Update app.py

app.py

@@ -121,21 +121,27 @@ def classify_zip_and_analyze_color(zip_file):
                 # ---------------------------
                 # Face detection & characterization
                 # ---------------------------
-                face_info = ""
+                faces_data = []
                 try:
                     img_cv2 = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-                    faces = DeepFace.analyze(
-                        img_cv2,
-                        actions=["age", "gender", "emotion"],
-                        enforce_detection=False
+                    detected_faces = DeepFace.analyze(
+                        img_cv2, actions=["age", "gender", "emotion"], enforce_detection=False
                     )
-                    if isinstance(faces, list):
-                        for f in faces:
-                            face_info += f"Age: {f['age']}, Gender: {f['gender']}, Emotion: {f['dominant_emotion']}; "
+                    if isinstance(detected_faces, list):
+                        for f in detected_faces:
+                            faces_data.append({
+                                "age": f["age"],
+                                "gender": f["gender"],  # dict of probabilities
+                                "emotion": f["dominant_emotion"]
+                            })
                     else:
-                        face_info = f"Age: {faces['age']}, Gender: {faces['gender']}, Emotion: {faces['dominant_emotion']}"
+                        faces_data.append({
+                            "age": detected_faces["age"],
+                            "gender": detected_faces["gender"],  # dict
+                            "emotion": detected_faces["dominant_emotion"]
+                        })
                 except Exception:
-                    face_info = "No face detected"
+                    faces_data = []
 
                 results.append((
                     fname,
@@ -143,7 +149,7 @@ def classify_zip_and_analyze_color(zip_file):
                     ", ".join([p[1] for p in preds]),
                     hex_color,
                     basic_color,
-                    face_info
+                    faces_data
                 ))
 
     # Build dataframe
@@ -185,46 +191,29 @@ def classify_zip_and_analyze_color(zip_file):
     plot2_img = Image.open(buf2)
 
     # ---------------------------
-    # Extract age and weighted gender (handles gender dict)
+    # Extract age and weighted gender (confidence ≤ 0.9)
     # ---------------------------
     ages = []
     gender_confidence = {"Man": 0, "Woman": 0}
 
-    for info in df["Face Info"]:
-        if info != "No face detected":
-            for face_str in info.split(";"):
-                face_str = face_str.strip()
-                if face_str:
-                    # Age
-                    age_part = face_str.split(",")[0]
-                    age = int(age_part.replace("Age:", "").strip())
-                    ages.append(age)
-
-                    # Gender parsing
-                    gender_part = face_str.split(",")[1]
-                    gender_dict_str = gender_part.replace("Gender:", "").strip()
-                    try:
-                        gender_dict = eval(gender_dict_str)
-                    except:
-                        continue
-
-                    # Take the highest probability as gender
-                    gender = max(gender_dict, key=gender_dict.get)
-                    conf = float(gender_dict[gender]) / 100  # convert % to 0-1
-
-                    # Weight capped at 0.9
-                    weight = min(conf, 0.9)
-                    if gender in gender_confidence:
-                        gender_confidence[gender] += weight
-                    else:
-                        gender_confidence[gender] = weight
+    for face_list in df["Face Info"]:
+        for face in face_list:  # each face is a dict
+            ages.append(face["age"])
+            gender_dict = face["gender"]  # dict of probabilities
+            gender = max(gender_dict, key=gender_dict.get)
+            conf = float(gender_dict[gender]) / 100  # convert % to 0-1
+            weight = min(conf, 0.9)
+            if gender in gender_confidence:
+                gender_confidence[gender] += weight
+            else:
+                gender_confidence[gender] = weight
 
     # ---------------------------
-    # Plot 3: Gender distribution (weighted ≤ 90%)
+    # Plot 3: Gender distribution (weighted ≤ 0.9)
     # ---------------------------
     fig3, ax3 = plt.subplots()
     ax3.bar(gender_confidence.keys(), gender_confidence.values(), color=["lightblue", "pink"])
-    ax3.set_title("Gender Distribution (Weighted ≤ 90% Confidence)")
+    ax3.set_title("Gender Distribution (Weighted ≤90% Confidence)")
     ax3.set_ylabel("Sum of Confidence")
     buf3 = io.BytesIO()
     plt.savefig(buf3, format="png")