Create app.py

app.py

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+# ================================================================
+# HW3: Driving a Stop Sign Image Classifier with Gradio
+#
+# Author: Your Name
+# Course: 24679 - Designing and Deploying AI/ML Systems
+# Dataset: Binary stop sign dataset (class_1 = stop sign, class_0 = not stop sign)
+# Task: Image classification deployed via Hugging Face Space
+#
+# Acknowledgments:
+# - Model trained by a classmate in Homework 2
+# - Deployment scaffold and documentation supported with AI assistance (ChatGPT, OpenAI)
+# - Reference: Class-provided notebook "image gradio.ipynb"
+# ================================================================
+
+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
+
+
+# -------------------------
+# Hugging Face model setup
+# -------------------------
+MODEL_REPO_ID = "cassieli226/sign-identification-automl"  # <- update with teammate’s repo
+ZIP_FILENAME = "autogluon_predictor_dir.zip"
+HF_TOKEN = os.getenv("HF_TOKEN", None)
+
+CACHE_DIR = pathlib.Path("hf_assets")
+EXTRACT_DIR = CACHE_DIR / "predictor_native"
+
+
+def _prepare_predictor_dir() -> str:
+    """Download and extract the AutoGluon predictor directory from Hugging Face."""
+    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)
+
+
+# -------------------------
+# Class labels
+# -------------------------
+CLASS_LABELS = {
+    0: "🚦 Not a Stop Sign",
+    1: "🛑 Stop Sign"
+}
+
+
+def _human_label(c):
+    try:
+        ci = int(c)
+        return CLASS_LABELS.get(ci, str(c))
+    except Exception:
+        return CLASS_LABELS.get(c, str(c))
+
+
+# -------------------------
+# Prediction + preprocessing
+# -------------------------
+def do_predict(pil_img: PIL.Image.Image):
+    """Run prediction on an uploaded image. Returns original, preprocessed, and probabilities."""
+
+    if pil_img is None:
+        return None, None, "No image provided.", {}
+
+    try:
+        # --- Save to temp path for AutoGluon ---
+        tmpdir = pathlib.Path(tempfile.mkdtemp())
+        img_path = tmpdir / "input.png"
+        pil_img.save(img_path)
+
+        # --- Preprocess (resize to 224x224 for visualization only) ---
+        preprocessed_img = pil_img.copy()
+        preprocessed_img = preprocessed_img.resize((224, 224))
+
+        # --- Build input dataframe for AutoGluon ---
+        df = pandas.DataFrame({"image": [str(img_path)]})
+
+        # --- Predict probabilities ---
+        proba_df = PREDICTOR.predict_proba(df)
+
+        # Rename for clarity
+        proba_df = proba_df.rename(columns={
+            0: "🚦 Not a Stop Sign (0)",
+            1: "🛑 Stop Sign (1)"
+        })
+        row = proba_df.iloc[0]
+
+        pretty_dict = {
+            "🚦 Not a Stop Sign": float(row.get("🚦 Not a Stop Sign (0)", 0.0)),
+            "🛑 Stop Sign": float(row.get("🛑 Stop Sign (1)", 0.0)),
+        }
+
+        return pil_img, preprocessed_img, "Prediction complete", pretty_dict
+
+    except Exception as e:
+        return None, None, f"Error: {str(e)}", {}
+
+
+# -------------------------
+# Example images
+# -------------------------
+EXAMPLES = [
+    ["https://upload.wikimedia.org/wikipedia/commons/thumb/f/f9/STOP_sign.jpg/640px-STOP_sign.jpg"],
+    ["https://upload.wikimedia.org/wikipedia/commons/1/19/Swiss_Frutiger_Traffic_Sign.jpg"]
+]
+
+
+# -------------------------
+# Gradio interface
+# -------------------------
+with gradio.Blocks() as demo:
+
+    gradio.Markdown("# 🛑 Stop Sign Detector")
+    gradio.Markdown("""
+    Upload a road scene or traffic sign image, and this app will classify whether
+    a stop sign is present. The interface shows both the original image and the
+    preprocessed image (224x224) that the model actually sees.
+    """)
+
+    with gradio.Row():
+        image_in = gradio.Image(
+            type="pil",
+            label="Upload or capture an image",
+            sources=["upload", "webcam"],
+            image_mode="RGB"
+        )
+
+    with gradio.Row():
+        orig_out = gradio.Image(type="pil", label="Original Image")
+        preproc_out = gradio.Image(type="pil", label="Preprocessed Image (224x224)")
+
+    status_out = gradio.Textbox(label="Status")
+    proba_pretty = gradio.Label(num_top_classes=2, label="Class probabilities")
+
+    image_in.change(
+        fn=do_predict,
+        inputs=[image_in],
+        outputs=[orig_out, preproc_out, status_out, proba_pretty]
+    )
+
+    gradio.Examples(
+        examples=EXAMPLES,
+        inputs=[image_in],
+        label="Representative examples",
+        examples_per_page=3,
+        cache_examples=False,
+    )
+
+if __name__ == "__main__":
+    demo.launch()