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| import transformers | |
| import gradio as gr | |
| import datasets | |
| import torch | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| # from transformers import ViTFeatureExtractor, ViTForImageClassification | |
| dataset = datasets.load_dataset('beans') | |
| extractor = AutoFeatureExtractor.from_pretrained("suresh-subramanian/beans-classification") | |
| model = AutoModelForImageClassification.from_pretrained("suresh-subramanian/beans-classification") | |
| # feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224") | |
| labels = dataset['train'].features['labels'].names | |
| def classify(im): | |
| features = extractor(im, return_tensors='pt') | |
| with torch.no_grad(): | |
| 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 | |
| # examples = [["powdery mildew.jpg"], ["375010.jpg"]] | |
| # Set gradio interface | |
| gr_interface = gr.Interface(classify, inputs='image', outputs='label', title='Bean Classification', description='Monitor your crops health in easier way') | |
| # Launch gradio | |
| gr_interface.launch(debug=True) |