'commit'

AI/WxMini/Start.py

@@ -247,23 +247,6 @@ async def reply(person_id: str = Form(...),
                 "success": False
             }
 
-        if '生成图片' in prompt or '生图' in prompt:
-            success, key = generate_image(prompt)
-            if success:
-                image_url = f"{OSS_PREFIX}{key}"
-                image_width, image_height = getImgWidthHeight(image_url)
-                # 记录聊天数据到 MySQL
-                await save_chat_to_mysql(app.state.mysql_pool, person_id, prompt,
-                                         key, '', 0, input_type=2, output_type=4,
-                                         input_image_type=0, image_width=image_width, image_height=image_height)
-                # 返回数据
-                return {
-                    "success": True,
-                    "url": None,
-                    "duration": 0,  # 返回大模型的回复时长
-                    "response": key,  # 返回大模型的回复
-                }
-
         # 从连接池中获取一个连接
         connection = milvus_pool.get_connection()
 
@@ -315,14 +298,14 @@ async def reply(person_id: str = Form(...),
         # 拼接交互提示词
         if '天气' in prompt or '降温' in prompt or '气温' in prompt or '下雨' in prompt or '下雪' in prompt or '风' in prompt:
             weather_info = await get_weather('长春')
-            history_prompt += f"天气信息: {weather_info}\n"
+            history_prompt = f"天气信息: {weather_info}\n"
 
         logger.info(f"历史交互提示词: {history_prompt}")
 
         # NBA与CBA
         result = await get_news(client, prompt)
         if result is not None:
-            history_prompt += result
+            history_prompt = result
             print("新闻返回了下面的内容：" + result)
 
         # 调用大模型，将历史交互作为提示词

AI/WxMini/Utils/NewsUtil.py

@@ -6,19 +6,22 @@ import requests
 from bs4 import BeautifulSoup
 from fastapi import HTTPException
 from WxMini.Milvus.Config.MulvusConfig import *
+
 # 配置日志
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 logger = logging.getLogger(__name__)
 
+
 # 分词与关键词提取
 def extract_keywords(user_input):
     # 使用 jieba 进行分词
     words = jieba.lcut(user_input)
     # 过滤掉无意义的词（如标点符号、停用词等）
-    stop_words = ['的', '了', '吗', '呢', '是', '在', '啊', '呀', '怎么', '怎么样', '？', '，', '。']
+    stop_words = ['的', '了', '吗', '呢', '是', '在', '啊', '呀', '怎么', '怎么样', '今天','今日', '？', '，', '。']
     keywords = [word for word in words if word not in stop_words]
     return keywords
 
+
 # 搜索新浪新闻
 def search_sina_news(keyword='新闻', time='d', page=1):
     results = []
@@ -49,6 +52,7 @@ def search_sina_news(keyword='新闻', time='d', page=1):
         logger.error(f"请求新浪新闻时出错: {e}")
     return results
 
+
 # 根据关键词调用搜索函数
 def search_based_on_keywords(keywords):
     if not keywords:
@@ -56,6 +60,7 @@ def search_based_on_keywords(keywords):
     # 使用第一个关键词进行搜索
     return search_sina_news(keyword=keywords[0], time='d', page=1)
 
+
 # 使用AI整理搜索结果
 async def format_results_with_ai(client, results):
     if not results:
@@ -78,6 +83,7 @@ async def format_results_with_ai(client, results):
         logger.error("大模型调用超时")
         raise HTTPException(status_code=500, detail="大模型调用超时")
 
+
 # 主函数
 async def get_news(client, user_input):
     keywords = extract_keywords(user_input)
@@ -87,4 +93,3 @@ async def get_news(client, user_input):
         return formatted_response
     else:
         return "未找到相关新闻。"
-

AI/WxMini/安装.txt

@@ -4,4 +4,22 @@ https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/Tencent_AILab_ChineseE
 # 显示已安装包的版本号
 pip show gensim
 pip show jieba
-pip show pymilvus
+pip show pymilvus
+
+
+        if '生成图片' in prompt or '生图' in prompt:
+            success, key = generate_image(prompt)
+            if success:
+                image_url = f"{OSS_PREFIX}{key}"
+                image_width, image_height = getImgWidthHeight(image_url)
+                # 记录聊天数据到 MySQL
+                await save_chat_to_mysql(app.state.mysql_pool, person_id, prompt,
+                                         key, '', 0, input_type=2, output_type=4,
+                                         input_image_type=0, image_width=image_width, image_height=image_height)
+                # 返回数据
+                return {
+                    "success": True,
+                    "url": None,
+                    "duration": 0,  # 返回大模型的回复时长
+                    "response": key,  # 返回大模型的回复
+                }