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| from datasets import load_dataset | |
| import gradio as gr | |
| import torch | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| # Define dataset, feature extractor, and model | |
| 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 = 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( | |
| fn=classify, inputs="image", outputs="label", title="Bean leaf classification" | |
| ) | |
| interface.launch(debug=True) | |