File size: 1,387 Bytes
546f352 0bda39c 546f352 1587eea c10710a 546f352 c10710a 546f352 7669f3a 546f352 7669f3a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 |
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() |