Update app.py

app.py

@@ -1,7 +1,7 @@
 import os
 import numpy as np
 import tensorflow as tf
-from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from tensorflow.keras.preprocessing.image import img_to_array
 from tensorflow.keras.applications.efficientnet_v2 import preprocess_input
 from tensorflow.keras.models import load_model
 import gradio as gr
@@ -35,7 +35,8 @@ model = load_model("setosys_dogs_model.h5")
 
 # Preprocessing function
 def preprocess_image(image):
-    img = load_img(image, target_size=(224, 224))  # Resize image to 224x224
+    # No need to use load_img, the image is already a PIL image from Gradio
+    img = image.resize((224, 224))  # Resize image to 224x224
     img_array = img_to_array(img)
     img_array = preprocess_input(img_array)  # EfficientNetV2 preprocessing
     img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
@@ -48,12 +49,12 @@ def predict_dog_breed(image):
     class_idx = np.argmax(predictions)  # Get index of the class with the highest probability
     breed = class_labels[class_idx]  # Map the index to the breed name
     confidence = predictions[0][class_idx]  # Confidence score
-    return breed, confidence
+    return [breed, confidence]  # Return as a list
 
 # Create a Gradio interface
 iface = gr.Interface(fn=predict_dog_breed, 
-                     inputs=gr.Image(type="pil"),  # Updated input type to 'gr.Image'
-                     outputs=[gr.Textbox(), gr.Number()],  # Updated output to Textbox and Number
+                     inputs=gr.Image(type="pil"),  # Gradio automatically handles PIL images
+                     outputs=gr.JSON(),  # Outputs will be a JSON response (list of results)
                      live=True)
 
 # Launch the Gradio app