app.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import keras
import gradio as gr

SHAPE = (224, 224, 3)

disease_risk = keras.models.load_model('predictor_Disease_Risk.h5')

def cut_and_resize(image):
  LOW_TOL = 20
  img_bw = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  img_bw[img_bw<=LOW_TOL] = 0
  y_nonzero, x_nonzero = np.nonzero(img_bw)
  image = image[np.min(y_nonzero):np.max(y_nonzero), np.min(x_nonzero): np.max(x_nonzero), ]
  return cv2.resize(image, SHAPE[:2], interpolation = cv2.INTER_LINEAR)
 
def simple_normalizer(X):
  return X / 255.0

def predict (image_path):
  image = simple_normalizer(cut_and_resize(cv2.imread(image_path)))
  result = disease_risk.predict(np.array([image]))[0][0]
  return {'Enferma': float(result), 'Sana': 1 - float(result)}
 
title = 'RetinAI (versión alfa)'
description = 'Modelo de deep learning que permite clasificar imágenes de la retina en patológicas y no patológicas. Primera fase de un proyecto que pretende realizar screening de las principales enfermedades de la retina que producen ceguera. Las imágenes deben tener fondo negro.'
article = 'Demo del proyecto para Saturdays.\nAutores del modelo: [...] '

interface = gr.Interface(
  predict,
  inputs = [gr.outputs.Image()],
  outputs= [gr.outputs.Label(num_top_classes=2, label='Retina')],
  title = title, description = description, article = article,
  theme = 'peach',
  examples = ['82.png', '15.png', '61.png', '37.png', '631.png', '23.png', '8.png']
)

interface.launch()