'commit'

AI/WxMini/Start.py

@@ -602,24 +602,67 @@ async def generate_upload_params(current_user: dict = Depends(get_current_user))
 
 
 @app.get("/aichat/recognize_content")
-async def web_recognize_content(image_url: str, current_user: dict = Depends(get_current_user)):
-    logger.info(f"current_user:{current_user['login_name']}")
-    person_id = current_user['person_id']
-    try:
-        async def generate_stream():
-            # 假设 recognize_content 是一个异步生成器，逐条返回识别结果
-            async for result in recognize_content(client, app.state.mysql_pool, person_id, image_url):
-                yield f"{str(result)}"  # 使用SSE格式
-                # 控制输出速度，间隔0.01秒
-                await asyncio.sleep(0.01)
+async def web_recognize_content(image_url: str,
+                                #current_user: dict = Depends(get_current_user)
+                                ):
+    #logger.info(f"current_user:{current_user['login_name']}")
+    #person_id = current_user['person_id']
+    person_id='0F66D9C5-428C-FE03-67EE-E5BEE46CB495'
+    # 获取图片宽高
+    image_width, image_height = getImgWidthHeight(image_url)
+
+    # 调用 AI 模型生成内容（流式输出）
+    response = await client.chat.completions.create(
+        model="qwen-vl-plus",
+        messages=[{"role": "user", "content": [
+            {"type": "text", "text": "这是什么"},
+            {"type": "image_url", "image_url": {"url": image_url}}
+        ]}],
+        stream=True
+    )
 
-        return StreamingResponse(
-            generate_stream(),
-            media_type="text/event-stream",  # 使用SSE的media_type
-            headers={"Cache-Control": "no-cache", "Connection": "keep-alive"}  # 禁用缓存，保持连接
-        )
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+    # 定义一个生成器函数，用于逐字返回流式结果
+    async def generate_stream():
+        summary = ""  # 用于存储最终拼接的字符串
+        try:
+            # 逐块处理 AI 返回的内容
+            async for chunk in response:
+                if chunk.choices[0].delta.content:
+                    chunk_content = chunk.choices[0].delta.content
+                    # 逐字返回
+                    for char in chunk_content:
+                        print(char, end="", flush=True)  # 打印到控制台
+                        yield char.encode("utf-8")  # 逐字返回
+                        summary += char  # 拼接字符
+
+            # 流式传输完成后，记录到数据库
+            await save_chat_to_mysql(
+                app.state.mysql_pool, person_id, f'{image_url}', summary, "", 0, 2, 2, 2, image_width, image_height
+            )
+
+        except asyncio.CancelledError:
+            # 客户端提前断开连接，无需处理
+            print("客户端断开连接")
+        except Exception as e:
+            error_response = json.dumps({
+                "success": False,
+                "message": f"生成内容失败: {str(e)}"
+            })
+            print(error_response)
+            yield error_response.encode("utf-8")
+
+    # 使用 StreamingResponse 返回流式结果
+    return StreamingResponse(
+        generate_stream(),
+        media_type="text/plain; charset=utf-8",  # 明确指定字符编码为 UTF-8
+        headers={
+            "Cache-Control": "no-cache",  # 禁用缓存
+            "Content-Type": "text/event-stream; charset=utf-8",  # 设置内容类型和字符编码
+            "Transfer-Encoding": "chunked",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",  # 禁用 Nginx 缓冲（如果使用 Nginx）
+        }
+    )
 
 
 @app.get("/aichat/recognize_text")

AI/WxMini/Utils/ImageUtil.py

@@ -53,36 +53,6 @@ async def recognize_text(client, pool, person_id, image_url):
         print(f"记录到数据库时出错：{e}")
 
 
-async def recognize_content(client, pool, person_id, image_url):
-    """
-    识别图片中的内容，流式输出
-    """
-    completion = await client.chat.completions.create(
-        model="qwen-vl-plus",
-        messages=[{"role": "user", "content": [
-            {"type": "text", "text": "这是什么"},
-            {"type": "image_url", "image_url": {"url": image_url}}
-        ]}],
-        stream=True
-    )
-    full_text = ""  # 用于存储最终拼接的字符串
-    async for chunk in completion:
-        if chunk.choices[0].delta.content is not None:
-            for char in chunk.choices[0].delta.content:
-                if char != ' ':
-                    yield char  # 流式输出字符
-                    full_text += char  # 拼接字符
-                    # print(char, end='')
-                    time.sleep(0.1)  # 控制输出速度
-
-    # 获取图片宽高
-    image_width, image_height = getImgWidthHeight(image_url)
-    # 记录到数据库
-    try:
-        await save_chat_to_mysql(pool, person_id, f'{image_url}', full_text, "", 0, 2, 2, 2, image_width, image_height)
-    except Exception as e:
-        print(f"记录到数据库时出错：{e}")
-
 
 async def recognize_math(pool, person_id, image_url):
     client = AsyncOpenAI(