| import datasets | |
| import torch | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| dataset = datasets.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 | |
| import gradio as gr | |
| Instruction = "Submit bean-leaf images with different leaf conditions" | |
| title="Bean-leaf-disease Image classification demo" | |
| description = "Drop an Input image to classify, Observe the model prediction across 3 distinct categories." | |
| article = """ | |
| - Select an image from the examples provided as demo image | |
| - Click submit button to make Image classification | |
| - Click clear button to try new Image for classification | |
| """ | |
| interface = gr.Interface( | |
| classify, | |
| inputs='image', | |
| outputs='label', | |
| instructuction = Instruction, | |
| title = title, | |
| description = description, | |
| article = article, | |
| examples=["image1.jpg", | |
| "image2.jpg", | |
| "image3.jpg", | |
| "image4.jpg"] | |
| ) | |
| interface.launch(debug=True) | |