app.py

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()