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import gradio as gr
import tensorflow as tf
import numpy as np
import os
import tensorflow as tf
import numpy as np
from keras.models import load_model
from tensorflow.keras.utils import load_img
# Charger le modèle
model = load_model('model_cv.h5')
def format_decimal(value):
decimal_value = format(value, ".2f")
return decimal_value
def detect(img):
img = np.expand_dims(img, axis=0)
img = img/255
prediction = model.predict(img)[0]
if format_decimal(prediction[0]) >= "0.5":
return "Bactérie détectée"
if format_decimal(prediction[1]) >= "0.5":
return "Poumon sain"
if format_decimal(prediction[2]) >= "0.5":
return "Virus détecté"
# result = detect(img)
# print(result)
os.system("tar -zxvf examples.tar.gz")
examples = ['examples/n1.jpeg', 'examples/n2.jpeg', 'examples/n3.jpeg', 'examples/n4.jpeg', 'examples/n5.jpeg',
'examples/n6.jpeg', 'examples/n7.jpeg', 'examples/n8.jpeg', 'examples/p6.jpeg', 'examples/p7.jpeg',]
input = gr.inputs.Image(shape=(100,100))
title = "PneumoDetect: Pneumonia Detection from Chest X-Rays"
iface = gr.Interface(fn=detect, inputs=input, outputs="text",examples = examples, examples_per_page=20, title=title)
iface.launch(inline=False)
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