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| import torch | |
| from datasets import load_dataset | |
| import gradio as gr | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| # This should be the same as the first line of Python code in this Colab notebook | |
| dataset = load_dataset('beans') | |
| feature_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="Leaf Spot Classifier", | |
| description="Classify the leaf into one of: angular_leaf_spot, bean_rust, healthy", | |
| examples=["examples/healthy_test.15.jpg", "examples/angular_leaf_spot_test.0.jpg", "examples/bean_rust_test.32.jpg"], | |
| cache_examples=False, | |
| fn=classify, | |
| inputs=gr.Image(shape=(224, 224)), | |
| outputs=gr.Label(num_top_classes=3), | |
| ) | |
| interface.launch(debug=True) | |