Update app.py

app.py

@@ -60,6 +60,24 @@ def get_dominant_color(image,num_colors=5):
     hex_color = f"#{dominant_color[0]:02x}{dominant_color[1]:02x}{dominant_color[2]:02x}"
     return dominant_color, hex_color
 
+# ---------------------------
+# Gender normalization
+# ---------------------------
+def normalize_gender(raw_gender):
+    if raw_gender is None:
+        return "Inconnu"
+    g = str(raw_gender).lower()
+    if g in ["man", "male", "m"]:
+        return "Homme"
+    elif g in ["woman", "female", "f"]:
+        return "Femme"
+    elif g in ["homme"]:
+        return "Homme"
+    elif g in ["femme"]:
+        return "Femme"
+    else:
+        return "Inconnu"
+
 # ---------------------------
 # Core analysis
 # ---------------------------
@@ -103,25 +121,17 @@ def classify_zip_and_analyze_color(zip_file):
                     img_cv2, actions=["age","gender","emotion"], enforce_detection=False
                 )
 
-                # Ensure detected_faces is a list
+                # Ensure we have a list
                 if isinstance(detected_faces, dict):
                     detected_faces = [detected_faces]
 
                 for f in detected_faces:
-                    gender = str(f.get("gender", "Unknown")).lower()
-                    if gender in ["man", "male"]:
-                        gender_fr = "Homme"
-                    elif gender in ["woman", "female"]:
-                        gender_fr = "Femme"
-                    else:
-                        gender_fr = "Inconnu"
-
+                    gender_fr = normalize_gender(f.get("gender") or f.get("dominant_gender"))
                     faces_data.append({
                         "age": f.get("age", -1),
                         "gender": gender_fr,
                         "emotion": f.get("dominant_emotion", "Unknown")
                     })
-
             except:
                 faces_data = []