Create app.py

app.py

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+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+import gradio as gr
+
+# Load model
+model = torch.load("best.pt", map_location=torch.device('cpu'))
+model.eval()
+
+# Define class names (update these based on model's training)
+classes = [
+    "Apple___Apple_scab", "Apple___Black_rot", "Apple___Cedar_apple_rust",
+    "Apple___healthy", "Blueberry___healthy", "Cherry_(including_sour)___Powdery_mildew",
+    "Cherry_(including_sour)___healthy", "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot",
+    # ...continue all 39 classes...
+]
+
+# Image transforms
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+])
+
+# Prediction function
+def predict(img):
+    img = transform(img).unsqueeze(0)
+    with torch.no_grad():
+        outputs = model(img)
+        _, predicted = torch.max(outputs, 1)
+        label = classes[predicted.item()]
+    return f"Prediction: {label}"
+
+# Gradio Interface
+gr.Interface(fn=predict,
+             inputs=gr.Image(type="pil"),
+             outputs="text",
+             title="🌿 AgroScan: Plant Disease Detector",
+             description="Upload a plant leaf image to detect disease."
+).launch()