", 0.0)), # Updated dictionary keys
+    }
+
+    return pretty_dict
+
+# Representative example images! These can be local or links.
+EXAMPLES = [
+    ["https://datasets-server.huggingface.co/assets/ecopus/sign_identification/--/a1506696eb48233ed9cd1afa1bea8e7002a7ad85/--/default/original/2/image/image.jpg?Expires=1758737216&Signature=uWOZzcVh97AFIfr9g6DQpm3YDAJUQKn5etPOan9JsZAKH5p0PGd1lX3gD6BfoN~MxhSru7DSYqX25ohageHm0dpAAnP0MiOnbirCRjRIKubBuRUI6NH5KdZ5uIAyA7Qdxd4f7~jo1YDS4QfqWPv0WMhJnKXCWqYStPFENK8HkVN7lYBk4q1lC8iEtjav5WSib~n2Wk8QN9BnhXCvczCpWol7aCCXaZG2QdHjJ3FqwoEW1p2kQC6luJoXXdvHMrRnSvDSR0i7kr6XS2cbaPZc6kJN8jKa06JUYIdgoVZM3RZfrY8DaxR3o9XA7lbM~W8-wrGULCOjLzsgSkN4fuzDuw__&Key-Pair-Id=K3EI6M078Z3AC3"], # Example from the new dataset
+    ["https://datasets-server.huggingface.co/assets/ecopus/sign_identification/--/a1506696eb48233ed9cd1afa1bea8e7002a7ad85/--/default/original/6/image/image.jpg?Expires=1758737216&Signature=AxmjPAovyejutgVUsczDxtIIvpqHCYxyE-J1NO2EAvwoasguueWPpFFi3Buv2--LdyEnkuZsbO8tEFttbwinKPirc002NZHcNvM~BMJQIvgAxaKc7hNbQBcMhmFUI27opJU-5fX5QGxVULy5UkTnIPtHtQhdcbMFCa8PVP~AQ5RREBdTbwAzXp-KVVExqH~MUHqt9~HMUoPbNUs41dytIjgsoBF4~PNsDu6~dosLtLjJGQtMFZtRBYUH2gzHNgNHLLikiFrwWp1SKMg1LnkqBLbjq3Qsr7Bw3DmyzrGenIw9mOh9aQ9jFW2Pw32LgC54yxzWHluZ0t2xSP9-Vh0MWw__&Key-Pair-Id=K3EI6M078Z3AC3"],
+    ["https://datasets-server.huggingface.co/assets/ecopus/sign_identification/--/a1506696eb48233ed9cd1afa1bea8e7002a7ad85/--/default/original/16/image/image.jpg?Expires=1758737216&Signature=xR-QqmSpXqj9Z6YB-p8URFQ~Og90zuqSnvyHulcwrl42XMeU1bnd~Z6WjIE1T80MPQpas22gM--ou5pw-vzJctNhjz7kH~ZCdvWPL9QIQQ7NxRc5uoEikZTv2SiFBF5X6PpARjddFZmBKfD2~Rq-kUU2-FYvIRwUmodWB5wRblmrovfFB1aRBY4-oLN~ghl2xpb-RgCZWVMbyI5FGMddGhGFygJICcTe2qOmtXLZcyyrGd~j3XwntVE6cp1rlfTGRcXl1H~ykq-G4OBZkOAYCa~u9yLeUcaoaRHGvg6Sla3-efvQUMQ2kENDrqgljAxx-zFLHi4E1ezfFH4SvZx5AA__&Key-Pair-Id=K3EI6M078Z3AC3"],# Example from the new dataset
+    ["https://datasets-server.huggingface.co/assets/ecopus/sign_identification/--/a1506696eb48233ed9cd1afa1bea8e7002a7ad85/--/default/original/10/image/image.jpg?Expires=1758737216&Signature=GfHEIohqv359Wkp6WO1U8qyGKzDT5ilUqy4LOSzQZngNdZgiFgg7r902ZWDNR3EhKvTlog3oDsyGlVOWgtEqkR5QR2FUw-kp--Hf6TNeya3D6yvWcoSTaQL3aRgHCm1lFzYJR20HHbJ3M7f-mf4cNwe1m2nzSxTkMNDfUQNdyl6w6l6mujUSFRzSzYd6LSxVYCdLVeCYrnlj3kkpB~DqCz0TrhHbrtj0kxoNuGYSgG6NIdq67sE9m9KWoIX5HpVOfSe-AYiDh8lqO2kQJwQcmVFRiIJqH~q7DjpCnz0SmH1iWdQ-4TZlTL-5PPWGEMHOn1V~L2dFEw4enNd-zNK~tg__&Key-Pair-Id=K3EI6M078Z3AC3"] # Example from the new dataset
+
+]
+
+# Gradio UI
+with gradio.Blocks() as demo:
+
+    # Provide an introduction
+    gradio.Markdown("# Stop Sign Detection?") # Updated title
+    gradio.Markdown("""
+    This is a simple app that demonstrates how to use an autogluon multimodal
+    predictor in a gradio space to predict if a picture contains a stop sign. To use,
+    just upload a photo. The result will be generated automatically if there is or is not a stop sign.
+    """) # Updated description
+
+    # Interface for the incoming image
+    image_in = gradio.Image(type="pil", label="Input image", sources=["upload", "webcam"])
+
+    # Interface elements to show htte result and probabilities
+    proba_pretty = gradio.Label(num_top_classes=2, label="Class probabilities")
+
+    # Whenever a new image is uploaded, update the result
+    image_in.change(fn=do_predict, inputs=[image_in], outputs=[proba_pretty])
+
+      # For clickable example images
+    gradio.Examples(
+        examples=EXAMPLES,
+        inputs=[image_in],
+        label="Representative examples",
+        examples_per_page=8,
+        cache_examples=False,
+    )
+
+if __name__ == "__main__":
+    demo.launch()