Update app.py

app.py

@@ -14,12 +14,11 @@ model2 = load_model_from_hub("arsath-sm/face_classification_model2", "face_class
 
 def preprocess_image(image):
     img = tf.image.resize(image, (224, 224))  # Resize to match the input size of your models
-    img = tf.cast(img, tf.float32) / 255.0    # Normalize pixel values
-    return tf.expand_dims(img, 0)             # Add batch dimension
+    img = tf.cast(img, tf.float32) / 255.0  # Normalize pixel values
+    return tf.expand_dims(img, 0)  # Add batch dimension
 
 def predict_image(image):
     preprocessed_image = preprocess_image(image)
-    
     # Make predictions using both models
     pred1 = model1.predict(preprocessed_image)[0][0]
     pred2 = model2.predict(preprocessed_image)[0][0]
@@ -30,19 +29,19 @@ def predict_image(image):
     result2 = "Real" if pred2 > 0.5 else "Fake"
     confidence2 = pred2 if pred2 > 0.5 else 1 - pred2
     
-    return {
-        "Model 1 (ResNet) Prediction": f"{result1} (Confidence: {confidence1:.2f})",
-        "Model 2 (Inception) Prediction": f"{result2} (Confidence: {confidence2:.2f})"
-    }
+    return (
+        f"Model 1 (ResNet) Prediction: {result1} (Confidence: {confidence1:.2f})",
+        f"Model 2 (Inception) Prediction: {result2} (Confidence: {confidence2:.2f})"
+    )
 
 # Create the Gradio interface
 iface = gr.Interface(
     fn=predict_image,
     inputs=gr.Image(),
-    outputs={
-        "Model 1 (ResNet) Prediction": gr.Textbox(),
-        "Model 2 (Inception) Prediction": gr.Textbox()
-    },
+    outputs=[
+        gr.Textbox(label="Model 1 (ResNet) Prediction"),
+        gr.Textbox(label="Model 2 (Inception) Prediction")
+    ],
     title="Real vs AI-Generated Face Classification",
     description="Upload an image to classify whether it's a real face or an AI-generated face using two different models: ResNet-style and Inception-style."
 )