Update app.py

app.py

@@ -8,9 +8,9 @@ model = tf.keras.models.load_model("car_brand_classifier_final.h5")
 
 # Define the preprocessing function
 def preprocess_image(image):
-    if isinstance(image, str):  # If image is a file path, open it
-        image = Image.open(image)
-    
+    if isinstance(image, np.ndarray):  # If Gradio gives a NumPy array, convert it to PIL Image
+        image = Image.fromarray(image)
+
     image = image.convert("RGB")  # Ensure it's in RGB mode
     image = image.resize((299, 299))  # Resize to match model input size
     image = np.array(image) / 255.0  # Normalize pixel values
@@ -32,7 +32,7 @@ def predict(image):
 # Create the Gradio interface
 iface = gr.Interface(
     fn=predict,
-    inputs="image",
+    inputs=gr.Image(type="numpy"),  # Ensure Gradio passes a NumPy array
     outputs="text",
     title="Car Vision",
     description="Upload an image of a car to classify its brand."