Create app.py

app.py

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+import torch
+from torchvision import models, transforms
+from PIL import Image
+import gradio as gr
+
+## Define the device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the trained model
+def load_model():
+    model = models.resnet50(pretrained=False)
+    num_classes = 4  # Update based on your rice disease classes
+    model.fc = torch.nn.Sequential(
+        torch.nn.Linear(model.fc.in_features, 256),
+        torch.nn.ReLU(),
+        torch.nn.Linear(256, num_classes)
+    )  
+    model.load_state_dict(torch.load(r"/kaggle/input/rice_epoch8/pytorch/default/1/best_model_epoch_8.pth", map_location=device), strict=False)
+    model = model.to(device)
+    model.eval()
+    return model
+
+# Define preprocessing steps
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Prediction function
+def predict(image):
+    # Ensure image is in RGB
+    image = image.convert("RGB")
+    
+    input_tensor = transform(image).unsqueeze(0).to(device)
+    
+    # Perform inference
+    with torch.no_grad():
+        outputs = model(input_tensor)
+        _, predicted_class = torch.max(outputs, 1)
+    
+    # Map predicted class index to actual labels
+    class_names = ["Brown Spot", "Healthy", "Leaf Blast", "Neck Blast"]
+    predicted_label = class_names[predicted_class.item()]
+    
+    # Calculate confidence scores
+    probabilities = torch.nn.functional.softmax(outputs, dim=1)[0]
+    confidence = probabilities[predicted_class.item()].item()
+    
+    return f"Predicted Disease: {predicted_label}\nConfidence: {confidence*100:.2f}%"
+
+# Load the model globally
+model = load_model()
+
+# Create Gradio interface
+def launch_interface():
+    # Create a Gradio interface
+    iface = gr.Interface(
+        theme="Subh775/orchid_candy",
+        fn=predict,
+        inputs=gr.Image(type="pil", label="Upload Rice Leaf Image"),
+        outputs=gr.Textbox(label="Prediction Results"),
+        title="Rice Disease Classification",
+        description="Upload a rice leaf image to detect disease type",
+        examples=[
+            ["https://doa.gov.lk/wp-content/uploads/2020/06/brownspot3-1024x683.jpg"],
+            ["https://arkansascrops.uada.edu/posts/crops/rice/images/Fig%206%20Rice%20leaf%20blast%20coalesced%20lesions.png"],
+            ["https://th.bing.com/th/id/OIP._5ejX_5Z-M0cO5c2QUmPlwHaE7?w=280&h=187&c=7&r=0&o=5&dpr=1.1&pid=1.7"],
+            ["https://www.weknowrice.com/wp-content/uploads/2022/11/how-to-grow-rice.jpeg"],
+        ],
+        allow_flagging="never"
+    )
+    
+    return iface
+
+# Launch the interface
+if __name__ == "__main__":
+    interface = launch_interface()
+    interface.launch(share=True)