add app

app.py

@@ -16,6 +16,9 @@ CAR_BRANDS = sorted(os.listdir(DATASET_PATH))
 # Ensure the CAR_BRANDS list contains only valid directories
 CAR_BRANDS = [brand for brand in CAR_BRANDS if os.path.isdir(os.path.join(DATASET_PATH, brand))]
 
+# Print the list of car brands for debugging
+print("Car brands:", CAR_BRANDS)
+
 # Define the preprocessing function
 def preprocess_image(image):
     """
@@ -24,6 +27,10 @@ def preprocess_image(image):
     if isinstance(image, np.ndarray):  # If Gradio gives a NumPy array, convert it to PIL Image
         image = Image.fromarray(image)
 
+    # Check if the image is valid
+    if image is None or image.size == 0:
+        raise ValueError("Invalid or blank image uploaded.")
+
     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
@@ -35,25 +42,38 @@ def predict(image):
     """
     Predict the car brand and return the predicted brand and sample images.
     """
-    # Preprocess the image
-    processed_image = preprocess_image(image)
-
-    # Make a prediction
-    predictions = model.predict(processed_image)
-    predicted_class_index = np.argmax(predictions, axis=1)[0]
-    predicted_brand = CAR_BRANDS[predicted_class_index]
-
-    # Get sample images from the predicted brand folder
-    brand_folder = os.path.join(DATASET_PATH, predicted_brand)
-    sample_images = []
-    if os.path.exists(brand_folder):
-        for filename in os.listdir(brand_folder)[:5]:  # Limit to 5 sample images
-            img_path = os.path.join(brand_folder, filename)
-            if os.path.isfile(img_path):
-                sample_images.append(Image.open(img_path).resize((200, 200)))  # Resize for consistency
-
-    # Return the predicted brand and sample images as separate outputs
-    return predicted_brand, sample_images or ["No images found for this brand."]
+    try:
+        # Preprocess the image
+        processed_image = preprocess_image(image)
+
+        # Make a prediction
+        predictions = model.predict(processed_image)
+        predicted_class_index = np.argmax(predictions, axis=1)[0]
+        predicted_brand = CAR_BRANDS[predicted_class_index]
+
+        # Debugging: Print the predicted brand
+        print(f"Predicted brand: {predicted_brand}")
+
+        # Get sample images from the predicted brand folder
+        brand_folder = os.path.join(DATASET_PATH, predicted_brand)
+        print(f"Predicted brand folder: {brand_folder}")
+
+        sample_images = []
+        if os.path.exists(brand_folder):
+            print(f"Found {len(os.listdir(brand_folder))} files in the folder.")
+            for filename in os.listdir(brand_folder)[:5]:  # Limit to 5 sample images
+                img_path = os.path.join(brand_folder, filename)
+                if os.path.isfile(img_path):
+                    sample_images.append(Image.open(img_path).resize((200, 200)))  # Resize for consistency
+        else:
+            print("Brand folder does not exist.")
+
+        # Return the predicted brand and sample images as separate outputs
+        return predicted_brand, sample_images or ["No images found for this brand."]
+
+    except Exception as e:
+        print(f"Error during prediction: {e}")
+        return "Error", ["An error occurred during prediction."]
 
 # Create the Gradio interface
 iface = gr.Interface(