Spaces:
Sleeping
Sleeping
File size: 1,402 Bytes
e81879a 3559626 05c62eb dc4e72f e81879a dc4e72f ba8079a d08200f 2679436 c7429e9 94fe01a 05c62eb ba8079a dc4e72f d08200f 4ddb435 fb887f7 dc4e72f 07505b0 dc4e72f 2dfea56 ad0e7fc 4ddb435 05c62eb ba540df 05c62eb 8862ebf 05c62eb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
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 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 prediction[0] <= 0.80:
# return "Pneumonia Detected!"
# return "Pneumonia Not Detected!"
if format_decimal(prediction[0]) >= "0.5":
return "Risque d'infection bactérienne"
if format_decimal(prediction[1]) >= "0.5":
return "Poumon sain"
if format_decimal(prediction[2]) >= "0.5":
return "Risque d'infection biologique"
# 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: Detection de pneumonie par x-ray"
iface = gr.Interface(fn=detect, inputs=input, outputs="text",examples = examples, examples_per_page=20, title=title)
iface.launch(inline=False)
|