Update app.py

app.py

@@ -2,21 +2,35 @@ 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
 
-os.system("tar -zxvf examples.tar.gz")
+# Charger le modèle
+model = load_model('/content/drive/MyDrive/T-DEV-810/model_cv.h5')
 
-model = tf.keras.models.load_model('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):
-    img = img.reshape(1, 100, 100, 3)
-    prediction = np.around(model.predict(img)[0], decimals=0)[0]
-
-    if prediction == 1:
-        return "Pneumonia Detected!"
+    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',