vit_model_test.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
from transformers import ViTForImageClassification
from PIL import Image

class CustomModel:
    def __init__(self):
        # Explicitly set the device to CPU
        self.device = torch.device('cpu')

        # Load the pre-trained ViT 
        self.model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224').to(self.device)
        self.model.classifier = nn.Linear(self.model.config.hidden_size, 2).to(self.device)

        # Load model weights
        self.model.load_state_dict(torch.load('trained_model.pth', map_location=self.device, weights_only=True))
        self.model.eval()

        # Resize the image and make it a tensor (add dimension)
        self.preprocess = transforms.Compose([
            transforms.Resize((224, 224)),
            transforms.ToTensor()
        ])

    def predict(self, image: Image.Image):
        # Preprocess the image
        image = self.preprocess(image).unsqueeze(0).to(self.device)  
        
        # Perform inference
        with torch.no_grad():
            outputs = self.model(image)
            logits = outputs.logits
            probabilities = F.softmax(logits, dim=1)
            confidences, predicted = torch.max(probabilities, 1)
            predicted_label = predicted.item()
            confidence = confidences.item() * 100  # Convert to percentage format

        return predicted_label, confidence