| import numpy as np | |
| import gradio as gr | |
| from transformers import AutoImageProcessor, AutoModelForImageClassification | |
| from PIL import Image | |
| processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224") | |
| model = AutoModelForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224") | |
| def classifier(image): | |
| image = Image.open(image.raw) | |
| inputs = processor(images=image, return_tensors="pt") | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| # model predicts one of the 1000 ImageNet classes | |
| predicted_class_idx = logits.argmax(-1).item() | |
| return model.config.id2label[predicted_class_idx] | |
| food = gr.Interface( | |
| fn=classifier, | |
| inputs=gr.Image(type="pil"), | |
| outputs="text", | |
| title = "what's your eating?", | |
| description = "A simple model for food classification" | |
| ) | |
| food.launch() |