Upload 5 files

app.py

@@ -640,7 +640,6 @@ def create_interface():
                         gr.Examples(
                             examples=[
                                 "room_01.jpg",
-                                "room_02.jpg",
                                 "street_04.jpg",
                                 "street_05.jpg",
                                 "landmark_Louvre_01.jpg",

confidence_templates.py

@@ -0,0 +1,6 @@
+
+CONFIDENCE_TEMPLATES = {
+    "high": "{description} {details}",
+    "medium": "This appears to be {description} {details}",
+    "low": "This might be {description}, but the confidence is low. {details}"
+}

enhance_scene_describer.py

@@ -12,7 +12,7 @@ from object_template_fillers import OBJECT_TEMPLATE_FILLERS
 from lighting_conditions import LIGHTING_CONDITIONS
 from viewpoint_templates import VIEWPOINT_TEMPLATES
 from cultural_templates import CULTURAL_TEMPLATES
-from confifence_templates import CONFIDENCE_TEMPLATES
+from confidence_templates import CONFIDENCE_TEMPLATES
 from landmark_data import ALL_LANDMARKS
 
 class EnhancedSceneDescriber:

lighting_analyzer.py

@@ -167,13 +167,12 @@ class LightingAnalyzer:
         # Separate HSV channels
         h_channel, s_channel, v_channel = cv2.split(hsv_img)
 
-        # --- Brightness Features ---
+        # Brightness Features 
         avg_brightness = np.mean(v_channel)
         brightness_std = np.std(v_channel)
         dark_pixel_ratio = np.sum(v_channel < self.config.get("dark_pixel_threshold", 50)) / (height * width) # 使用配置閾值
         bright_pixel_ratio = np.sum(v_channel > self.config.get("bright_pixel_threshold", 220)) / (height * width) # 新增：亮部像素比例
 
-        # --- Color Features ---
         # Yellow-Orange Ratio
         yellow_orange_mask = ((h_channel >= 15) & (h_channel <= 45)) # Adjusted range slightly
         yellow_orange_ratio = np.sum(yellow_orange_mask) / (height * width)
@@ -183,7 +182,6 @@ class LightingAnalyzer:
         blue_ratio = np.sum(blue_mask) / (height * width)
 
         # More specific "Sky-Like Blue" Ratio - for clearer skies
-        # 中文備註：更精確地定義「天空藍」，排除室內常見的深藍或青色。
         sky_like_blue_hue_min = self.config.get("sky_blue_hue_min", 100)
         sky_like_blue_hue_max = self.config.get("sky_blue_hue_max", 130) # Typical sky blue Hues in HSV
         sky_like_blue_sat_min = self.config.get("sky_blue_sat_min", 60)   # Sky is usually somewhat saturated
@@ -201,8 +199,7 @@ class LightingAnalyzer:
 
         avg_saturation = np.mean(s_channel)
 
-        # --- Sky Region Analysis (Top 1/3 of image) ---
-        # 中文備註：專門分析圖像頂部區域，這是判斷天空的關鍵。
+        # 專門分析圖像頂部區域，這是判斷天空的關鍵
         top_third_height = height // 3
         sky_region_v = v_channel[:top_third_height, :]
         sky_region_s = s_channel[:top_third_height, :]
@@ -241,8 +238,7 @@ class LightingAnalyzer:
         else:
             color_atmosphere = "neutral"
 
-        # --- Gradient and Texture Features (on small image for speed) ---
-        # 中文備註：在縮小的灰階圖像上計算梯度，以提高效率。
+        # Gradient and Texture Features (on small image for speed) 
         gx = cv2.Sobel(small_gray, cv2.CV_32F, 1, 0, ksize=3)
         gy = cv2.Sobel(small_gray, cv2.CV_32F, 0, 1, ksize=3)
 
@@ -253,7 +249,6 @@ class LightingAnalyzer:
 
 
         # Texture complexity of the top region (potential ceiling or sky)
-        # 中文備註：分析頂部區域的紋理複雜度，天空通常紋理簡單，天花板可能複雜。
         small_top_third_height = small_gray.shape[0] // 3
         small_sky_region_gray = small_gray[:small_top_third_height, :]
         if small_sky_region_gray.size > 0:

scene_description.py

@@ -7,7 +7,7 @@ from scene_detail_templates import SCENE_DETAIL_TEMPLATES
 from object_template_fillers import OBJECT_TEMPLATE_FILLERS
 from activity_templates import ACTIVITY_TEMPLATES
 from safety_templates import SAFETY_TEMPLATES
-from confifence_templates import CONFIDENCE_TEMPLATES
+from confidence_templates import CONFIDENCE_TEMPLATES
 
 class SceneDescriptor:
     """