Create app.py

app.py

@@ -0,0 +1,57 @@
+import gradio as gr
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+from PIL import Image
+import io
+
+# Load the model
+print("Loading model...")
+model = load_model("model.keras")
+print("✅ Model loaded successfully!")
+
+# Your exact class names
+class_names = [
+    "Oral Homogenous Leukoplakia",
+    "Oral Non-Homogenous Leukoplakia",
+    "Other Oral White Lesions"
+]
+
+def predict_image(img):
+    try:
+        # Preprocess the 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
+
+        # Make prediction
+        predictions = model.predict(img_array, verbose=0)
+        predicted_class = int(np.argmax(predictions[0]))
+        confidence = float(np.max(predictions[0]) * 100)
+
+        # Prepare results
+        result = class_names[predicted_class]
+        confidences = {class_names[i]: float(predictions[0][i] * 100) for i in range(3)}
+
+        return result, confidences
+
+    except Exception as e:
+        return f"Error: {str(e)}", {}
+
+# Create Gradio Interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil", label="Upload Oral Image"),
+    outputs=[
+        gr.Label(label="Predicted Condition"),
+        gr.Label(label="Confidence Scores")
+    ],
+    title="OralScan AI - Oral Lesion Classifier",
+    description="Upload an image of oral lesion to get prediction using MobileNetV2",
+    examples=None,
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    interface.launch()