Update app.py

app.py

@@ -8,19 +8,19 @@ from tensorflow.keras.models import load_model
 # Load your trained model
 model = load_model('/home/user/app/resnet50.h5')  # Ensure this path is correct
 
-def predict_character(img):
-    img = Image.fromarray(img.astype('uint8'), 'RGB')  # Ensure the image is in RGB format
-    img = img.resize((299, 299))  # Resize the image to the required size for Xception
+def predict_pokemon(img):
+    img = Image.fromarray(img.astype('uint8'), 'RGB')  # Ensure the image is in RGB
+    img = img.resize((224, 224))  # Resize the image properly using PIL
     img_array = keras_image.img_to_array(img)  # Convert the image to an array
-    img_array = np.expand_dims(img_array, axis=0)  # Expand dimensions to match the model input
-    img_array = preprocess_input(img_array)  # Preprocess the input for Xception
+    img_array = np.expand_dims(img_array, axis=0)  # Expand dimensions to fit model input
+    img_array = preprocess_input(img_array)  # Preprocess the input as expected by ResNet50
     
-    prediction = model.predict(img_array)  # Make a prediction with the model
-    classes = ['bishop', 'knight', 'rook']  # Specific character names
+    prediction = model.predict(img_array)  # Predict using the model
+    classes = ['bishop', 'knight', 'rook' ]  # Specific Pokémon names
     return {classes[i]: float(prediction[0][i]) for i in range(3)}  # Return the prediction
 
-# Define the Gradio interface
-interface = gr.Interface(fn=predict_character, 
+# Define Gradio interface
+interface = gr.Interface(fn=predict_pokemon, 
                          inputs="image",  # Simplified input type
                          outputs="label",  # Simplified output type
                          title="Chess Piece Classifier",