inference.py

from PIL import Image
import torch
import torchvision.transforms as transforms
import torchvision.models as models

# Load model
model = models.resnet18(pretrained=False, num_classes=3)
model.load_state_dict(torch.load("pytorch_model.bin", map_location=torch.device("cpu")))
model.eval()

# Preprocessing function
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406],
                         [0.229, 0.224, 0.225])
])

# Labels
labels = ["A", "B", "C", "D", "E", "F", "G"]

# Required function
def predict(image: Image.Image):
    img_tensor = transform(image).unsqueeze(0)
    with torch.no_grad():
        outputs = model(img_tensor)
        probs = torch.nn.functional.softmax(outputs[0], dim=0)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}