diff --git a/gui/window.py b/gui/window.py
index 76a9d6c..dc775c9 100644
--- a/gui/window.py
+++ b/gui/window.py
@@ -27,7 +27,7 @@ class MainWindow(QMainWindow):
#清空缓存
profile = QWebEngineProfile.defaultProfile()
profile.clearHttpCache()
- self.browser.load(QUrl('http://127.0.0.1:5000'))
+ self.browser.load(QUrl('http://192.168.1.111:5000'))
self.setCentralWidget(self.browser)
MyThread(target=self.runnable).start()
diff --git a/llm/VllmGPT.py b/llm/VllmGPT.py
index 45b75f1..1bd6119 100644
--- a/llm/VllmGPT.py
+++ b/llm/VllmGPT.py
@@ -4,7 +4,7 @@ import requests
class VllmGPT:
- def __init__(self, host="127.0.0.1",
+ def __init__(self, host="192.168.1.111",
port="8000",
model="THUDM/chatglm3-6b",
max_tokens="1024"):
@@ -17,7 +17,7 @@ class VllmGPT:
def question(self,cont):
chat_list = []
- url = "http://127.0.0.1:8101/v1/completions"
+ url = "http://192.168.1.111:8101/v1/completions"
req = json.dumps({
"model": "THUDM/chatglm3-6b",
"prompt": cont,
@@ -51,6 +51,6 @@ class VllmGPT:
return res['choices'][0]['message']['content']
if __name__ == "__main__":
- vllm = VllmGPT('127.0.0.1','8101','Qwen-7B-Chat')
+ vllm = VllmGPT('192.168.1.111','8101','Qwen-7B-Chat')
req = vllm.question2("你叫什么名字啊今年多大了")
print(req)
diff --git a/llm/agent/tools/SendWX.py b/llm/agent/tools/SendWX.py
index ccf9c5d..30dc15f 100644
--- a/llm/agent/tools/SendWX.py
+++ b/llm/agent/tools/SendWX.py
@@ -4,7 +4,7 @@ from langchain.tools import BaseTool
import requests
import json
-url = "http://127.0.0.1:4008/send"
+url = "http://192.168.1.111:4008/send"
headers = {'Content-Type': 'application/json'}
data = {
"message": "你好",
diff --git a/llm/nlp_ChatGLM3.py b/llm/nlp_ChatGLM3.py
index c0c25a5..ed892b9 100644
--- a/llm/nlp_ChatGLM3.py
+++ b/llm/nlp_ChatGLM3.py
@@ -25,7 +25,7 @@ def question(cont, uid=0, observation=""):
content = {
"prompt":"请简单回复我。" + cont,
"history":chat_list}
- url = "http://127.0.0.1:8000/v1/completions"
+ url = "http://192.168.1.111:8000/v1/completions"
req = json.dumps(content)
headers = {'content-type': 'application/json'}
r = requests.post(url, headers=headers, data=req)
diff --git a/llm/nlp_VisualGLM.py b/llm/nlp_VisualGLM.py
index 3efcc3f..c5131e0 100644
--- a/llm/nlp_VisualGLM.py
+++ b/llm/nlp_VisualGLM.py
@@ -25,7 +25,7 @@ def question(cont, uid=0, observation=""):
filepath = os.path.join(current_working_directory, "data", filename)
cv2.imwrite(filepath, img)
content["image"] = filepath
- url = "http://127.0.0.1:8080"
+ url = "http://192.168.1.111:8080"
print(content)
req = json.dumps(content)
headers = {'content-type': 'application/json'}
diff --git a/llm/nlp_privategpt.py b/llm/nlp_privategpt.py
index 04dcc47..38caa40 100644
--- a/llm/nlp_privategpt.py
+++ b/llm/nlp_privategpt.py
@@ -2,7 +2,7 @@ import hashlib
import os
from pgpt_python.client import PrivateGPTApi
-client = PrivateGPTApi(base_url="http://127.0.0.1:8001")
+client = PrivateGPTApi(base_url="http://192.168.1.111:8001")
index_name = "knowledge_data"
folder_path = "llm/privategpt/knowledge_base"
diff --git a/main.py b/main.py
index 94e8fe9..96aa67f 100644
--- a/main.py
+++ b/main.py
@@ -120,7 +120,7 @@ if __name__ == '__main__':
contentdb.init_db()
#ip替换
- if config_util.fay_url != "127.0.0.1":
+ if config_util.fay_url != "192.168.1.111":
replace_ip_in_file("gui/static/js/index.js", config_util.fay_url)
replace_ip_in_file("gui/static/js/setting.js", config_util.fay_url)
diff --git a/system.conf b/system.conf
index c1fbc84..80eb004 100644
--- a/system.conf
+++ b/system.conf
@@ -4,7 +4,7 @@
#建议使用funasr,请依照asr/funasr/README>md的说明启动
ASR_mode = funasr
#ASR二选一（需要运行fay/test/funasr服务）集成达摩院asr项目、感谢中科大脑算法工程师张聪聪提供集成代码
-local_asr_ip=127.0.0.1
+local_asr_ip=192.168.1.111
local_asr_port=10197
# ASR二选一（第1次运行建议用这个，免费3个月）， 阿里云 实时语音识别 服务密钥（必须）https://ai.aliyun.com/nls/trans
@@ -58,7 +58,7 @@ gpt_base_url=https://api.zyai.online/v1
#gpt model engine 如：gpt-3.5-turbo、moonshot-v1-8k
gpt_model_engine=gpt-3.5-turbo
-#gpt(fastgpt)代理(可为空，填写例子：127.0.0.1:7890)
+#gpt(fastgpt)代理(可为空，填写例子：192.168.1.111:7890)
proxy_config=
#通义星尘 服务密钥(NLP多选1) https://xingchen.aliyun.com/
@@ -66,7 +66,7 @@ xingchen_api_key=
xingchen_characterid=
#ollama服务 (NLP多选1)
-#ollama_ip 可以填写 127.0.0.1 或者 localhost
+#ollama_ip 可以填写 192.168.1.111 或者 localhost
ollama_ip = localhost
#ollama_model 可以填写 qwen:latest , llama2:latest , yi:lastest , pi:latest , gemma:latest
ollama_model = gemma:latest
@@ -75,8 +75,8 @@ ollama_model = gemma:latest
coze_bot_id=
coze_api_key=
-#启动模式common、web（服务器或docker请使用web方式，通过http://127.0.0.1:5000控制）
+#启动模式common、web（服务器或docker请使用web方式，通过http://192.168.1.111:5000控制）
start_mode=web
#服务器主动地址
-fay_url = 127.0.0.1
\ No newline at end of file
+fay_url = 192.168.1.111
\ No newline at end of file
diff --git a/test/ollama/README.md b/test/ollama/README.md
index bea28f8..9e2ac89 100644
--- a/test/ollama/README.md
+++ b/test/ollama/README.md
@@ -19,7 +19,7 @@ ollama pull qwen:latest #阿里开源模型
```
chat_module=ollama_api
-ollama_ip = 127.0.0.1
+ollama_ip = 192.168.1.111
ollama_model = phi:latest # llama2:latest , yi:lastest , pi:latest , gemma:latest (开源大语言模型多选1)
```
服务配置
diff --git a/test/ovr_lipsync/ffmpeg/doc/ffmpeg-all.html b/test/ovr_lipsync/ffmpeg/doc/ffmpeg-all.html
index 2d58c04..a72711a 100644
--- a/test/ovr_lipsync/ffmpeg/doc/ffmpeg-all.html
+++ b/test/ovr_lipsync/ffmpeg/doc/ffmpeg-all.html
@@ -23099,12 +23099,12 @@ an external server.
Example:
Create a localhost stream on port 5555:
-
ffmpeg -re -i input -f mpegts zmq:tcp://127.0.0.1:5555
+ffmpeg -re -i input -f mpegts zmq:tcp://192.168.1.111:5555
Multiple clients may connect to the stream using:
-
ffplay zmq:tcp://127.0.0.1:5555
+ffplay zmq:tcp://192.168.1.111:5555
Streaming to multiple clients is implemented using a ZeroMQ Pub-Sub pattern.
diff --git a/test/ovr_lipsync/ffmpeg/doc/ffmpeg-protocols.html b/test/ovr_lipsync/ffmpeg/doc/ffmpeg-protocols.html
index b892318..cba6377 100644
--- a/test/ovr_lipsync/ffmpeg/doc/ffmpeg-protocols.html
+++ b/test/ovr_lipsync/ffmpeg/doc/ffmpeg-protocols.html
@@ -2622,12 +2622,12 @@ an external server.
Example:
Create a localhost stream on port 5555:
-
ffmpeg -re -i input -f mpegts zmq:tcp://127.0.0.1:5555
+ffmpeg -re -i input -f mpegts zmq:tcp://192.168.1.111:5555
Multiple clients may connect to the stream using:
-
ffplay zmq:tcp://127.0.0.1:5555
+ffplay zmq:tcp://192.168.1.111:5555
Streaming to multiple clients is implemented using a ZeroMQ Pub-Sub pattern.
diff --git a/test/ovr_lipsync/ffmpeg/doc/ffplay-all.html b/test/ovr_lipsync/ffmpeg/doc/ffplay-all.html
index 2c57444..70765d1 100644
--- a/test/ovr_lipsync/ffmpeg/doc/ffplay-all.html
+++ b/test/ovr_lipsync/ffmpeg/doc/ffplay-all.html
@@ -11528,12 +11528,12 @@ an external server.
Example:
Create a localhost stream on port 5555:
-
ffmpeg -re -i input -f mpegts zmq:tcp://127.0.0.1:5555
+ffmpeg -re -i input -f mpegts zmq:tcp://192.168.1.111:5555
Multiple clients may connect to the stream using:
-
ffplay zmq:tcp://127.0.0.1:5555
+ffplay zmq:tcp://192.168.1.111:5555
Streaming to multiple clients is implemented using a ZeroMQ Pub-Sub pattern.
diff --git a/test/ovr_lipsync/ffmpeg/doc/ffprobe-all.html b/test/ovr_lipsync/ffmpeg/doc/ffprobe-all.html
index 528af1d..7a2d64c 100644
--- a/test/ovr_lipsync/ffmpeg/doc/ffprobe-all.html
+++ b/test/ovr_lipsync/ffmpeg/doc/ffprobe-all.html
@@ -11892,12 +11892,12 @@ an external server.
Example:
Create a localhost stream on port 5555:
-
ffmpeg -re -i input -f mpegts zmq:tcp://127.0.0.1:5555
+ffmpeg -re -i input -f mpegts zmq:tcp://192.168.1.111:5555
Multiple clients may connect to the stream using:
-
ffplay zmq:tcp://127.0.0.1:5555
+ffplay zmq:tcp://192.168.1.111:5555
Streaming to multiple clients is implemented using a ZeroMQ Pub-Sub pattern.
diff --git a/test/python_connector_demo/remote_audio.py b/test/python_connector_demo/remote_audio.py
index 8229b22..1ba3d12 100644
--- a/test/python_connector_demo/remote_audio.py
+++ b/test/python_connector_demo/remote_audio.py
@@ -58,7 +58,7 @@ def receive_audio(client):
if __name__ == "__main__":
client = socket.socket()
- client.connect(("127.0.0.1", 10001))
+ client.connect(("192.168.1.111", 10001))
# client.send(b"User")#指定用户名
# client.send(b"