Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+
+# Load the model
+print("Loading MobileNetV2 model...")
+try:
+    model = load_model("model.keras")
+    print("✅ Model loaded successfully!")
+except Exception as e:
+    print(f"❌ Model loading failed: {e}")
+    model = None
+
+class_names = [
+    "Oral Homogenous Leukoplakia",
+    "Oral Non-Homogenous Leukoplakia",
+    "Other Oral White Lesions"
+]
+
+def predict_image(img):
+    if model is None:
+        return "Error: Model failed to load. Please check if model.keras is uploaded.", {}
+
+    try:
+        # Preprocess image
+        img = img.resize((224, 224))
+        img_array = image.img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array = img_array / 255.0
+
+        # Predict
+        predictions = model.predict(img_array, verbose=0)
+        predicted_class = int(np.argmax(predictions[0]))
+        confidence = float(np.max(predictions[0]) * 100)
+
+        result = class_names[predicted_class]
+        confidences = {class_names[i]: round(float(predictions[0][i] * 100), 2) for i in range(3)}
+
+        return result, confidences
+
+    except Exception as e:
+        return f"Prediction error: {str(e)}", {}
+
+# Gradio Interface (updated for newer Gradio)
+demo = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil", label="Upload Oral Image"),
+    outputs=[
+        gr.Label(label="Predicted Condition"),
+        gr.JSON(label="Confidence Scores")
+    ],
+    title="🦷 OralScan AI - Oral Lesion Classifier",
+    description="Upload an image to detect oral white lesions using MobileNetV2",
+    examples=None,
+    flagging_mode="never"   # Updated parameter name
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)