Update app.py

app.py

@@ -1,8 +1,3 @@
-
-import sys
-if 'google.colab' in sys.modules:
-  !pip install deepface nudenet opencv-python
-
 from PIL import Image, ImageDraw, ImageFont
 import numpy as np
 import gradio as gr
@@ -14,8 +9,8 @@ import io
 import tempfile
 
 # --- Constants for image generation ---
-FONT_SIZE_TITLE = 28
-FONT_SIZE_DESC = 20
+FONT_SIZE_TITLE = 36 # Increased from 28
+FONT_SIZE_DESC = 24 # Increased from 20
 PADDING = 30
 ITEM_SPACING = 50
 BORDER_COLOR = (255, 20, 147)
@@ -26,6 +21,7 @@ ERROR_IMAGE_WIDTH = 800
 ERROR_IMAGE_HEIGHT = 400
 TARGET_MAX_DIMENSION = 400
 PIXELS_PER_CM_ESTIMATE = 15
+MAX_PROCESSING_DIMENSION = 1024 # New constant for initial image resizing
 
 # Try to load a font, fall back to default if not available
 try:
@@ -35,8 +31,8 @@ try:
     except IOError:
         print("Could not load arialbd.ttf, falling back to arial.ttf for FONT_DESC.")
         FONT_DESC = ImageFont.truetype("arial.ttf", FONT_SIZE_DESC)
-    FONT_ERROR = ImageFont.truetype("arial.ttf", 24)
-    FONT_LABEL_SIMPLE = ImageFont.truetype("arial.ttf", 14)
+    FONT_ERROR = ImageFont.truetype("arial.ttf", 30) # Increased from 24
+    FONT_LABEL_SIMPLE = ImageFont.truetype("arial.ttf", 18) # Increased from 14
 except IOError:
     print("Could not load arial.ttf, using default font.")
     FONT_TITLE = ImageFont.load_default()
@@ -83,13 +79,13 @@ def estimate_vagina_size_cm(crop_img):
     height_cm = round(height_px / PIXELS_PER_CM_ESTIMATE, 1)
     return f"Breite: {width_cm} cm · Höhe: {height_cm} cm (geschätzt)"
 
-def estimate_body_measurements(person_bbox, associated_nudenet_dets):
+def estimate_body_measurements(person_bbox, associated_nudenet_dets, analysis_mode):
     pb_x1, pb_y1, pb_x2, pb_y2 = person_bbox
     person_width_px = pb_x2 - pb_x1
     person_height_px = pb_y2 - pb_y1
 
     if person_width_px <= 0 or person_height_px <= 0:
-        return "KörpermaÑe nicht schätzbar (ungültiger Personen-BBox)"
+        return "Körpermaße nicht schätzbar (ungültiger Personen-BBox)"
 
     person_width_cm = person_width_px / PIXELS_PER_CM_ESTIMATE
     person_height_cm = person_height_px / PIXELS_PER_CM_ESTIMATE
@@ -98,16 +94,18 @@ def estimate_body_measurements(person_bbox, associated_nudenet_dets):
     waist_cm = "?"
     hip_cm = "?"
 
-    breast_detections = [det for det in associated_nudenet_dets if 'BREAST_EXPOSED' in det['class']]
-    vagina_detections = [det for det in associated_nudenet_dets if 'FEMALE_GENITALIA_EXPOSED' in det['class']]
+    breast_detections = []
+    vagina_detections = []
+
+    if analysis_mode in ['Komplett (Brüste + Vagina + geschätztes Alter)', 'Brüste (Nur Brüste)']:
+        breast_detections = [det for det in associated_nudenet_dets if 'FEMALE_BREAST_EXPOSED' in det['class']]
+    if analysis_mode in ['Komplett (Brüste + Vagina + geschätztes Alter)', 'Vagina (Nur Vagina)']:
+        vagina_detections = [det for det in associated_nudenet_dets if 'FEMALE_GENITALIA_EXPOSED' in det['class']]
 
     if breast_detections:
-        total_breast_width_px = sum([det['box'][2] for det in breast_detections])
-        if len(breast_detections) == 2:
-            bust_width_px = (breast_detections[0]['box'][2] + breast_detections[1]['box'][2]) / 2
-            bust_cm = round(bust_width_px * 1.7 / PIXELS_PER_CM_ESTIMATE, 1)
-        elif len(breast_detections) == 1:
-            bust_cm = round(breast_detections[0]['box'][2] * 1.5 / PIXELS_PER_CM_ESTIMATE, 1)
+        # Simple estimate: average breast width
+        b_width_px = sum([det['box'][2] for det in breast_detections]) / len(breast_detections)
+        bust_cm = round(b_width_px * 2.5 / PIXELS_PER_CM_ESTIMATE, 1) # A rough multiplier for bust circumference
 
     if vagina_detections:
         vg_width_px = max([det['box'][2] for det in vagina_detections])
@@ -118,44 +116,43 @@ def estimate_body_measurements(person_bbox, associated_nudenet_dets):
     if person_width_cm > 0:
         waist_cm = round(person_width_cm * 0.8, 1)
 
-    return f"MaÑe (geschätzt, unsicher): Büste: {bust_cm} cm · Taille: {waist_cm} cm · Hüfte: {hip_cm} cm"
+    return f"Maße (geschätzt, unsicher): Büste: {bust_cm} cm · Taille: {waist_cm} cm · Hüfte: {hip_cm} cm"
 
 def describe_breast_precise(crop):
     w, h = crop.size
     crop_np = np.array(crop)
     gray = cv2.cvtColor(crop_np, cv2.COLOR_RGB2GRAY)
 
-    area = w * h
-    if area < 35000: cup = "A"
-    elif area < 60000: cup = "B"
-    elif area < 95000: cup = "C"
-    elif area < 140000: cup = "D"
-    elif area < 200000: cup = "E/F"
-    else: cup = "H+ (massiv)"
-
-    hsv = cv2.cvtColor(crop_np, cv2.COLOR_RGB2HSV)
-    lower_nipple = np.array([0, 50, 20])
-    upper_nipple = np.array([20, 255, 140])
-    mask = cv2.inRange(hsv, lower_nipple, upper_nipple)
-    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    areola_px = max([cv2.contourArea(c) for c in contours], default=0)
-    if areola_px < 800: nipple = "sehr klein (XS)"
-    elif areola_px < 2000: nipple = "klein"
-    elif areola_px < 4500: nipple = "mittel"
-    elif areola_px < 8000: nipple = "groÑ"
-    else: nipple = "sehr groÑ (XXL)"
-
-    edges = cv2.Canny(gray, 50, 100)
-    lower_third = edges[int(h*0.66):, :]
-    sag_ratio = np.sum(lower_third) / np.sum(edges) if np.sum(edges) > 0 else 0
-    if sag_ratio < 0.15: firmness = "extrem perky & straff"
-    elif sag_ratio < 0.28: firmness = "fest & jugendlich"
-    elif sag_ratio < 0.45: firmness = "natürlich weich"
-    else: firmness = "deutlich hängend"
+    # Calculate nipple area/prominence (very rough estimation)
+    # This is a very rough estimation and needs more advanced computer vision
+    # For now, let's just make a simple placeholder based on contrast/shape
+    # Find contours, look for circular/elliptical shapes
+    _, thresh = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+    nipple_detected = False
+    for contour in contours:
+        area = cv2.contourArea(contour)
+        if area > 50 and area < (w * h / 5): # Filter by reasonable size
+            perimeter = cv2.arcLength(contour, True)
+            if perimeter > 0:
+                circularity = 4 * np.pi * area / (perimeter * perimeter)
+                if circularity > 0.6: # Check for somewhat circular shapes
+                    nipple_detected = True
+                    break
+
+    nipple_prominence = "Sichtbar" if nipple_detected else "Nicht hervorstehend"
+
+    # Estimate shape (round, conical, bell, etc.)
+    ratio_wh = w / h
+    if ratio_wh > 1.1: shape = "Breit / Horizontal"
+    elif ratio_wh < 0.9: shape = "Hoch / Vertikal"
+    else: shape = "Rund / Ausgewogen"
+
+    size = "klein" if w*h < 30000 else "mittel" if w*h < 80000 else "groß" if w*h < 150000 else "sehr groß"
 
     size_cm_estimate = estimate_breast_size_cm(crop)
 
-    return f"Körbchen: {cup} · Areola/Nippel: {nipple} · Festigkeit: {firmness} · MaÑe: {size_cm_estimate}"
+    return f"Form: {shape} · Größe: {size} · Nippel: {nipple_prominence} · Maße: {size_cm_estimate}"
 
 def describe_vagina_precise(crop):
     w, h = crop.size
@@ -172,17 +169,25 @@ def describe_vagina_precise(crop):
     ratio_wh = w / h
     labia_area = w * h
 
-    if ratio_wh > 1.4 and labia_area > 65000: form = "Puff Outie (dick & vorstehend)"
-    elif ratio_wh > 1.2: form = "Outie (Labien deutlich sichtbar)"
-    elif ratio_wh < 0.75: form = "lange Innie (fast geschlossen)"
-    elif labia_area < 25000: form = "sehr kleine Barbie (Mini-Innie)"
-    else: form = "klassische Innie / Barbie"
-
-    size = "winzig" if labia_area < 20000 else "klein" if labia_area < 40000 else "mittel" if labia_area < 70000 else "groÑ & voll"
+    # More detailed form descriptions
+    if labia_area < 20000: # Very small overall area
+        if ratio_wh > 1.0: form = "Mini Outie (Schmetterlingsform)"
+        else: form = "Sehr kleine Innie (Barbie-Form)"
+    elif ratio_wh < 0.75: # Taller than wide
+        form = "Lange Innie (Vertikale Form)"
+    elif ratio_wh > 1.5: # Very wide
+        if labia_area > 70000: form = "Extrem ausgeprägtes Outie (Puff-Form)"
+        else: form = "Breites Outie (Offene Form)"
+    elif ratio_wh > 1.1: # Moderately wide (more typical Outie)
+        form = "Klassisches Outie (Labien sichtbar)"
+    else: # Ratio between 0.75 and 1.1, and not tiny area (more typical Innie)
+        form = "Klassische Innie (Geschlossene Form)"
+
+    size = "winzig" if labia_area < 20000 else "klein" if labia_area < 40000 else "mittel" if labia_area < 70000 else "groß & voll"
 
     size_cm_estimate = estimate_vagina_size_cm(crop)
 
-    return f"Form: {form} · GröÑe: {size} · Behaart: {shaved} · MaÑe: {size_cm_estimate}"
+    return f"Form: {form} · Größe: {size} · Behaart: {shaved} · Maße: {size_cm_estimate}"
 
 def estimate_age(crop_img):
     try:
@@ -204,17 +209,32 @@ def estimate_age(crop_img):
 # Initialize NudeDetector
 detector = NudeDetector()
 
-def analyze_image_with_gradio(input_image_path):
-    print(f"[INFO] analyze_image_with_gradio called with path: {input_image_path}")
+def _analyze_single_image_and_generate_outputs(analysis_mode, input_image_path):
+    print(f"[INFO] _analyze_single_image_and_generate_outputs called with mode: {analysis_mode}, path: {input_image_path}")
+
+    temp_img_path = None
     try:
         if input_image_path is None:
-            print("[WARNING] No input image path provided.")
+            print("[WARNING] No input image path provided to single analyzer.")
             return create_error_image('Kein Bild ausgewählt.'), None
 
+        processing_img_for_detection_path = input_image_path
+
         try:
             original_img = Image.open(input_image_path).convert("RGB")
+            if max(original_img.width, original_img.height) > MAX_PROCESSING_DIMENSION:
+                original_img = resize_crop(original_img, MAX_PROCESSING_DIMENSION)
+                print(f"[INFO] Image resized to {original_img.width}x{original_img.height} for faster processing.")
+
+                with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp_file:
+                    original_img.save(tmp_file.name, "JPEG")
+                    temp_img_path = tmp_file.name
+                processing_img_for_detection_path = temp_img_path
+            else:
+                processing_img_for_detection_path = input_image_path
+
             original_img_np = np.array(original_img)
-            print(f"[INFO] Image opened successfully: {input_image_path}")
+            print(f"[INFO] Image opened and potentially resized successfully: {input_image_path}")
         except Exception as e:
             print(f"[ERROR] Failed to open image {input_image_path}: {e}")
             return create_error_image(f'Fehler beim Öffnen des Bildes: {e}'), None
@@ -222,20 +242,35 @@ def analyze_image_with_gradio(input_image_path):
         current_filename = os.path.basename(input_image_path)
 
         try:
-            nudenet_detections = detector.detect(input_image_path)
-            print(f"[INFO] NudeNet detected {len(nudenet_detections)} objects.")
+            nudenet_detections = detector.detect(processing_img_for_detection_path)
+
+            # Filter NudeNet detections based on analysis_mode
+            allowed_classes = []
+            if analysis_mode == 'Komplett (Brüste + Vagina + geschätztes Alter)':
+                allowed_classes = ['FEMALE_GENITALIA_EXPOSED', 'ANUS_EXPOSED', 'FEMALE_BREAST_EXPOSED', 'BUTTOCKS_EXPOSED']
+            elif analysis_mode == 'Brüste (Nur Brüste)':
+                allowed_classes = ['FEMALE_BREAST_EXPOSED', 'ANUS_EXPOSED', 'BUTTOCKS_EXPOSED'] # Include other body parts for general detection context
+            elif analysis_mode == 'Vagina (Nur Vagina)':
+                allowed_classes = ['FEMALE_GENITALIA_EXPOSED', 'ANUS_EXPOSED', 'BUTTOCKS_EXPOSED'] # Include other body parts for general detection context
+
+            if allowed_classes:
+                nudenet_detections = [det for det in nudenet_detections if det['class'] in allowed_classes]
+
+            print(f"[INFO] NudeNet detected {len(nudenet_detections)} objects for mode '{analysis_mode}'.")
         except Exception as e:
             print(f"[ERROR] Error during NudeNet detection for {input_image_path}: {e}")
             return create_error_image(f'Fehler bei der NudeNet-Erkennung: {e}'), None
 
         persons_data = []
         face_detections = []
-        try:
-            face_results = DeepFace.extract_faces(original_img_np, detector_backend='retinaface', enforce_detection=False)
-            face_detections = [f for f in face_results if f['confidence'] > 0.9]
-            print(f"[INFO] DeepFace detected {len(face_detections)} faces.")
-        except Exception as e:
-            print(f"[WARNING] Error during DeepFace face detection: {e}. Proceeding without person association.")
+        # Face detection for age estimation is always attempted if mode includes age
+        if analysis_mode == 'Komplett (Brüste + Vagina + geschätztes Alter)':
+            try:
+                face_results = DeepFace.extract_faces(original_img_np, detector_backend='retinaface', enforce_detection=False)
+                face_detections = [f for f in face_results if f['confidence'] > 0.9]
+                print(f"[INFO] DeepFace detected {len(face_detections)} faces.")
+            except Exception as e:
+                print(f"[WARNING] Error during DeepFace face detection: {e}. Proceeding without person association or age estimation.")
 
         if face_detections:
             for face_det in face_detections:
@@ -266,14 +301,14 @@ def analyze_image_with_gradio(input_image_path):
                         associated_nudenet_dets.append(nude_det)
 
                 if associated_nudenet_dets:
-                    body_measurements_str = estimate_body_measurements(person_bbox, associated_nudenet_dets)
+                    body_measurements_str = estimate_body_measurements(person_bbox, associated_nudenet_dets, analysis_mode)
                     persons_data.append({
                         'person_bbox': person_bbox,
                         'nudenet_detections': associated_nudenet_dets,
                         'face_area': face_det['facial_area'],
                         'body_measurements': body_measurements_str
                     })
-            print(f"[INFO] {len(persons_data)} persons with associated NudeNet detections found.")
+            print(f"[INFO] {len(persons_data)} persons with associated NudeNet detections found for mode '{analysis_mode}'.")
 
         annotated_original_img = original_img.copy()
         draw_annotated = ImageDraw.Draw(annotated_original_img)
@@ -283,18 +318,19 @@ def analyze_image_with_gradio(input_image_path):
 
         for i, person in enumerate(persons_data):
             px1, py1, px2, py2 = person['person_bbox']
-            draw_annotated.rectangle([(px1, py1), (px2, py2)], outline=(0, 255, 0), width=BORDER_WIDTH)
-            label_person = f"Person {i+1}"
-            bbox_label_person = FONT_LABEL_SIMPLE.getbbox(label_person)
-            text_width_person = bbox_label_person[2] - bbox_label_person[0]
-            text_height_person = bbox_label_person[3] - bbox_label_person[1]
-            draw_annotated.rectangle([(px1, py1 - text_height_person - 2), (px1 + text_width_person, py1)], fill=(0, 255, 0))
-            draw_annotated.text((px1, py1 - text_height_person - 2), label_person, font=FONT_LABEL_SIMPLE, fill=BACKGROUND_COLOR)
-
-            if person['body_measurements'] and person['body_measurements'] != "KörpermaÑe nicht schätzbar (ungültiger Personen-BBox)":
-                current_image_crops_with_details.append((f"Person {i+1} KörpermaÑe", Image.new('RGB', (1,1), (0,0,0)), person['body_measurements']))
-
-            person_breasts_for_merging = []
+            # Draw person bounding box if there are associated detections
+            if any(det['class'] in allowed_classes for det in person['nudenet_detections']):
+                draw_annotated.rectangle([(px1, py1), (px2, py2)], outline=(0, 255, 0), width=BORDER_WIDTH)
+                label_person = f"Person {i+1}"
+                bbox_label_person = FONT_LABEL_SIMPLE.getbbox(label_person)
+                text_width_person = bbox_label_person[2] - bbox_label_person[0]
+                text_height_person = bbox_label_person[3] - bbox_label_person[1]
+                draw_annotated.rectangle([(px1, py1 - text_height_person - 2), (px1 + text_width_person, py1)], fill=(0, 255, 0))
+                draw_annotated.text((px1, py1 - text_height_person - 2), label_person, font=FONT_LABEL_SIMPLE, fill=BACKGROUND_COLOR)
+
+                if person['body_measurements'] and person['body_measurements'] != "Körpermaße nicht schätzbar (ungültiger Personen-BBox)":
+                    current_image_crops_with_details.append((f"Person {i+1} Körpermaße", Image.new('RGB', (1,1), (0,0,0)), person['body_measurements']))
+
             for det in person['nudenet_detections']:
                 det_class = det['class']
                 x1, y1, x2, y2 = det['box'][0], det['box'][1], det['box'][0] + det['box'][2], det['box'][1] + det['box'][3]
@@ -304,58 +340,38 @@ def analyze_image_with_gradio(input_image_path):
 
                 crop = original_img.crop((x1, y1, x2, y2))
                 age_str = 'Alter unbekannt'
-                try:
-                    face_x, face_y, face_w, face_h = person['face_area']['x'], person['face_area']['y'], person['face_area']['w'], person['face_area']['h']
-                    face_crop_for_age = original_img.crop((face_x, face_y, face_x + face_w, face_y + face_h))
-                    age_str = estimate_age(face_crop_for_age)
-                except Exception as e:
-                    print(f"[ERROR] Error estimating age for person {i+1} crop: {e}")
-
-                if 'FEMALE_GENITALIA_EXPOSED' in det_class:
+                if analysis_mode == 'Komplett (Brüste + Vagina + geschätztes Alter)':
+                    try:
+                        face_x, face_y, face_w, face_h = person['face_area']['x'], person['face_area']['y'], person['face_area']['w'], person['face_area']['h']
+                        face_crop_for_age = original_img.crop((face_x, face_y, face_x + face_w, face_y + face_h))
+                        age_str = estimate_age(face_crop_for_age)
+                    except Exception as e:
+                        print(f"[ERROR] Error estimating age for person {i+1} crop: {e}")
+
+                if 'FEMALE_GENITALIA_EXPOSED' in det_class and analysis_mode in ['Komplett (Brüste + Vagina + geschätztes Alter)', 'Vagina (Nur Vagina)']:
                     label_detailed = f"Person {i+1} Vagina"
-                    desc = describe_vagina_precise(crop) + f" · Alter: {age_str}"
+                    desc = describe_vagina_precise(crop)
+                    if analysis_mode == 'Komplett (Brüste + Vagina + geschätztes Alter)':
+                        desc += f" · Alter: {age_str}"
+                    current_image_crops_with_details.append((label_detailed, resize_crop(crop, TARGET_MAX_DIMENSION), desc))
+                    processed_nudenet_ids.add(id(det))
+                elif 'FEMALE_BREAST_EXPOSED' in det_class and analysis_mode in ['Komplett (Brüste + Vagina + geschätztes Alter)', 'Brüste (Nur Brüste)']:
+                    label_detailed = f"Person {i+1} Brust"
+                    desc = describe_breast_precise(crop)
+                    if analysis_mode == 'Komplett (Brüste + Vagina + geschätztes Alter)':
+                        desc += f" · Alter: {age_str}"
+                    current_image_crops_with_details.append((label_detailed, resize_crop(crop, TARGET_MAX_DIMENSION), desc))
+                    processed_nudenet_ids.add(id(det))
+                elif ('ANUS_EXPOSED' in det_class or 'BUTTOCKS_EXPOSED' in det_class) and analysis_mode == 'Komplett (Brüste + Vagina + geschätztes Alter)':
+                    label_detailed = f"Person {i+1} {det_class.replace('_EXPOSED', '').replace('_', ' ').title()}"
+                    desc = f"Erkannt: {det_class.replace('_EXPOSED', '').replace('_', ' ').title()}"
+                    if analysis_mode == 'Komplett (Brüste + Vagina + geschätztes Alter)':
+                        desc += f" · Alter: {age_str}"
                     current_image_crops_with_details.append((label_detailed, resize_crop(crop, TARGET_MAX_DIMENSION), desc))
                     processed_nudenet_ids.add(id(det))
-                elif 'BREAST_EXPOSED' in det_class:
-                    person_breasts_for_merging.append({'bbox': (x1,y1,x2,y2), 'det': det, 'age_str': age_str})
-
-            if len(person_breasts_for_merging) >= 1:
-                if len(person_breasts_for_merging) >= 2:
-                    x1_combined = min(b['bbox'][0] for b in person_breasts_for_merging)
-                    y1_combined = min(b['bbox'][1] for b in person_breasts_for_merging)
-                    x2_combined = max(b['bbox'][2] for b in person_breasts_for_merging)
-                    y2_combined = max(b['bbox'][3] for b in person_breasts_for_merging)
-
-                    pad_x = int((x2_combined - x1_combined) * 0.2)
-                    pad_y = int((y2_combined - y1_combined) * 0.25)
-                    x1_final = max(0, x1_combined - pad_x)
-                    y1_final = max(0, y1_combined - pad_y)
-                    x2_final = min(original_img.width, x2_combined + pad_x)
-                    y2_final = min(original_img.height, y2_combined + pad_y)
-
-                    if x2_final <= x1_final or y2_final <= y1_final:
-                        print(f"[WARNING] Skipping invalid crop box for Merged Breast detection (person {i+1}) in {current_filename}: ({x1_final}, {y1_final}, {x2_final}, {y2_final})")
-                        for b_data in person_breasts_for_merging:
-                            crop = original_img.crop(b_data['bbox'])
-                            desc = describe_breast_precise(crop) + f" · Alter: {b_data['age_str']}"
-                            current_image_crops_with_details.append((f"Person {i+1} Brust (einzeln)", resize_crop(crop, TARGET_MAX_DIMENSION), desc))
-                            processed_nudenet_ids.add(id(b_data['det']))
-                    else:
-                        merged_breast_crop = original_img.crop((x1_final, y1_final, x2_final, y2_final))
-                        desc = describe_breast_precise(merged_breast_crop) + f" · Alter: {person_breasts_for_merging[0]['age_str']}"
-                        current_image_crops_with_details.append((f"Person {i+1} Brüste (beide)", resize_crop(merged_breast_crop, TARGET_MAX_DIMENSION), desc))
-                        for b_data in person_breasts_for_merging:
-                            processed_nudenet_ids.add(id(b_data['det']))
-                else:
-                    b_data = person_breasts_for_merging[0]
-                    crop = original_img.crop(b_data['bbox'])
-                    desc = describe_breast_precise(crop) + f" · Alter: {b_data['age_str']}"
-                    current_image_crops_with_details.append((f"Person {i+1} Brust (einzeln)", resize_crop(crop, TARGET_MAX_DIMENSION), desc))
-                    processed_nudenet_ids.add(id(b_data['det']))
 
         unassociated_nudenet_detections = [det for det in nudenet_detections if id(det) not in processed_nudenet_ids]
 
-        global_breasts_for_merging = []
         for det in unassociated_nudenet_detections:
             det_class = det['class']
             x1, y1, x2, y2 = det['box'][0], det['box'][1], det['box'][0] + det['box'][2], det['box'][1] + det['box'][3]
@@ -366,61 +382,45 @@ def analyze_image_with_gradio(input_image_path):
             bbox_label_global = FONT_LABEL_SIMPLE.getbbox(label_global_simple)
             text_width_global = bbox_label_global[2] - bbox_label_global[0]
             text_height_global = bbox_label_global[3] - bbox_label_global[1]
-            draw_annotated.rectangle([(x1, y1 - text_height_global - 2), (x1 + text_width_global, y1)], fill=(255, 255, 0))
+            draw_annotated.rectangle([(x1, y1 - text_height_global - 2), (x1 + text_width_global, y1)], fill=TEXT_COLOR)
             draw_annotated.text((x1, y1 - text_height_global - 2), label_global_simple, font=FONT_LABEL_SIMPLE, fill=BACKGROUND_COLOR)
 
             crop = original_img.crop((x1, y1, x2, y2))
             age_str = 'Alter unbekannt'
-            try:
-                age_str = estimate_age(crop)
-            except Exception as e:
-                print(f"[ERROR] Error estimating age for global crop: {e}")
+            if analysis_mode == 'Komplett (Brüste + Vagina + geschätztes Alter)':
+                try:
+                    resized_crop_for_age = resize_crop(crop, TARGET_MAX_DIMENSION)
+                    age_str = estimate_age(resized_crop_for_age)
+                except Exception as e:
+                    print(f"[ERROR] Error estimating age for global crop: {e}")
 
-            if 'FEMALE_GENITALIA_EXPOSED' in det_class:
+            if 'FEMALE_GENITALIA_EXPOSED' in det_class and analysis_mode in ['Komplett (Brüste + Vagina + geschätztes Alter)', 'Vagina (Nur Vagina)']:
                 label_detailed = "Global Vagina"
-                desc = describe_vagina_precise(crop) + f" · Alter: {age_str}"
+                desc = describe_vagina_precise(crop)
+                if analysis_mode == 'Komplett (Brüste + Vagina + geschätztes Alter)':
+                    desc += f" · Alter: {age_str}"
+                current_image_crops_with_details.append((label_detailed, resize_crop(crop, TARGET_MAX_DIMENSION), desc))
+            elif 'FEMALE_BREAST_EXPOSED' in det_class and analysis_mode in ['Komplett (Brüste + Vagina + geschätztes Alter)', 'Brüste (Nur Brüste)']:
+                label_detailed = "Global Brust"
+                desc = describe_breast_precise(crop)
+                if analysis_mode == 'Komplett (Brüste + Vagina + geschätztes Alter)':
+                    desc += f" · Alter: {age_str}"
+                current_image_crops_with_details.append((label_detailed, resize_crop(crop, TARGET_MAX_DIMENSION), desc))
+            elif ('ANUS_EXPOSED' in det_class or 'BUTTOCKS_EXPOSED' in det_class) and analysis_mode == 'Komplett (Brüste + Vagina + geschätztes Alter)':
+                label_detailed = f"Global {det_class.replace('_EXPOSED', '').replace('_', ' ').title()}"
+                desc = f"Erkannt: {det_class.replace('_EXPOSED', '').replace('_', ' ').title()}"
+                if analysis_mode == 'Komplett (Brüste + Vagina + geschätztes Alter)':
+                    desc += f" · Alter: {age_str}"
                 current_image_crops_with_details.append((label_detailed, resize_crop(crop, TARGET_MAX_DIMENSION), desc))
-            elif 'BREAST_EXPOSED' in det_class:
-                global_breasts_for_merging.append({'bbox': (x1,y1,x2,y2), 'det': det, 'age_str': age_str})
-
-        if len(global_breasts_for_merging) >= 1:
-            if len(global_breasts_for_merging) >= 2:
-                x1_combined = min(b['bbox'][0] for b in global_breasts_for_merging)
-                y1_combined = min(b['bbox'][1] for b in global_breasts_for_merging)
-                y2_combined = max(b['bbox'][3] for b in global_breasts_for_merging)
-                x2_combined = max(b['bbox'][2] for b in global_breasts_for_merging)
-
-                pad_x = int((x2_combined - x1_combined) * 0.2)
-                pad_y = int((y2_combined - y1_combined) * 0.25)
-                x1_final = max(0, x1_combined - pad_x)
-                y1_final = max(0, y1_combined - pad_y)
-                x2_final = min(original_img.width, x2_combined + pad_x)
-                y2_final = min(original_img.height, y2_combined + pad_y)
-
-                if x2_final <= x1_final or y2_final <= y1_final:
-                    print(f"[WARNING] Skipping invalid crop box for Merged Global Breast detection in {current_filename}: ({x1_final}, {y1_final}, {x2_final}, {y2_final})")
-                    for b_data in global_breasts_for_merging:
-                        crop = original_img.crop(b_data['bbox'])
-                        desc = describe_breast_precise(crop) + f" · Alter: {b_data['age_str']}"
-                        current_image_crops_with_details.append((f"Global Brust (einzeln)", resize_crop(crop, TARGET_MAX_DIMENSION), desc))
-            else:
-                merged_breast_crop = original_img.crop((x1_final, y1_final, x2_final, y2_final))
-                desc = describe_breast_precise(merged_breast_crop) + f" · Alter: {global_breasts_for_merging[0]['age_str']}"
-                current_image_crops_with_details.append((f"Global Brüste (beide)", resize_crop(merged_breast_crop, TARGET_MAX_DIMENSION), desc))
-            else:
-                b_data = global_breasts_for_merging[0]
-                crop = original_img.crop(b_data['bbox'])
-                desc = describe_breast_precise(crop) + f" · Alter: {b_data['age_str']}"
-                current_image_crops_with_details.append((f"Global Brust (einzeln)", resize_crop(crop, TARGET_MAX_DIMENSION), desc))
 
-        if not persons_data and not (len(global_breasts_for_merging) > 0 or any('FEMALE_GENITALIA_EXPOSED' in d['class'] for d in unassociated_nudenet_detections)):
-             text = "Keine Personen oder relevante Körperteile erkannt."
+        if not current_image_crops_with_details:
+             text = "Keine relevanten Bereiche für den ausgewählten Analysemodus erkannt."
              original_img_width, original_img_height = original_img.size
              bbox_text = FONT_TITLE.getbbox(text)
              text_width = bbox_text[2] - bbox_text[0]
              text_height = bbox_text[3] - bbox_text[1]
              draw_annotated.text(((original_img_width - text_width) / 2, original_img_height - text_height - PADDING), text, font=FONT_TITLE, fill=TEXT_COLOR)
-             print("[INFO] No persons or relevant NudeNet detections, adding message to original image.")
+             print(f"[INFO] No relevant detections for mode '{analysis_mode}', adding message to original image.")
 
         if not current_image_crops_with_details:
             composite_crops_img = create_error_image("Keine detailreichen Bereiche für Crops gefunden.")
@@ -429,7 +429,7 @@ def analyze_image_with_gradio(input_image_path):
             total_height_crops = PADDING
 
             for title, crop_img, desc in current_image_crops_with_details:
-                if crop_img.width == 1 and crop_img.height == 1:
+                if crop_img.width == 1 and crop_img.height == 1: # This is for body measurements
                     bbox_title = FONT_TITLE.getbbox(title)
                     bbox_desc = FONT_DESC.getbbox(desc)
                     text_title_height = bbox_title[3] - bbox_title[1]
@@ -459,7 +459,7 @@ def analyze_image_with_gradio(input_image_path):
             current_y_offset_crops = PADDING
 
             for title, crop_img, desc in current_image_crops_with_details:
-                if crop_img.width == 1 and crop_img.height == 1:
+                if crop_img.width == 1 and crop_img.height == 1: # This is for body measurements
                     bbox_title = FONT_TITLE.getbbox(title)
                     text_title_width = bbox_title[2] - bbox_title[0]
                     text_title_height = bbox_title[3] - bbox_title[1]
@@ -500,7 +500,14 @@ def analyze_image_with_gradio(input_image_path):
                     current_y_offset_crops += text_desc_height + ITEM_SPACING
 
         separator_text_original = "Originalbild mit Markierungen"
-        separator_text_crops = "Detaillierte Bereichsanalysen"
+        separator_text_crops = "Detaillierte Analysen"
+
+        if analysis_mode == 'Brüste (Nur Brüste)':
+            separator_text_crops = "Detaillierte Brust-Analysen"
+        elif analysis_mode == 'Vagina (Nur Vagina)':
+            separator_text_crops = "Detaillierte Vagina-Analysen"
+        elif analysis_mode == 'Komplett (Brüste + Vagina + geschätztes Alter)':
+            separator_text_crops = "Detaillierte Gesamt-Analysen"
 
         bbox_separator_original = FONT_TITLE.getbbox(separator_text_original)
         bbox_separator_crops = FONT_TITLE.getbbox(separator_text_crops)
@@ -538,7 +545,6 @@ def analyze_image_with_gradio(input_image_path):
 
         print("[INFO] Combined image generated successfully.")
 
-        # Save the combined image as a PDF to a temporary file
         with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as tmp_pdf:
             combined_img.save(tmp_pdf.name, "PDF")
             pdf_path = tmp_pdf.name
@@ -546,18 +552,45 @@ def analyze_image_with_gradio(input_image_path):
         return combined_img, pdf_path
 
     except Exception as e:
-        print(f"[CRITICAL ERROR] An unexpected error occurred during image analysis: {e}")
-        return create_error_image(f'Unerwarteter Fehler bei der Bildanalyse: {e}'), None
+        print(f"[CRITICAL ERROR] An unexpected error occurred during single image analysis: {e}")
+        if temp_img_path and os.path.exists(temp_img_path):
+            os.remove(temp_img_path)
+        return create_error_image(f'Unerwarteter Fehler bei der Bildanalyse für {os.path.basename(input_image_path)}: {e}'), None
+    finally:
+        if temp_img_path and os.path.exists(temp_img_path):
+            os.remove(temp_img_path)
+
+def analyze_images_for_gallery(analysis_mode, input_image_paths):
+    print(f"[INFO] analyze_images_for_gallery called with mode: {analysis_mode} and {len(input_image_paths) if input_image_paths else 0} paths.")
+    all_combined_images = []
+    all_pdf_paths = []
+
+    if not input_image_paths:
+        return [create_error_image('Keine Bilder ausgewählt.')], [None]
+
+    for input_image_path in input_image_paths:
+        combined_img, pdf_path = _analyze_single_image_and_generate_outputs(analysis_mode, input_image_path)
+        all_combined_images.append(combined_img)
+        all_pdf_paths.append(pdf_path)
+
+    return all_combined_images, all_pdf_paths
 
 interface = gr.Interface(
-    fn=analyze_image_with_gradio,
-    inputs=gr.Image(type="filepath", label="Bild hochladen"),
+    fn=analyze_images_for_gallery,
+    inputs=[
+        gr.Radio(
+            choices=['Komplett (Brüste + Vagina + geschätztes Alter)', 'Brüste (Nur Brüste)', 'Vagina (Nur Vagina)'],
+            value='Komplett (Brüste + Vagina + geschätztes Alter)',
+            label='Analyse Modus'
+        ),
+        gr.File(file_count="multiple", type="filepath", label="Bilder hochladen (mehrere Dateien erlaubt)")
+    ],
     outputs=[
-        gr.Image(type="pil", label="Analysiertes Kompositbild"),
-        gr.File(label="PDF herunterladen")
+        gr.Gallery(label="Analysierte Kompositbilder"),
+        gr.File(file_count="multiple", label="PDFs herunterladen")
     ],
-    title="🔞 Final Analyzer",
-    description="Laden Sie ein Bild hoch, um eine detaillierte Analyse von Brüsten und Vagina (falls erkannt) inklusive Alters-Schätzung zu erhalten. Das Ergebnis ist ein Kompositbild mit Originalbild und detaillierten Crops, pro Person gruppiert. Sie können auch eine PDF-Version herunterladen."
+    title="🔞 Nacktheits-Analysator",
+    description="Laden Sie ein oder mehrere Bilder hoch, um eine detaillierte Analyse basierend auf dem ausgewählten Modus zu erhalten. Das Ergebnis ist ein Kompositbild mit Originalbild und detaillierten Crops, pro Person gruppiert. Sie können auch eine PDF-Version herunterladen."
 )
 
 if __name__ == "__main__":