Update app.py

app.py

@@ -11,6 +11,9 @@ import pandas as pd
 from PIL import Image
 import plotly.express as px
 import time
+import os
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+
 
 theme = gr.themes.Soft(
     primary_hue="purple",
@@ -156,35 +159,38 @@ def _update_progress(progress, value, desc=""):
 # ---- PREDICT SINGLE ----
 from tensorflow.keras.preprocessing import image
 
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
 def predict_single(img_input, weights=(0.45, 0.25, 0.3), normalize=True):
     print("🔍 DEBUG GRADIO - Début de la prédiction")
 
     # Chargement et pré-traitement avec Keras (comme en local)
     if isinstance(img_input, str):
         # Chargement depuis un chemin
-        img_raw_x = image.load_img(img_input, target_size=(299, 299), color_mode="rgb")
-        img_raw_r = image.load_img(img_input, target_size=(224, 224), color_mode="rgb")
-        img_raw_d = image.load_img(img_input, target_size=(224, 224), color_mode="rgb")
+        img_path = img_input
+        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 (img_input est déjà une image)
-        # Conversion en array puis rechargement avec Keras pour uniformiser
+        # Cas d'upload via interface Gradio (img_input est une image PIL)
         temp_path = "temp_debug_image.jpg"
-        img_input.save(temp_path)  # Sauvegarde temporaire
-        img_raw_x = image.load_img(temp_path, target_size=(299, 299), color_mode="rgb")
-        img_raw_r = image.load_img(temp_path, target_size=(224, 224), color_mode="rgb")
-        img_raw_d = image.load_img(temp_path, target_size=(224, 224), color_mode="rgb")
+        img_input.save(temp_path)
+        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)  # Nettoyage
+        os.remove(temp_path)
+
+    # Conversion en arrays avec le même type de données que local (float32)
+    array_x = image.img_to_array(img_raw_x).astype(np.float32)
+    array_r = image.img_to_array(img_raw_r).astype(np.float32)
+    array_d = image.img_to_array(img_raw_d).astype(np.float32)
 
     print(f"📸 Images loaded:")
-    print(f"   Xception (299x299): {np.array(img_raw_x).shape}")
-    print(f"   ResNet (224x224): {np.array(img_raw_r).shape}")
-    print(f"   DenseNet (224x224): {np.array(img_raw_d).shape}")
-
-    # Conversion en arrays
-    array_x = image.img_to_array(img_raw_x)
-    array_r = image.img_to_array(img_raw_r)
-    array_d = image.img_to_array(img_raw_d)
+    print(f"   Xception (299x299): {array_x.shape}")
+    print(f"   ResNet (224x224): {array_r.shape}")
+    print(f"   DenseNet (224x224): {array_d.shape}")
 
     print(f"🔧 Arrays avant preprocessing:")
     print(f"   X shape: {array_x.shape}, dtype: {array_x.dtype}, range: [{array_x.min()}, {array_x.max()}]")