Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,36 @@
+
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+CLASSES = ["with_mask", "without_mask", "mask_worn_incorrectly"]
+LABELS  = {
+    "with_mask":             "✅ Mask Worn Correctly",
+    "without_mask":          "❌ No Mask",
+    "mask_worn_incorrectly": "⚠️  Mask Worn Improperly",
+}
+
+# Load model from Hub
+model_path = hf_hub_download(repo_id="mpuneeth17/face-mask-detection", filename="face_mask_model.h5")
+model      = tf.keras.models.load_model(model_path)
+
+def predict(image):
+    img  = Image.fromarray(image).resize((224, 224))
+    arr  = np.expand_dims(np.array(img), 0).astype("float32") / 255.0
+    pred = model.predict(arr, verbose=0)[0]
+    return {LABELS[cls]: float(conf) for cls, conf in zip(CLASSES, pred)}
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(label="Upload face image", type="numpy"),
+    outputs=gr.Label(num_top_classes=3, label="Mask Detection Result"),
+    title="🎭 Face Mask Detection AI",
+    description="Upload a face image to detect whether a mask is worn correctly, not worn, or worn improperly.\n\nPowered by MobileNetV2 + TensorFlow.",
+    examples=[],
+    theme=gr.themes.Soft(primary_hue="indigo"),
+    allow_flagging="never",
+)
+
+demo.launch()