Create app.py

app.py

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+import torch
+import torch.nn as nn
+from torchvision import transforms
+from PIL import Image
+import requests
+from torchvision.models import vgg19
+import gradio as gr
+
+# Define preprocessing
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),  # Resize images to 224x224
+    transforms.ToTensor(),  # Convert images to tensor
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # Normalize using ImageNet stats
+])
+
+# Load trained model
+model = models.vgg19(weights='DEFAULT')
+
+# Adjust the final fully connected layer for binary classification
+num_ftrs = model.classifier[-1].in_features  # Get the number of input features from the last layer
+model.classifier[-1] = nn.Linear(num_ftrs, 2)  # Replace with a new linear layer for binary classification
+
+# Load the saved weights into the model
+model.load_state_dict(torch.load('rice_plant_classification.pth', weights_only=True))  # Ensure this file exists
+model.eval()
+
+# Define class labels
+class_to_label = {0: 'Healthy', 1: 'Unhealthy'}
+
+# Inference function
+def predict(image):
+    # Preprocess the image
+    img = Image.fromarray(image)
+    img = preprocess(img).unsqueeze(0)  # Add batch dimension
+
+    # Perform inference
+    with torch.no_grad():
+        output = model(img)
+        probabilities = torch.softmax(output, dim=1)
+        predicted_class = torch.argmax(probabilities, 1).item()
+        confidence = probabilities[0][predicted_class].item()
+
+    # Return the class label and confidence
+    return class_to_label[predicted_class], f'{confidence * 100:.2f}%'
+
+example_images = ["healthy.jpeg", "unhealthy.jpeg"]
+
+# Create Gradio interface
+interface = gr.Interface(fn=predict, 
+                         inputs="image", 
+                         outputs=[gr.Textbox(label="Prediction"), gr.Textbox(label="Confidence")],
+                         title="Healthy vs Unhealthy Rice Plant Classifier",
+                         description="Upload a rice plant image to classify either it is healthy or unhealthy.",
+                         examples=example_images 
+                        )
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()