Fix & réorganisation

app.py

@@ -18,8 +18,8 @@ def format_image_type1(image):
 
 def format_image_type2(image):
     image = ImageOps.grayscale(image) # Charge en noir et blanc mais avec une meilleure résolution
-    image = img.resize((128, 128), Image.LANCZOS) # Redimensionner selon la taille attendue
-    image = np.array(img) / 255.0 # Normalisation
+    image = image.resize((128, 128), Image.LANCZOS) # Redimensionner selon la taille attendue
+    image = np.array(image) / 255.0 # Normalisation
     return image_single_fix(image)
 
 def image_single_fix(image_array):
@@ -60,26 +60,32 @@ def predict(image, model_name):
     return_text = ""
     if model_type == "gender_only":
         gender_value = prediction[0][0]
-        gender = "Homme" if gender_value < 0.5 else "Femme" # Genre basé sur probabilité
-        return_text = f"Genre: {gender} ({gender_value} - {get_gender_confidence(gender_value)} certitude)"
+        return_text = display_gender_prediction(gender_value)
     elif model_type == "age_only":
         age_value = prediction[0][0]
-        age = round(prediction[0][0]) # Arroundi à l'entier le plus proche "half away zero"
-        return_text = f"Age: {age} ({age_value})"
+        return_text = display_age_prediction(age_value)
     else: # "gender_age"
-        gender_value = prediction[0][0]
-        gender = "Homme" if gender_value < 0.5 else "Femme" # Genre basé sur probabilité
-        age_value = prediction[0][1]
-        age = round(prediction[0][0])
-        return_text = f"Genre: {gender} ({gender_value} - {get_gender_confidence(gender_value)} certitude)\nAge: {age} ({age_value})"
+        gender_value = prediction[0][0][0]
+        age_value = prediction[1][0][0]
+        return_text = display_gender_prediction(gender_value) + "\n" + display_age_prediction(age_value)
     
     return f"{return_text}"
 
 
 # Fonctions utiles
-def get_gender_confidence(gender_value):
+def get_gender_confidence(gender_value) -> str:
     return f"{round(abs(gender_value - 0.5) * 2 * 100)}%"
 
+def display_gender_prediction(gender_value) -> str:
+    gender = "Homme" if gender_value < 0.5 else "Femme" # Genre basé sur probabilité
+    rounded_gender_value_str = str(round(gender_value, 4))
+    return f"Genre: {gender} ({rounded_gender_value_str} - {get_gender_confidence(gender_value)} certitude)"
+
+def display_age_prediction(age_value) -> str:
+    age = round(age_value) # Arroundi à l'entier le plus proche "half away zero"
+    rounded_age_value_str = str(round(age_value, 3))
+    return f"Age: {age} ({rounded_age_value_str})"
+
 
 # Interface Gradio
 iface = gr.Interface(