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| import gradio as gr | |
| from datasets import load_dataset | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| dataset = 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(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 | |
| interface = gr.Interface( | |
| title="Plant Health Identifier", | |
| description="Upload an image of plant leaf to determine if it is infected or healthy", | |
| #examples= ['beanrot.jpeg'], | |
| fn=classify, | |
| inputs="image", | |
| outputs="label", | |
| ) | |
| interface.launch(debug=True) |