Fix: demo skip classify_scene, fix unpack error, fix cover path, update track name

app.py

@@ -758,22 +758,20 @@ async def demo(scene_type: str = Form("auto")):
 
 
 def _run_demo_pipeline(img_paths, expected_scene):
-    """执行示例检测流程（不涉及用户历史数据，纯两张图比对）"""
+    """执行示例检测流程（不涉及用户历史数据，纯两张图比对）
+
+    expected_scene 直接指定场景类型，跳过 classify_scene 节省时间
+    """
     import cv2
     import numpy as np
 
     path1, path2 = img_paths[0], img_paths[1]
 
-    # 场景分类（用第一张图）
+    # 跳过场景分类，直接使用 expected_scene
+    scene_type = expected_scene
     ocr_result = None
     full_text = ''
-    try:
-        scene_type, detail = classify_scene(path1, ocr_engine)
-        ocr_result = detail.get('ocr_result')
-        full_text = detail.get('full_text', '')
-    except Exception as e:
-        scene_type = expected_scene
-        print(f"[demo] 场景分类失败: {e}")
+    print(f"[demo] 跳过场景分类，直接使用指定场景: {scene_type}")
 
     is_same = False
     similarity = 0.0
@@ -792,9 +790,11 @@ def _run_demo_pipeline(img_paths, expected_scene):
         # 文本场景：OCR + BGE 比对
         from module.text_matcher import compute_text_similarity
         try:
-            # OCR 第二张图
-            scene2, detail2 = classify_scene(path2, ocr_engine)
-            text2 = detail2.get('full_text', '')
+            # 直接 OCR 提取两张图文本，跳过 classify_scene
+            from module.text_classifier import _ocr_image
+            full_text = _ocr_image(path1, ocr_engine) or ''
+            text2 = _ocr_image(path2, ocr_engine) or ''
+            log_entry['query_text'] = full_text
             log_entry['history_text'] = text2
 
             # BGE 编码比对

index.html

@@ -32,7 +32,7 @@ body{
 /* === 背景图 cover.png === */
 .bg-cover{
   position:fixed;inset:0;z-index:-3;
-  background-image:url('/cover.png');
+  background-image:url('cover.png');
   background-size:cover;background-position:center;
   opacity:0.35;
 }
@@ -78,7 +78,7 @@ body{
 }
 .parallax-bg .bg-cover-inner{
   position:absolute;inset:0;
-  background-image:url('/cover.png');
+  background-image:url('cover.png');
   background-size:cover;background-position:center;
   opacity:0.3;
 }
@@ -352,7 +352,7 @@ tr.row-fail td{color:#ef9a9a}
 
   <!-- 报名信息头 -->
   <div class="about-header-card">
-    <div class="track">报名赛道：造个新解法 — 聚焦新一代学习与工作方式</div>
+    <div class="track">报名赛道：学习工作赛道</div>
     <h1>慧眼同源</h1>
     <div class="tagline">多模态文档去重与版本溯源系统</div>
   </div>

module/text_classifier.py

@@ -84,6 +84,21 @@ def _compute_line_density(text_boxes, img_shape):
     return float(density_norm)
 
 
+def _ocr_image(image_path, ocr_engine):
+    """仅执行 OCR 提取文本，不做场景分类
+
+    Returns:
+        full_text: 提取的文本字符串
+    """
+    try:
+        result = ocr_engine.ocr(image_path, cls=True)
+        if result and result[0]:
+            return ' '.join(line[1][0] for line in result[0])
+    except Exception as e:
+        print(f"[OCR] 提取失败: {e}")
+    return ''
+
+
 def classify_scene(image_path, ocr_engine, doc_score_threshold=DOC_SCORE_THRESHOLD,
                    precomputed_ocr_result=None):
     """多信号加权评分判断图片属于文本场景还是复杂场景