Update app.py

app.py

@@ -1,24 +1,23 @@
 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
+from tensorflow.keras.utils import load_img, img_to_array
 
 # Charger le modèle
-
 model = load_model('best_model.h5')
 
-
 def format_decimal(value):
     decimal_value = format(value, ".2f")
     return decimal_value
-    
+
 def detect(img):
+    # Prétraiter l'image
+    img = img.resize((256, 256))  # Redimensionner l'image
+    img = img_to_array(img)
     img = np.expand_dims(img, axis=0)
-    img = img/255
+    img = img / 255.0  # Normaliser les valeurs de l'image
+
     # Faire une prédiction
     prediction = model.predict(img)[0]
 
@@ -39,22 +38,11 @@ def detect(img):
 
     return texte
 
-
-
-# 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 = "Orisha"
 
-iface = gr.Interface(fn=detect, inputs=input, outputs=[gr.Textbox(label="Classe", lines=10)],
-                     #examples = examples,
-                     #examples_per_page=20,
+iface = gr.Interface(fn=detect, 
+                     inputs=gr.Image(type="pil", shape=(256, 256)), 
+                     outputs=gr.Textbox(label="Classe", lines=10),
                      title=title)
-iface.launch(inline=False)
+
+iface.launch(inline=False)