Spaces:
Runtime error
Runtime error
# Import dependencies | |
from keras.models import load_model | |
from PIL import Image, ImageOps | |
import numpy as np | |
import gradio as gr | |
# Definition of the main function for predictions | |
def predict_nevus(image): | |
# Load the model | |
model = load_model('keras_model.h5') | |
# Create the array of the right shape to feed into the keras model | |
# The 'length' or number of images you can put into the array is | |
# determined by the first position in the shape tuple, in this case 1. | |
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32) | |
#turn the image into a numpy array | |
image_array = np.asarray(image) | |
# Normalize the image | |
normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1 | |
# Load the image into the array | |
data[0] = normalized_image_array | |
# run the inference | |
prediction = model.predict(data) | |
return { | |
'Melanoma': float(prediction[0][0]), | |
'Lunar': float(prediction[0][1]) | |
} | |
# Deploy with Gradio | |
examples = [ | |
['2.jpg'], | |
['37.jpg'], | |
['186.jpg'] | |
] | |
article_file = open("article.md", "r") | |
article = article_file.read() | |
iface = gr.Interface( | |
fn=predict_nevus, | |
inputs=gr.inputs.Image(shape=(224, 224)), | |
outputs="label", | |
title="Detector de melanomas", | |
description="Herramienta online que utiliza inteligencia artificial para detectar posibles melanomas en fotograf铆as de lunares.", | |
article=article, | |
examples=examples, | |
allow_flagging='never', | |
theme="peach" | |
) | |
iface.launch(debug=True) |