Update app.py

app.py

@@ -3,40 +3,37 @@ import tensorflow as tf
 import numpy as np
 import cv2
 
-# Load the SavedModel
-model = tf.keras.models.load_model("mask_mobilenet_savedmodel")  # Folder path
+# Load your SavedModel folder
+model = tf.keras.models.load_model("mask_mobilenet_savedmodel")
 
-# Prediction function
 def predict_mask(image):
     try:
-        # Convert to RGB if needed
-        if image.shape[2] == 4:  # RGBA
+        # Convert RGBA to RGB if needed
+        if image.shape[2] == 4:
             image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
 
-        # Resize to model input
+        # Resize and normalize
         image = cv2.resize(image, (224, 224))
-        image = image / 255.0  # Normalize
-        image = np.expand_dims(image, axis=0)  # Add batch dimension
+        image = image / 255.0
+        image = np.expand_dims(image, axis=0)
 
-        # Predict
-        preds = model.predict(image)
-        print("Preds:", preds)  # Logs in console
-
-        # Interpret prediction
+        # Predict using SavedModel callable
+        preds = model(image, training=False).numpy()
         result = "Mask" if preds[0][0] > 0.5 else "No Mask"
         return result
 
     except Exception as e:
-        print("Error:", e)  # Logs in console
+        print("Error:", e)
         return f"Error: {e}"
 
 # Gradio interface
 iface = gr.Interface(
     fn=predict_mask,
     inputs=gr.Image(type="numpy"),
-    outputs=gr.Textbox(label="Prediction"),
+    outputs="text",
     title="Mask Detection",
-    description="Upload an image to check if a person is wearing a mask or not."
+    description="Upload an image to check if a person is wearing a mask."
 )
 
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()