| import gradio as gr | |
| from fastai.vision.all import * | |
| import skimage | |
| train_path = Path("/kaggle/input/aptos2019/train_images") | |
| test_path = Path("/kaggle/input/aptos2019/test_images") | |
| train_df = pd.read_csv("/kaggle/input/aptos2019/train_1.csv") | |
| def get_x(r): | |
| filename = f"{r['id_code']}.png" | |
| file_path = train_path / filename | |
| if os.path.exists(file_path): | |
| return str(file_path) | |
| else: | |
| return "/kaggle/input/aptos2019/train_images/train_images/1ae8c165fd53.png" | |
| def get_y(r): return r['diagnosis'] | |
| learn = load_learner('model.pkl') | |
| labels = learn.dls.vocab | |
| def predict(img): | |
| img = PILImage.create(img) | |
| pred,pred_idx,probs = learn.predict(img) | |
| return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
| title = "Proliferative 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>" | |
| interpretation='default' | |
| enable_queue=True | |
| gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=6),title=title,description=description,article=article,interpretation=interpretation,enable_queue=enable_queue).launch() |