Update app.py

app.py

@@ -109,9 +109,9 @@ def classify_zip_and_analyze_color(zip_file):
                 except Exception:
                     continue
 
-                # Thumbnail for gallery
+                # Thumbnail for gallery (higher-quality)
                 thumb = image.copy()
-                thumb.thumbnail((300, 300))
+                thumb = thumb.resize((200, 200), Image.LANCZOS)
                 thumbnails.append(thumb)
 
                 # Classification
@@ -196,7 +196,7 @@ def classify_zip_and_analyze_color(zip_file):
     buf2.seek(0)
     plot2_img = Image.open(buf2)
 
-    # 3. Gender distribution
+    # 3. Gender distribution (weighted)
     ages = []
     gender_confidence = {"Man": 0, "Woman": 0}
     for face_list in df["Face Info"]:
@@ -221,12 +221,26 @@ def classify_zip_and_analyze_color(zip_file):
     buf3.seek(0)
     plot3_img = Image.open(buf3)
 
-    # 4. Age distribution
+    # 4. Age distribution by gender
+    ages_men = []
+    ages_women = []
+    for face_list in df["Face Info"]:
+        for face in face_list:
+            age = face["age"]
+            gender_dict = face["gender"]
+            gender = max(gender_dict, key=gender_dict.get)
+            if gender.lower() == "man":
+                ages_men.append(age)
+            else:
+                ages_women.append(age)
+
     fig4, ax4 = plt.subplots()
-    ax4.hist(ages, bins=range(0, 101, 5), color="lightgreen", edgecolor="black")
-    ax4.set_title("Age Distribution")
+    bins = range(0, 101, 5)
+    ax4.hist([ages_men, ages_women], bins=bins, color=["lightblue", "pink"], label=["Men", "Women"], stacked=False)
+    ax4.set_title("Age Distribution by Gender")
     ax4.set_xlabel("Age")
     ax4.set_ylabel("Count")
+    ax4.legend()
     buf4 = io.BytesIO()
     plt.savefig(buf4, format="png")
     plt.close(fig4)
@@ -248,7 +262,7 @@ demo = gr.Interface(
         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="Age Distribution"),
+        gr.Image(type="pil", label="Age Distribution by Gender"),
     ],
     title="Image Classifier with Color & Face Analysis",
     description="Upload a ZIP of images. Classifies images, analyzes dominant color, detects faces, and displays thumbnails.",