🚀 Maaany things

src/engines/LLMEngine/OpenaiLLMEngine.py

@@ -1,49 +1,43 @@
-import openai
+import anthropic
 import gradio as gr
-import orjson
-
-from abc import ABC, abstractmethod
 
 from .BaseLLMEngine import BaseLLMEngine
 
-OPENAI_POSSIBLE_MODELS = [
-    "gpt-3.5-turbo-0125",
-    "gpt-4-turbo-preview",
+# Assuming these are the models supported by Anthropics that you wish to include
+ANTHROPIC_POSSIBLE_MODELS = [
+    "claude-2.1",
+    # Add more models as needed
 ]
 
-class OpenaiLLMEngine(BaseLLMEngine):
+class AnthropicsLLMEngine(BaseLLMEngine):
     num_options = 1
-    name = "OpenAI"
-    description = "OpenAI language model engine."
+    name = "Anthropics"
+    description = "Anthropics language model engine."
 
     def __init__(self, options: list) -> None:
         self.model = options[0]
+        self.client = anthropic.Anthropic(api_key="YourAnthropicAPIKeyHere")  # Ensure API key is securely managed
         super().__init__()
-    
-    def generate(self, system_prompt: str, chat_prompt: str, max_tokens: int = 512, temperature: float = 1.0, json_mode: bool= False, top_p: float = 1, frequency_penalty: float = 0, presence_penalty: float = 0) -> str:
-        response = openai.chat.completions.create(
-            model=self.model,
+
+    def generate(self, system_prompt: str, chat_prompt: str, max_tokens: int = 1024, temperature: float = 1.0, json_mode: bool = False, top_p: float = 1, frequency_penalty: float = 0, presence_penalty: float = 0) -> str | dict:
+        # Note: Adjust the parameters as per Anthropics API capabilities
+        message = self.client.messages.create(
+            max_tokens=max_tokens,
             messages=[
                 {"role": "system", "content": system_prompt},
                 {"role": "user", "content": chat_prompt},
             ],
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            frequency_penalty=frequency_penalty,
-            presence_penalty=presence_penalty,
-            response_format={ "type": "json_object" } if json_mode else openai._types.NOT_GIVEN
+            model=self.model,
         )
-        return response.choices[0].message.content if not json_mode else orjson.loads(response.choices[0].message.content)
-
+        return message.content
 
     @classmethod
     def get_options(cls) -> list:
         return [
             gr.Dropdown(
                 label="Model",
-                choices=OPENAI_POSSIBLE_MODELS,
+                choices=ANTHROPIC_POSSIBLE_MODELS,
                 max_choices=1,
-                value=OPENAI_POSSIBLE_MODELS[0]
+                value=ANTHROPIC_POSSIBLE_MODELS[0]
             )
-        ]
+        ]