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('/content/drive/MyDrive/T-DEV-810/model_cv.h5')

# Charger l'image
img = load_img('/content/drive/MyDrive/T-DEV-810/DS/test/NORMAL/IM-0063-0001.jpeg', target_size=(100, 100))
# Prétraiter l'image
# img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)
img = img/255

def detect(img):
    prediction = model.predict(img)[0]
    print(prediction)
    if prediction[0] <= 0.80:
        return "Pneumonia  Detected!"

    return "Pneumonia Not Detected!"


result = detect(img)
print(result)
os.system("tar -zxvf examples.tar.gz")


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

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',
            'examples/p1.jpeg', 'examples/p2.jpeg', 'examples/p3.jpeg', 'examples/p4.jpeg', 'examples/p8.jpeg']

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)