Update app.py

app.py

@@ -14,8 +14,11 @@ labels = {name: index for index, name in enumerate(classes)}
 num_classes = len(classes)
 
 # Load the model
-from keras.models import load_model
-model = load_model('sketch_recogination_model_cnn.h5')
+model_path = 'sketch_recognition_model_cnn.h5'  # Ensure this path is correct
+try:
+    model = load_model(model_path)
+except Exception as e:
+    raise RuntimeError(f"Failed to load model from {model_path}: {e}")
 
 # Predict function for the interface
 def predict_fn(image):
@@ -29,8 +32,11 @@ def predict_fn(image):
         The predicted class name.
     """
     try:
+        # Extract the image data from the input dictionary
+        image_data = image['image'] if isinstance(image, dict) else image
+
         # Preprocessing the image
-        resized_image = tf.image.resize(image, (28, 28))  # Resize to (28, 28)
+        resized_image = tf.image.resize(image_data, (28, 28))  # Resize to (28, 28)
         grayscale_image = tf.image.rgb_to_grayscale(resized_image)  # Convert to grayscale
         image_array = np.array(grayscale_image) / 255.0  # Normalize the image
 
@@ -47,14 +53,12 @@ def predict_fn(image):
         return f"Error in prediction: {str(e)}"
 
 # Gradio application interface
-
 gr.Interface(
     fn=predict_fn,
     inputs="paint",
     outputs="label",
     title="DoodleDecoder",
     description="Draw something from: Car, House, Wine bottle, Chair, Table, Tree, Camera, Fish, Rain, Clock, Hat",
-    interpretation='default',
+    interpretation='default',  # Add the interpretation parameter here
     article="Draw large with thick stroke."
 ).launch()
-