convert openai service to new models

src/dailyai/services/azure_ai_services.py

@@ -66,11 +66,10 @@ class AzureLLMService(LLMService):
         )
 
     def run_llm_async(self, messages) -> Generator[str, None, None]:
-        local_messages = messages.copy()
-        messages_for_log = json.dumps(local_messages)
+        messages_for_log = json.dumps(messages)
         self.logger.debug(f"Generating chat via azure: {messages_for_log}")
 
-        response = self.get_response(local_messages, stream=True)
+        response = self.get_response(messages, stream=True)
 
         for chunk in response:
             if len(chunk.choices) == 0:
@@ -80,11 +79,10 @@ class AzureLLMService(LLMService):
                 yield chunk.choices[0].delta.content
 
     def run_llm(self, messages) -> str | None:
-        local_messages = messages.copy()
-        messages_for_log = json.dumps(local_messages)
+        messages_for_log = json.dumps(messages)
         self.logger.debug(f"Generating chat via azure: {messages_for_log}")
 
-        response = self.get_response(local_messages, stream=False)
+        response = self.get_response(messages, stream=False)
         if response and len(response.choices) > 0:
             return response.choices[0].message.content
         else:

src/dailyai/services/open_ai_service.py

@@ -0,0 +1,67 @@
+from dailyai.services.ai_services import AIService, TTSService, LLMService, ImageGenService
+from typing import Generator
+
+import requests
+from PIL import Image
+import io
+from openai import OpenAI
+
+import os
+import json
+
+class OpenAILLMService(LLMService):
+    def __init__(self, api_key=None, model=None):
+        super().__init__()
+        api_key = api_key or os.getenv("OPEN_AI_KEY")
+        self.model = model or os.getenv("OPEN_AI_MODEL")
+        self.client = OpenAI(api_key=api_key)
+
+    def get_response(self, messages, stream):
+        return self.client.chat.completions.create(
+            stream=stream,
+            messages=messages,
+            model=self.model
+        )
+
+    def run_llm_async(self, messages) -> Generator[str, None, None]:
+        messages_for_log = json.dumps(messages)
+        self.logger.debug(f"Generating chat via openai: {messages_for_log}")
+
+        response = self.get_response(messages, stream=True)
+
+        for chunk in response:
+            if len(chunk.choices) == 0:
+                continue
+
+            if chunk.choices[0].delta.content:
+                yield chunk.choices[0].delta.content
+
+    def run_llm(self, messages) -> str | None:
+        messages_for_log = json.dumps(messages)
+        self.logger.debug(f"Generating chat via azure: {messages_for_log}")
+
+        response = self.get_response(messages, stream=False)
+        if response and len(response.choices) > 0:
+            return response.choices[0].message.content
+        else:
+            return None
+
+class OpenAIImageGenService(ImageGenService):
+    def __init__(self, api_key=None, model=None):
+        super().__init__()
+        api_key = api_key or os.getenv("OPEN_AI_KEY")
+        self.model = model or os.getenv("OPEN_AI_MODEL")
+        self.client = OpenAI(api_key=api_key)
+
+    def run_image_gen(self, sentence) -> tuple[str, Image.Image]:
+        image = self.client.images.generate(
+            prompt=sentence,
+            n=1,
+            size=f"1024x1024"
+        )
+        image_url = image.data[0].url
+        response = requests.get(image_url)
+        dalle_stream = io.BytesIO(response.content)
+        dalle_im = Image.open(dalle_stream)
+
+        return (image_url, dalle_im)

src/dailyai/services/to_be_updated/open_ai_service.py

@@ -1,55 +0,0 @@
-from services.ai_service import AIService
-import requests
-from PIL import Image
-import io
-from openai import OpenAI
-
-client = OpenAI()
-import os
-import time
-import json
-
-class OpenAIService(AIService):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    def run_llm(self, messages, latest_user_message=None, stream = True):
-        local_messages = messages.copy()
-        if latest_user_message:
-            local_messages.append({"role": "user", "content": latest_user_message})
-        messages_for_log = json.dumps(local_messages, indent=2)
-        self.logger.info(f"==== generating chat via openai: {messages_for_log}")
-
-        model = os.getenv("OPEN_AI_MODEL")
-        if not model:
-            model = "gpt-4"
-        response = client.chat.completions.create(api_type = 'openai',
-        api_version = '2020-11-07',
-        api_base = "https://api.openai.com/v1",
-        api_key = os.getenv("OPEN_AI_KEY"),
-        model=model,
-        stream=stream,
-        messages=local_messages)
-
-        return response
-
-    def run_image_gen(self, sentence):
-        self.logger.info("🖌️ generating openai image async for ", sentence)
-        start = time.time()
-
-        image = client.images.generate(api_type = 'openai',
-        api_version = '2020-11-07',
-        api_base = "https://api.openai.com/v1",
-        api_key = os.getenv("OPEN_AI_KEY"),
-        prompt=f'{sentence} in the style of {self.image_style}',
-        n=1,
-        size=f"1024x1024")
-        image_url = image["data"][0]["url"]
-        self.logger.info("🖌️ generated image from url", image["data"][0]["url"])
-        response = requests.get(image_url)
-        self.logger.info("🖌️ got image from url", response)
-        dalle_stream = io.BytesIO(response.content)
-        dalle_im = Image.open(dalle_stream)
-        self.logger.info("🖌️ total time", time.time() - start)
-
-        return (image_url, dalle_im)