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| import datasets | |
| import torch | |
| import numpy as np | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| dataset = datasets.load_dataset("beans") | |
| extractor = AutoFeatureExtractor.from_pretrained("saved_model_files") | |
| model = AutoModelForImageClassification.from_pretrained("saved_model_files") | |
| labels = dataset['train'].features['labels'].names | |
| def classify(input_image): | |
| features = extractor(input_image, return_tensors='pt') | |
| logits = model(features["pixel_values"])[-1] | |
| probability = torch.nn.functional.softmax(logits, dim=-1) | |
| probs = probability[0].detach().numpy() | |
| confidences = {label: float(probs[i]) for i, label in enumerate(labels)} | |
| return confidences | |
| import gradio as gr | |
| interface = gr.Interface(fn=classify, inputs='image', outputs='label', title='Leaf classification on beans dataset', | |
| description='Sample fine-tuning a ViT for bean dataset classification') | |
| interface.launch() | |