classifier_inference.py

import torch
from torchvision import models, transforms
from PIL import Image

# ==== Configuration ====
MODEL_PATH = 'models/cat_dog_classifier.pth'
CLASS_NAMES = ['cat', 'dog']  # Make sure this order matches your training dataset

# ==== Load Model ====
model = models.mobilenet_v2(pretrained=False)
model.classifier[1] = torch.nn.Linear(model.last_channel, 2)
model.load_state_dict(torch.load(MODEL_PATH, map_location='cpu'))
model.eval()  # Set to evaluation mode

# ==== Image Preprocessing ====
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]),
])

# ==== Inference Function ====
def predict(image_path):
    image = Image.open(image_path).convert('RGB')
    input_tensor = transform(image).unsqueeze(0)  # Add batch dimension

    with torch.no_grad():
        outputs = model(input_tensor)
        predicted_class = outputs.argmax(1).item()
        confidence = torch.softmax(outputs, dim=1)[0][predicted_class].item()

    return {
        'class': CLASS_NAMES[predicted_class],
        'confidence': confidence
    }

if __name__ == "__main__":
    print(predict('raw_data/train/dog.0.jpg'))