Update app.py

app.py

@@ -1,16 +1,19 @@
 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
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import os
 
-# Load the model
-print("Loading model...")
-model = load_model("model.keras")
-print("✅ Model loaded successfully!")
+# Load 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
 
-# Your exact class names
 class_names = [
     "Oral Homogenous Leukoplakia",
     "Oral Non-Homogenous Leukoplakia",
@@ -18,40 +21,41 @@ class_names = [
 ]
 
 def predict_image(img):
+    if model is None:
+        return "Model failed to load. Please check logs.", {}
+
     try:
-        # Preprocess the image
+        # Preprocess
         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
+        # Predict
         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)}
+        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"Error: {str(e)}", {}
+        return f"Error during prediction: {str(e)}", {}
 
-# Create Gradio Interface
+# 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")
+        gr.JSON(label="Confidence Scores")
     ],
-    title="OralScan AI - Oral Lesion Classifier",
-    description="Upload an image of oral lesion to get prediction using MobileNetV2",
-    examples=None,
+    title="🦷 OralScan AI - Oral Lesion Classifier",
+    description="MobileNetV2 model for detecting oral white lesions",
     allow_flagging="never"
 )
 
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch(server_name="0.0.0.0", server_port=7860)