Update app.py

app.py

@@ -99,24 +99,31 @@ def classify_zip_and_analyze_color(zip_file):
             faces_data = []
             try:
                 img_cv2 = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-                detected_faces = DeepFace.analyze(img_cv2, actions=["age","gender","emotion"], enforce_detection=False)
-                if not isinstance(detected_faces, list):
+                detected_faces = DeepFace.analyze(
+                    img_cv2, actions=["age","gender","emotion"], enforce_detection=False
+                )
+
+                # Ensure detected_faces is a list
+                if isinstance(detected_faces, dict):
                     detected_faces = [detected_faces]
+
                 for f in detected_faces:
-                    gender = f["gender"].lower()
+                    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"
+
                     faces_data.append({
-                        "age": f["age"],
+                        "age": f.get("age", -1),
                         "gender": gender_fr,
-                        "emotion": f["dominant_emotion"]
+                        "emotion": f.get("dominant_emotion", "Unknown")
                     })
+
             except:
-                faces_data=[]
+                faces_data = []
 
             faces_str = "; ".join([
                 f"Age: {face['age']}, Gender: {face['gender']}, Emotion: {face['emotion']}"
@@ -157,17 +164,8 @@ def classify_zip_and_analyze_color(zip_file):
     buf2 = io.BytesIO(); plt.savefig(buf2, format="png", bbox_inches="tight"); plt.close(fig2); buf2.seek(0); plot2_img = Image.open(buf2)
 
     # Gender and age
-    ages_male = []
-    ages_female = []
-    for face_list in df["Face Info"]:
-        if face_list.strip()=="":
-            continue
-        for face_str in face_list.split("; "):
-            parts = face_str.split(", ")
-            age = int(parts[0].split(": ")[1])
-            gender = parts[1].split(": ")[1]
-            if gender=="Homme": ages_male.append(age)
-            elif gender=="Femme": ages_female.append(age)
+    ages_male = [int(f.split(", ")[0].split(": ")[1]) for row in df["Face Info"] for f in row.split("; ") if "Homme" in f]
+    ages_female = [int(f.split(", ")[0].split(": ")[1]) for row in df["Face Info"] for f in row.split("; ") if "Femme" in f]
 
     gender_counts = {"Homme": len(ages_male), "Femme": len(ages_female)}