Log configuration fixed

README.md

@@ -34,6 +34,11 @@ python main.py
 ## Configuration
 Create ".env.yaml" at the root of jarvis-models, and copy the following yaml configuration
 ```yaml
+log:
+  level: debug
+  time_format: "%Y-%m-%d %H:%M:%S"
+  filename: "D:/Workspace/Logging/jarvis/jarvis-models.log"
+
 melotts:
   url: http://{IP running docker melotts-api}:18080/convert/tts
 

main.py

@@ -14,7 +14,7 @@ class Main():
     def run(self):
         logger = logging.getLogger(__name__)
         logger.info("jarvis-models start", extra={"version": "0.0.1"})
-        uvicorn.run("server:app", host="0.0.0.0", port=8001, log_level="info")
+        uvicorn.run("server:app", host="0.0.0.0", port=8000, log_level="info")
 
 if __name__ == "__main__":
     injector = Injector()

src/blackbox/blackbox_factory.py

@@ -1,3 +1,4 @@
+from . import melotts
 from .audio_chat import AudioChat
 from .sentiment import Sentiment
 from .tts import TTS
@@ -37,6 +38,7 @@ class BlackboxFactory:
                  #chroma_query: ChromaQuery,
                  #chroma_upsert: ChromaUpsert,
                  #chroma_chat: ChromaChat,
+                 melotts: MeloTTS,
                  vlms: VLMS) -> None:
         self.models["audio_to_text"] = audio_to_text
         self.models["text_to_audio"] = text_to_audio
@@ -52,6 +54,7 @@ class BlackboxFactory:
         #self.models["chroma_query"] = chroma_query
         #self.models["chroma_upsert"] = chroma_upsert
         #self.models["chroma_chat"] = chroma_chat
+        self.models["melotts"] = melotts
         self.models["vlms"] = vlms
 
     def __call__(self, *args, **kwargs):

src/blackbox/g2e.py

@@ -42,17 +42,17 @@ class G2E(Blackbox):
 
         KOMBUKIKI康普茶价格 内地常规版：25 RMB 澳门常规版：28-29 MOP'''
 
-        prompt1 = ''''你是琪琪，活泼的康普茶看板娘，同时你对澳门十分熟悉，是一个澳门旅游专家，请回答任何关于澳门旅游的问题，回答尽量简练明了。
+        prompt1 = '''你是琪琪，活泼的康普茶看板娘，同时你对澳门十分熟悉，是一个澳门旅游专家，请回答任何关于澳门旅游的问题，回答尽量简练明了。'''
-        '''
-        inject_prompt = '(用活泼的语气说话回答，回答严格限制50字以内)'
 
+        #inject_prompt = '(用活泼的语气说话回答，回答严格限制50字以内)'
+        inject_prompt = '(回答简练，不要输出重复内容，只讲中文)'
 
-        prompt_template = [
-           {"role": "system", "content": background_prompt + prompt1},
-        ]
         #prompt_template = [
-        #    {"role": "system", "content": ''},
+        #   {"role": "system", "content": background_prompt + prompt1},
         #]
+        prompt_template = [
+            {"role": "system", "content": prompt1},
+        ]
 
 
         messages = prompt_template + context + [
@@ -61,6 +61,8 @@ class G2E(Blackbox):
                     "content": prompt + inject_prompt
                 }
             ]
+        print("**** History with current prompt input ： ****")
+        print(messages)
         client = OpenAI(
             api_key='YOUR_API_KEY',
             base_url=url
@@ -68,16 +70,19 @@ class G2E(Blackbox):
         #model_name = client.models.list().data[0].id
         model_name = client.models.list().data[1].id
         print(model_name)
+
         response = client.chat.completions.create(
         model=model_name,
         messages=messages,
             temperature=0.8,
-            top_p=0.8,
+            top_p=0.8
-            # max_tokens = 50
+            #frequency_penalty=0.5,
+            #presence_penalty=0.8,
+            #stop=100
         )
 
         fastchat_content = response.choices[0].message.content
-
+        print("*** Model response: " + fastchat_content + " ***")
         return fastchat_content
     
     async def fast_api_handler(self, request: Request) -> Response: