bug

app.py

@@ -66,6 +66,7 @@ def retrieve_content(query_text: str, query_image, source_type: str, text_input:
     if not query_text and query_image is None:
         return "请至少提供查询文本 或 上传查询图片！"
 
+    # 生成 query embedding
     content = []
     if query_text:
         content.append({"type": "text", "text": query_text})
@@ -77,7 +78,7 @@ def retrieve_content(query_text: str, query_image, source_type: str, text_input:
 
     try:
         with torch.no_grad():
-            # 修复在这里：直接传 content，不要 [content]
+            # 修复：直接传 content（已经是 list）
             query_emb = embedder.process(content, normalize=True)[0].cpu().numpy()
     except Exception as e:
         return f"查询 embedding 生成失败：{str(e)}"
@@ -96,6 +97,7 @@ def retrieve_content(query_text: str, query_image, source_type: str, text_input:
         if not text.strip():
             return "没有提供有效文本内容！"
 
+        # 切段
         segments = []
         step = 150
         for i in range(0, len(text), step):
@@ -105,9 +107,13 @@ def retrieve_content(query_text: str, query_image, source_type: str, text_input:
         seg_embs = []
         for seg in segments:
             seg_content = [{"type": "text", "text": seg}]
-            with torch.no_grad():
-                emb = embedder.process([seg_content], normalize=True)[0].cpu().numpy()
-            seg_embs.append(emb)
+            try:
+                with torch.no_grad():
+                    # 修复：直接传 seg_content，不要套 [ ]
+                    emb = embedder.process(seg_content, normalize=True)[0].cpu().numpy()
+                seg_embs.append(emb)
+            except Exception as e:
+                return f"段落 embedding 生成失败：{str(e)}"
 
         sims = [np.dot(query_emb, emb) / (np.linalg.norm(query_emb) * np.linalg.norm(emb) + 1e-8) for emb in seg_embs]
         top_indices = np.argsort(sims)[-3:][::-1]
@@ -138,12 +144,17 @@ def retrieve_content(query_text: str, query_image, source_type: str, text_input:
                 pil_frame = Image.fromarray(frame_rgb)
 
                 frame_content = [{"type": "image", "image": pil_frame}]
-                with torch.no_grad():
-                    emb = embedder.process([frame_content], normalize=True)[0].cpu().numpy()
-                frame_embs.append(emb)
-
-                time_sec = frame_idx / fps
-                timestamps.append(f"{int(time_sec // 60):02d}:{int(time_sec % 60):02d}")
+                try:
+                    with torch.no_grad():
+                        # 修复：直接传 frame_content
+                        emb = embedder.process(frame_content, normalize=True)[0].cpu().numpy()
+                    frame_embs.append(emb)
+
+                    time_sec = frame_idx / fps
+                    timestamps.append(f"{int(time_sec // 60):02d}:{int(time_sec % 60):02d}")
+                except Exception as e:
+                    cap.release()
+                    return f"视频帧 embedding 生成失败：{str(e)}"
 
             frame_idx += 1