Create app.py

app.py

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+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+import gradio as gr
+from PIL import Image
+
+# Define the model architecture (must match the saved model)
+class_names = ["cordana", "healthy", "pestalotiopsis", "sigatoka"]
+def load_model():
+    model = models.alexnet(pretrained=False)
+    num_ftrs = model.classifier[6].in_features
+    model.classifier[6] = nn.Linear(num_ftrs, len(class_names))  # Adjust this for your number of classes
+
+    # Load the model weights
+    model.load_state_dict(torch.load('model_alexnet.pth'))
+    model.eval()  # Set to evaluation mode
+
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    return model, device
+
+model_alexnet, device = load_model()
+
+# Image transformations
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+# Prediction function
+def predict_image(image):
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+    image = transform(image).unsqueeze(0)  # Add batch dimension
+    image = image.to(device)
+    
+    with torch.no_grad():
+        outputs = model_alexnet(image)
+        _, predicted = torch.max(outputs, 1)
+        predicted = predicted.cpu().numpy()
+    
+    return class_names[predicted[0]]  # Adjust this if needed
+
+    
+iface = gr.Interface(fn=predict_image, inputs="image", outputs="label",
+                     description="This model is a fine-tuned version of AlexNet specifically designed to identify four types of diseases in banana tree leaves. It can classify the leaves as Cordana, Healthy, Pestalotiopsis, or Sigatoka. Upload a photo of a banana leaf and the model will help you determine its health condition.",
+                     examples=[
+        'data/test/cordana/1.jpeg',
+        'data/test/healthy/5.jpeg',
+        'data/test/pestalotiopsis/5.jpeg',
+        'data/test/sigatoka/1.jpeg'
+    ]
+)
+
+iface.launch()