Update app.py

app.py

@@ -6,7 +6,7 @@ import tensorflow as tf
 import keras
 from keras.models import Model
 from keras.preprocessing import image
-from tensorflow.keras.preprocessing import image
+#from tensorflow.keras.preprocessing import image
 from huggingface_hub import hf_hub_download
 import pandas as pd
 from PIL import Image
@@ -172,16 +172,16 @@ def predict_single(img_input, weights=(0.45, 0.25, 0.3), normalize=True):
     # Chargement et pré-traitement avec Keras (comme en local)
     if isinstance(img_input, str):
         img_path = img_input
-        img_raw_x = image.load_img(img_path, target_size=(299, 299), interpolation="bilinear")
-        img_raw_r = image.load_img(img_path, target_size=(224, 224), interpolation="bilinear")
-        img_raw_d = image.load_img(img_path, target_size=(224, 224), interpolation="bilinear")
+        img_raw_x = image.load_img(img_path, target_size=(299, 299))
+        img_raw_r = image.load_img(img_path, target_size=(224, 224))
+        img_raw_d = image.load_img(img_path, target_size=(224, 224))
     else:
         # Cas d'upload via interface Gradio
         temp_path = "temp_debug_image.jpg"
         img_input.save(temp_path)
-        img_raw_x = image.load_img(temp_path, target_size=(299, 299), interpolation="bilinear")
-        img_raw_r = image.load_img(temp_path, target_size=(224, 224), interpolation="bilinear")
-        img_raw_d = image.load_img(temp_path, target_size=(224, 224), interpolation="bilinear")
+        img_raw_x = image.load_img(temp_path, target_size=(299, 299))
+        img_raw_r = image.load_img(temp_path, target_size=(224, 224))
+        img_raw_d = image.load_img(temp_path, target_size=(224, 224))
         import os
         os.remove(temp_path)