Update app.py

app.py

@@ -124,17 +124,13 @@ def classify_zip_and_analyze_color(zip_file):
                 face_info = ""
                 try:
                     img_cv2 = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-                    faces = DeepFace.analyze(
-                        img_cv2, 
-                        actions=["age", "gender", "emotion"], 
-                        enforce_detection=False
-                    )
-                    if isinstance(faces, list):
+                    faces = DeepFace.analyze(img_cv2, actions=["age", "gender", "emotion"], enforce_detection=False)
+                    if isinstance(faces, list):  # multiple faces
                         for f in faces:
                             face_info += f"Age: {f['age']}, Gender: {f['gender']}, Gender Confidence: {f['gender_confidence']*100:.2f}, Emotion: {f['dominant_emotion']}; "
-                    else:
+                    else:  # single face
                         face_info = f"Age: {faces['age']}, Gender: {faces['gender']}, Gender Confidence: {faces['gender_confidence']*100:.2f}, Emotion: {faces['dominant_emotion']}"
-                except Exception:
+                except Exception as e:
                     face_info = "No face detected"
 
                 results.append((
@@ -185,7 +181,7 @@ def classify_zip_and_analyze_color(zip_file):
     plot2_img = Image.open(buf2)
 
     # ---------------------------
-    # Extract age and weighted gender (confidence ≤ 90%)
+    # Extract age and gender (confidence ≤ 80%)
     # ---------------------------
     ages = []
     gender_confidence = {"Man": 0, "Woman": 0}
@@ -199,30 +195,30 @@ def classify_zip_and_analyze_color(zip_file):
                     age_part = face_str.split(",")[0]
                     age = int(age_part.replace("Age:", "").strip())
                     ages.append(age)
-
-                    # Gender
+                    
+                    # Gender and confidence
                     gender_part = face_str.split(",")[1]
                     gender = gender_part.replace("Gender:", "").strip()
-
+                    
                     # Extract confidence
                     conf = 1.0
                     for part in face_str.split(","):
                         if "Gender Confidence:" in part:
-                            conf = float(part.split("Gender Confidence:")[1].strip()) / 100
+                            conf = float(part.split("Gender Confidence:")[1].strip()) / 100  # convert % to 0-1
 
-                    # Only include if confidence ≤ 0.9
-                    if conf <= 0.9:
+                    # Only include if confidence ≤ 0.8
+                    if conf <= 0.8:
                         if gender in gender_confidence:
                             gender_confidence[gender] += conf
                         else:
                             gender_confidence[gender] = conf
 
     # ---------------------------
-    # Plot 3: Gender distribution (weighted ≤ 90%)
+    # Plot 3: Gender distribution (confidence ≤ 80%)
     # ---------------------------
     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 (Confidence ≤ 80%)")
     ax3.set_ylabel("Sum of Confidence")
     buf3 = io.BytesIO()
     plt.savefig(buf3, format="png")
@@ -257,7 +253,7 @@ demo = gr.Interface(
         gr.File(label="Download XLSX"),
         gr.Image(type="pil", label="Basic Color Frequency"),
         gr.Image(type="pil", label="Top Prediction Distribution"),
-        gr.Image(type="pil", label="Gender Distribution (Weighted ≤90%)"),
+        gr.Image(type="pil", label="Gender Distribution (≤80% Confidence)"),
         gr.Image(type="pil", label="Age Distribution"),
     ],
     title="Image Classifier with Color & Face Analysis",
@@ -266,3 +262,4 @@ demo = gr.Interface(
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)
+