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| import datasets | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| import numpy as np | |
| import gradio as gr | |
| import torch | |
| dataset = datasets.load_dataset("beans") # This should be the same as the first line of Python code in this Colab notebook | |
| extractor = AutoFeatureExtractor.from_pretrained("andresgtn/vit-base-bean-health-classifier") | |
| model = AutoModelForImageClassification.from_pretrained("andresgtn/vit-base-bean-health-classifier") | |
| # add to cuda? | |
| #model.eval() | |
| #model.to(device) | |
| labels = dataset['train'].features['labels'].names | |
| def classify(im): | |
| features = extractor(im, return_tensors='pt') | |
| #features.to(device) # move to gpu as model, if available | |
| with torch.no_grad(): | |
| logits = model(**features).logits | |
| probability = torch.nn.functional.softmax(logits, dim=-1) | |
| #probs = probability[0].to('cpu').detach().numpy() | |
| probs = probability[0].detach().numpy() | |
| confidences = {label: float(probs[i]) for i, label in enumerate(labels)} | |
| return confidences | |
| interface = gr.Interface(classify, gr.Image(shape=(200, 200)), 'text') | |
| #demo.launch() | |
| interface.launch(debug=False) |