Create app.py

app.py

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+import gradio as gr
+import torch
+from PIL import Image
+from torchvision import transforms
+from transformers import ViTForImageClassification
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = ViTForImageClassification.from_pretrained('umutbozdag/plant-identity', num_labels=10, ignore_mismatched_sizes=True)
+model.load_state_dict(torch.load('model.pth', map_location=device))
+model.to(device)
+model.eval()
+
+# Define the prediction function
+def predict_image(img):
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    img_t = transform(img).unsqueeze(0).to(device)
+    with torch.no_grad():
+        outputs = model(img_t).logits
+        _, predicted = torch.max(outputs, 1)
+        class_names = ["Aloe Vera", "Areca Palm", "Boston Fern", "Chinese evergreen", "Dracaena", "Money Tree", "Peace lily", "Rubber Plant", "Snake Plant", "ZZ Plant"]
+        return class_names[predicted.item()]
+
+# Create a Gradio interface
+interface = gr.Interface(fn=predict_image, inputs=gr.Image(type="pil"), outputs="text")
+interface.launch(share = True)