predict.py

import torch
from torchvision import transforms
from PIL import Image
from model import WasteCNN  # Import the model architecture

def predict_waste(image_path):
    # Load the model
    model = WasteCNN()
    model.load_state_dict(torch.load('waste_classifier.pth', map_location=torch.device('cpu')))
    model.eval()

    # Prepare the image
    transform = transforms.Compose([
        transforms.Resize((128, 128)),
        transforms.ToTensor(),
    ])
    
    image = Image.open(image_path).convert('RGB')
    image = transform(image).unsqueeze(0)  # Add batch dimension

    # Make prediction
    with torch.no_grad():
        output = model(image)
        _, predicted = torch.max(output, 1)
        
    return "Dry Waste" if predicted.item() == 0 else "Wet Waste"

if __name__ == "__main__":
    # Example usage
    image_path = input("Enter the path to your waste image: ")
    result = predict_waste(image_path)
    print(f"Prediction: {result}")