Create app.py

app.py

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+import gradio as gr
+import torch
+import torchvision.transforms as transforms
+from torchvision.models import resnet50
+from PIL import Image
+
+# Load the model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = resnet50(pretrained=False)
+model.fc = torch.nn.Linear(model.fc.in_features, 14)  # Adjust for 14 classes
+model.load_state_dict(torch.load("resnet50_model_hf.pt", map_location=device))
+model.eval()
+
+# Define image transformations
+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])
+])
+
+# Class labels (adjust according to your dataset)
+class_labels = [
+    "Corn___Common_Rust", "Corn___Gray_Leaf_Spot", "Corn___Healthy", "Corn___Northern_Leaf_Blight",
+    "Rice___Brown_Spot", "Rice___Healthy", "Rice___Leaf_Blast", "Rice___Neck_Blast",
+    "Wheat___Brown_Rust", "Wheat___Healthy", "Wheat___Yellow_Rust",
+    "Sugarcane__Red_Rot", "Sugarcane__Healthy", "Sugarcane__Bacterial Blight"
+]
+
+# Prediction function
+def predict(image):
+    image = transform(image).unsqueeze(0).to(device)  # Add batch dimension
+    with torch.no_grad():
+        outputs = model(image)
+        _, predicted_class = torch.max(outputs, 1)
+        return class_labels[predicted_class.item()]
+
+# Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(),
+    title="Crop Disease Classification",
+    description="Upload an image to classify crop diseases using ResNet-50."
+)
+
+interface.launch()