app.py

import gradio as gr
from huggingface_hub import InferenceAPI

# Specify the model name
model_repo = "jdelgado2002/diabetic_retinopathy_detection"

# Initialize the InferenceAPI client
api = InferenceAPI(model_repo)

labels = ["No DR", "Mild", "Moderate", "Severe", "Proliferative DR"]

def predict(img):
    # Convert the image to a base64 string
    img_str = gr.inputs.Image.to_base64(img, ext="jpeg")
    
    # Perform inference
    inputs = {"inputs": img_str}
    result = api(inputs)
    
    # Convert the result to the expected format
    return {labels[i]: float(result[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()