Update app.py

app.py

@@ -1,39 +1,39 @@
-import gradio as gr
-import numpy as np
-from PIL import Image
-import onnxruntime as ort
-
-# Load the ONNX model
-model_path = "cifar10_model.onnx"
-ort_session = ort.InferenceSession(model_path)
-
-# CIFAR-10 class labels
-labels = [
-    "airplane", "automobile", "bird", "cat", "deer",
-    "dog", "frog", "horse", "ship", "truck"
-]
-
-def preprocess_image(image):
-    # Resize to 32x32 and normalize
-    image = image.resize((32, 32))
-    image = np.array(image).astype(np.float32) / 255.0
-    # Reshape to (1, 3, 32, 32) [batch, channels, height, width]
-    return np.expand_dims(image.transpose(2, 0, 1), axis=0)
-
-def predict(image):
-    # Preprocess the image
-    input_data = preprocess_image(image)
-    # Run inference
-    outputs = ort_session.run(None, {"serving_default_keras_tensor:0": input_data})[0]
-    predicted_class_idx = np.argmax(outputs)
-    return labels[predicted_class_idx]
-
-# Gradio Interface
-gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Label(num_top_classes=3),
-    title="CIFAR-10 Classifier",
-    description="Upload an image to classify it into CIFAR-10 categories.",
-    allow_flagging="never"  # Disable flagging
+import gradio as gr
+import numpy as np
+from PIL import Image
+import onnxruntime as ort
+
+# Load the ONNX model
+model_path = "cifar10_model.onnx"
+ort_session = ort.InferenceSession(model_path)
+
+# CIFAR-10 class labels
+labels = [
+    "airplane", "automobile", "bird", "cat", "deer",
+    "dog", "frog", "horse", "ship", "truck"
+]
+
+def preprocess_image(image):
+    # Resize to 32x32 and normalize
+    image = image.resize((32, 32))
+    image = np.array(image).astype(np.float32) / 255.0
+    # Reshape to (1, 3, 32, 32) [batch, channels, height, width]
+    return np.expand_dims(image.transpose(2, 0, 1), axis=0)
+
+def predict(image):
+    # Preprocess the image
+    input_data = preprocess_image(image)
+    # Run inference
+    outputs = ort_session.run(None, {"serving_default_keras_tensor:0": input_data})[0]
+    predicted_class_idx = np.argmax(outputs)
+    return labels[predicted_class_idx]
+
+# Gradio Interface
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="CIFAR-10 Classifier",
+    description="Upload an image to classify it into CIFAR-10 categories.",
+    allow_flagging="never"  # Disable flagging
 ).launch(server_name="0.0.0.0", server_port=7860)