| import gradio as gr | |
| from fastai.vision.all import * | |
| import skimage | |
| def get_x(r): return "" | |
| def get_y(r): return r['diagnosis'] | |
| learn = load_learner('model.pkl') | |
| labels = learn.dls.vocab | |
| # Define the mapping from label numbers to descriptions | |
| label_descriptions = { | |
| 0: "No DR", | |
| 1: "Mild", | |
| 2: "Moderate", | |
| 3: "Severe", | |
| 4: "Proliferative DR" | |
| } | |
| def predict(img): | |
| img = PILImage.create(img) | |
| pred,pred_idx,probs = learn.predict(img) | |
| # Use the label_descriptions dictionary to return descriptions instead of numbers | |
| return {label_descriptions[labels[i]]: float(probs[i]) for i in range(len(labels))} | |
| title = "Diabetic Retinopathy Detection" | |
| description = """Detects severity of diabetic retinopathy - | |
| 0 - No DR | |
| 1 - Mild | |
| 2 - Moderate | |
| 3 - Severe | |
| 4 - Proliferative DR | |
| """ | |
| article="<p style='text-align: center'><a href='https://www.kaggle.com/code/josemauriciodelgado/proliferative-retinopathy' target='_blank'>Notebook</a></p>" | |
| # Get a list of all image paths in the test folder | |
| test_folder = "test" # replace with the actual path to your test folder | |
| image_paths = [os.path.join(test_folder, img) for img in os.listdir(test_folder) if img.endswith(('.png', '.jpg', '.jpeg'))] | |
| gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(), | |
| outputs=gr.Label(num_top_classes=5), | |
| examples=image_paths, # set the examples parameter to the list of image paths | |
| article=article, | |
| title=title, | |
| description=description, | |
| ).launch() |