Update app.py

app.py

@@ -3,6 +3,14 @@ import numpy as np
 import cv2
 import gradio as gr
 
+# Load model once at startup
+try:
+    model = tf.keras.models.load_model('eyeStateModel.h5', compile=False)
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"Error loading model: {str(e)}")
+    model = None
+
 def preprocess_image(image):
     # Convert to RGB if needed
     if len(image.shape) == 2:
@@ -21,38 +29,32 @@ def preprocess_image(image):
     return image
 
 def predict_eye_state(image):
+    if model is None:
+        return {"Error": 1.0}
+        
     try:
         if image is None:
-            return {
-                "Error": 1.0,
-                "Message": "Please provide an image"
-            }
+            return {"Error": 1.0}
         
         # Preprocess the image
         processed_image = preprocess_image(image)
         
-        # Load model and make prediction
-        with tf.keras.utils.custom_object_scope({'GlorotUniform': tf.keras.initializers.glorot_uniform()}):
-            model = tf.keras.models.load_model('eyeStateModel.h5', compile=False)
-        
         # Make prediction
         prediction = model.predict(processed_image)
         
-        # Map prediction to class names with confidences
-        class_names = ['Eyes Closed', 'Eyes Open']
-        confidences = {
-            class_names[0]: float(prediction[0][0]),
-            class_names[1]: float(prediction[0][1])
-        }
+        # Get confidence scores
+        closed_conf = float(prediction[0][0])
+        open_conf = float(prediction[0][1])
         
-        return confidences
+        # Return dictionary with numeric values
+        return {
+            "Eyes Closed": closed_conf,
+            "Eyes Open": open_conf
+        }
         
     except Exception as e:
         print(f"Error in prediction: {str(e)}")
-        return {
-            "Error": 1.0,
-            "Message": f"Error during prediction: {str(e)}"
-        }
+        return {"Error": 1.0}
 
 # Create Gradio interface
 demo = gr.Interface(
@@ -60,7 +62,7 @@ demo = gr.Interface(
     inputs=gr.Image(),
     outputs=gr.Label(num_top_classes=2),
     title="Eye State Detection",
-    description="Upload an image of eyes to detect if they are open or closed. The model will return confidence scores for both states."
+    description="Upload an image of eyes to detect if they are open or closed."
 )
 
 if __name__ == "__main__":