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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 = load_dataset('beans', 'full_size') | |
| extractor = AutoFeatureExtractor.from_pretrained("saved_model_files") | |
| model = AutoModelForImageClassification.from_pretrained("saved_model_files") | |
| labels = dataset['train'].features['labels'].names | |
| def classify(im): | |
| features = feature_extractor(im, 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 | |
| description = "Bean leaf health classification wit Google's ViT" | |
| title = "Bean Leaf Health Check" | |
| examples = [["'angular_leaf_spot': 0.9999030828475952, 'bean_rust': 5.320278796716593e-05, 'healthy': 4.378804806037806e-05"]] | |
| gr_interface = gr.Interface(classify, inputs='image', outputs='label', title='Bean Classification', description='Monitor your crops health in easier way') | |
| gr_interface.launch(debug=True) |