Create app.py

app.py

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+import torch
+import torchvision.transforms as transforms
+from torchvision.models import resnet50
+from PIL import Image
+import gradio as gr
+
+# Load the pre-trained model (ResNet50)
+model = resnet50(pretrained=True)
+model.eval()
+
+# Define the transforms
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  # Resize to the size expected by ResNet
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Define the prediction function
+def predict(image):
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+    image = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        outputs = model(image)
+        _, predicted = torch.max(outputs, 1)
+    return predicted.item()
+
+# Create and launch the Gradio interface
+iface = gr.Interface(fn=predict, inputs="image", outputs="label")
+iface.launch()