| 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) |
|
|
