app.py

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_multi.h5')



def detect(img):
    prediction = model.predict(img)[0]
    print(prediction)
    
    def format_decimal(value):
        decimal_value = format(value, ".2f")
        return decimal_value
    
    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")


input = gr.inputs.Image(shape=(100))



title = "PneumoDetect: Pneumonia Detection from Chest X-Rays"

iface = gr.Interface(fn=detect, inputs=input, outputs="text", examples_per_page=20, title=title)
iface.launch(inline=False)