Update app.py

app.py

@@ -3,9 +3,8 @@ import numpy as np
 from PIL import Image
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing import image
-import os
 
-# Load model
+# Load the model
 print("Loading MobileNetV2 model...")
 try:
     model = load_model("model.keras")
@@ -22,10 +21,10 @@ class_names = [
 
 def predict_image(img):
     if model is None:
-        return "Model failed to load. Please check logs.", {}
+        return "Error: Model failed to load. Please check if model.keras is uploaded.", {}
 
     try:
-        # Preprocess
+        # Preprocess image
         img = img.resize((224, 224))
         img_array = image.img_to_array(img)
         img_array = np.expand_dims(img_array, axis=0)
@@ -42,10 +41,10 @@ def predict_image(img):
         return result, confidences
 
     except Exception as e:
-        return f"Error during prediction: {str(e)}", {}
+        return f"Prediction error: {str(e)}", {}
 
-# Gradio Interface
-interface = gr.Interface(
+# Gradio Interface (updated for newer Gradio)
+demo = gr.Interface(
     fn=predict_image,
     inputs=gr.Image(type="pil", label="Upload Oral Image"),
     outputs=[
@@ -53,9 +52,10 @@ interface = gr.Interface(
         gr.JSON(label="Confidence Scores")
     ],
     title="🦷 OralScan AI - Oral Lesion Classifier",
-    description="MobileNetV2 model for detecting oral white lesions",
-    allow_flagging="never"
+    description="Upload an image to detect oral white lesions using MobileNetV2",
+    examples=None,
+    flagging_mode="never"   # Updated parameter name
 )
 
 if __name__ == "__main__":
-    interface.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch(server_name="0.0.0.0", server_port=7860)