Update app.py

app.py

@@ -1,6 +1,8 @@
 import requests
 import tensorflow as tf
 import gradio as gr
+from PIL import Image
+import numpy as np
 
 def classify_image(input_image):
     # Download human-readable labels for ImageNet.
@@ -22,26 +24,44 @@ def classify_image(input_image):
         classifier_activation="softmax"
     )
 
-    # Resize the input image to the expected size
-    input_image = input_image.reshape((1, 224, 224, 3))  # Reshape for a single prediction
+    # Handle input_image (ensure it's a PIL Image)
+    if isinstance(input_image, str):  # If it's a file path or URL
+        input_image = Image.open(input_image).convert("RGB")
+    elif isinstance(input_image, np.ndarray):  # If it's a numpy array
+        input_image = Image.fromarray(input_image).convert("RGB")
+
+    # Resize the image to 224x224
+    input_image = input_image.resize((224, 224))
+
+    # Convert image to a numpy array
+    input_image = np.array(input_image)
+
+    # Ensure it's in the right format (RGB channels only)
+    if input_image.shape[-1] == 4:  # If there's an alpha channel
+        input_image = input_image[..., :3]  # Remove the alpha channel
+
+    # Reshape for a single prediction
+    input_image = input_image.reshape((1, 224, 224, 3))
+
+    # Preprocess the image
     input_image = tf.keras.applications.mobilenet_v2.preprocess_input(input_image)
-    
+
     # Perform prediction
     prediction = inception_net.predict(input_image).flatten()
     confidences = {labels[i]: float(prediction[i]) for i in range(len(labels))}
     return confidences
-    
+
 image = gr.Image(interactive=True, label="Upload Image")
 label = gr.Label(num_top_classes=3, label="Top Predictions")
 
 demo = gr.Interface(
     title="Image Classifier Keras",
-    fn= classify_image,
-    inputs= image, 
-    outputs= label,
-    examples= [["./images/banana.jpg"], ["./images/car.jpg"], ["./images/guitar.jpg"], ["./images/lion.jpg"]],  # Use valid URLs or local paths
-    theme= "default",
-    css= ".footer{display:none !important}"
+    fn=classify_image,
+    inputs=image,
+    outputs=label,
+    examples=[["./images/banana.jpg"], ["./images/car.jpg"], ["./images/guitar.jpg"], ["./images/lion.jpg"]],
+    theme="default",
+    css=".footer{display:none !important}"
 )
 
 if __name__ == "__main__":