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| # -*- coding: utf-8 -*- | |
| """app | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1XX8pCT291obpzL4fc1vu5L_HTG027lle | |
| """ | |
| import gradio as gr | |
| import torch | |
| import datasets | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| 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("saved_model_files") | |
| model = AutoModelForImageClassification.from_pretrained("saved_model_files") | |
| 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 | |
| interface = gr.Interface(classify, inputs='image', outputs='label', title='Bean plant disease classifier', description='Detect diseases in beans leaves using their images.', examples=['bean-plant-example.jpeg', 'non-bean-leaf-example.jpeg']) | |
| interface.launch(debug=False) |