Trail app

app.py

@@ -0,0 +1,49 @@
+import gradio as gr
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+
+# Load the trained model
+model_path = "cifar_net.pth"
+model = torch.load(model_path, map_location=torch.device('cpu'))
+model.eval()
+
+# Define class labels for CIFAR-10
+classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
+
+def classify_image(image):
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
+    ])
+
+    # Preprocess the input image
+    image = transform(image).unsqueeze(0)
+
+    # Perform inference with the model
+    outputs = model(image)
+    _, predicted = torch.max(outputs, 1)
+    predicted_class = classes[predicted.item()]
+
+    return predicted_class
+
+def classify_images(images):
+    return [classify_image(image) for image in images]
+
+inputs_image = gr.inputs.Image(label="Input Image", type="pil")
+outputs_image = gr.outputs.Label(label="Predicted Class")
+interface_image = gr.Interface(
+    fn=classify_images,
+    inputs=inputs_image,
+    outputs=outputs_image,
+    title="CIFAR-10 Image Classifier",
+    description="Classify images into one of the CIFAR-10 classes.",
+    examples=[
+        ['image_0.jpg'], 
+        ['image_1.jpg']
+    ],
+    allow_flagging=False
+)
+
+if __name__ == "__main__":
+    interface_image.launch()

cifar_net.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a39b10e26fc76a2d0f097dfd792ff7dac5a7c79ecbb1732017a726f3c1fafdc5
+size 251167

requirements.txt

@@ -0,0 +1,8 @@
+# Ultralytics requirements
+# Usage: pip install -r requirements.txt
+ 
+# Base ----------------------------------------
+torch==1.9.0
+torchvision==0.10.0
+gradio==2.3.5
+Pillow==8.2.0