models.py

import torch
from transformers import SwinForImageClassification, AutoImageProcessor
from PIL import Image
import matplotlib.pyplot as plt

from transformers import SwinForImageClassification, AutoImageProcessor

class GarbageClassifier:
    def __init__(self, model_dir, num_labels=4):
        self.labels = ['glass', 'metal', 'paper', 'plastic']
        self.model, self.processor = self.load_model_and_processor(model_dir, num_labels)

    def load_model_and_processor(self, model_dir, num_labels):
        model = SwinForImageClassification.from_pretrained(model_dir, num_labels=num_labels)
        processor = AutoImageProcessor.from_pretrained(model_dir)
        return model, processor


    def evaluate_image(self, image_path):
        image = Image.open(image_path)
        inputs = self.processor(images=image, return_tensors="pt")
        
        with torch.no_grad():
            outputs = self.model(**inputs)
            logits = outputs.logits
            predicted_class = logits.argmax(-1).item()
        
        plt.imshow(image)
        plt.title(f"Predicted Class: {self.labels[predicted_class]}")
        plt.axis('off')
        plt.show()