依旧修pyright喵喵喵~

src/chat/utils/utils_image.py

@@ -344,14 +344,15 @@ class ImageManager:
             # --- 新的帧选择逻辑：均匀抽取4帧 ---
             num_frames = len(all_frames)
             if num_frames <= 4:
-                # 如果总帧数小于等于4，则全部选中
+                # 如果总宽度小于等于4，则全部选中
                 selected_frames = all_frames
+                indices = list(range(num_frames))
             else:
                 # 使用linspace计算4个均匀分布的索引
                 indices = np.linspace(0, num_frames - 1, 4, dtype=int)
                 selected_frames = [all_frames[i] for i in indices]
 
-            logger.debug(f"GIF Frame Analysis: Total frames={num_frames}, Selected indices={indices if num_frames > 4 else list(range(num_frames))}")
+            logger.debug(f"GIF Frame Analysis: Total frames={num_frames}, Selected indices={indices}")
             # --- 帧选择逻辑结束 ---
 
             # 如果选择后连一帧都没有（比如GIF只有一帧且后续处理失败？）或者原始GIF就没帧，也返回None

src/chat/utils/utils_video.py

@@ -37,7 +37,7 @@ _locks_guard = asyncio.Lock()
 
 logger = get_logger("utils_video")
 
-from inkfox import video
+from inkfox import video  # type: ignore
 
 
 class VideoAnalyzer:
@@ -123,7 +123,6 @@ class VideoAnalyzer:
     # ---- 批量分析 ----
     async def _analyze_batch(self, frames: list[tuple[str, float]], question: str | None) -> str:
         from src.llm_models.payload_content.message import MessageBuilder, RoleType
-        from src.llm_models.utils_model import RequestType
 
         prompt = self.batch_analysis_prompt.format(
             personality_core=self.personality_core, personality_side=self.personality_side
@@ -139,12 +138,7 @@ class VideoAnalyzer:
         for b64, _ in frames:
             mb.add_image_content("jpeg", b64)
         message = mb.build()
-        model_info, api_provider, client = self.video_llm._select_model()
-        resp = await self.video_llm._execute_request(
-            api_provider=api_provider,
-            client=client,
-            request_type=RequestType.RESPONSE,
-            model_info=model_info,
+        resp = await self.video_llm.execute_with_messages(
             message_list=[message],
             temperature=None,
             max_tokens=None,

src/chat/utils/utils_video_legacy.py

@@ -31,9 +31,9 @@ def _extract_frames_worker(
     max_image_size: int,
     frame_extraction_mode: str,
     frame_interval_seconds: float | None,
-) -> list[Any] | list[tuple[str, str]]:
+) -> list[tuple[str, float]] | list[tuple[str, str]]:
     """线程池中提取视频帧的工作函数"""
-    frames = []
+    frames: list[tuple[str, float]] = []
     try:
         cap = cv2.VideoCapture(video_path)
         fps = cap.get(cv2.CAP_PROP_FPS)
@@ -42,7 +42,7 @@ def _extract_frames_worker(
 
         if frame_extraction_mode == "time_interval":
             # 新模式：按时间间隔抽帧
-            time_interval = frame_interval_seconds
+            time_interval = frame_interval_seconds or 2.0
             next_frame_time = 0.0
             extracted_count = 0  # 初始化提取帧计数器
 
@@ -61,7 +61,7 @@ def _extract_frames_worker(
                     # 调整图像大小
                     if max(pil_image.size) > max_image_size:
                         ratio = max_image_size / max(pil_image.size)
-                        new_size = tuple(int(dim * ratio) for dim in pil_image.size)
+                        new_size = (int(pil_image.size[0] * ratio), int(pil_image.size[1] * ratio))
                         pil_image = pil_image.resize(new_size, Image.Resampling.LANCZOS)
 
                     # 转换为base64
@@ -240,6 +240,7 @@ class LegacyVideoAnalyzer:
             estimated_frames = min(self.max_frames, total_frames // frame_interval + 1)
         else:
             estimated_frames = self.max_frames
+            frame_interval = 1
 
         logger.info(f"计算得出帧间隔: {frame_interval} (将提取约{estimated_frames}帧)")
 
@@ -276,7 +277,7 @@ class LegacyVideoAnalyzer:
                 return await self._extract_frames_fallback(video_path)
 
             logger.info(f"✅ 成功提取{len(frames)}帧 (线程池模式)")
-            return frames
+            return frames  # type: ignore
 
         except Exception as e:
             logger.error(f"线程池帧提取失败: {e}")
@@ -315,7 +316,7 @@ class LegacyVideoAnalyzer:
                     # 调整图像大小
                     if max(pil_image.size) > self.max_image_size:
                         ratio = self.max_image_size / max(pil_image.size)
-                        new_size = tuple(int(dim * ratio) for dim in pil_image.size)
+                        new_size = (int(pil_image.size[0] * ratio), int(pil_image.size[1] * ratio))
                         pil_image = pil_image.resize(new_size, Image.Resampling.LANCZOS)
 
                     # 转换为base64
@@ -463,11 +464,11 @@ class LegacyVideoAnalyzer:
         # logger.info(f"✅ 多帧消息构建完成，包含{len(frames)}张图片")
 
         # 获取模型信息和客户端
-        model_info, api_provider, client = self.video_llm._select_model()
+        model_info, api_provider, client = self.video_llm._select_model()  # type: ignore
         # logger.info(f"使用模型: {model_info.name} 进行多帧分析")
 
         # 直接执行多图片请求
-        api_response = await self.video_llm._execute_request(
+        api_response = await self.video_llm._execute_request(  # type: ignore
             api_provider=api_provider,
             client=client,
             request_type=RequestType.RESPONSE,