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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() |