Spaces:
Running
Running
| # -*- coding: utf-8 -*- | |
| __author__ = "Yash Kumar Lal, Github@ykl7" | |
| import os | |
| import openai | |
| from openai import OpenAI | |
| import anthropic | |
| import time | |
| import random | |
| random.seed(1234) | |
| class LLMReasoner(): | |
| def __init__(self, options): | |
| if options["model_family"] == "OpenAI": | |
| self.client = OpenAI(api_key=options["API_KEY"]) | |
| elif options["model_family"] == "Anthropic": | |
| os.environ["ANTHROPIC_API_KEY"] = options["API_KEY"] | |
| self.client = anthropic.Anthropic() | |
| self.model_family = options["model_family"] | |
| self.model_name = options["model_name"] | |
| self.max_tokens = options["max_tokens"] | |
| self.temp = 0.0 if "temperature" not in options else options["temperature"] | |
| self.top_p = 1.0 if "top_p" not in options else options["top_p"] | |
| self.frequency_penalty = 0.0 if "frequency_penalty" not in options else options["frequency_penalty"] | |
| self.presence_penalty = 0.0 if "presence_penalty" not in options else options["presence_penalty"] | |
| def make_openai_chat_completions_api_call(self, message): | |
| prompt = [{"role": "user", "content": message}] | |
| try: | |
| if "gpt-4o" in self.model_name: | |
| response = self.client.chat.completions.create( | |
| model=self.model_name, | |
| messages=prompt, | |
| temperature=self.temp, | |
| max_completion_tokens=self.max_tokens, | |
| top_p=self.top_p, | |
| frequency_penalty=self.frequency_penalty, | |
| presence_penalty=self.presence_penalty | |
| ) | |
| elif "o3-mini" in self.model_name: | |
| response = self.client.chat.completions.create( | |
| model=self.model_name, | |
| messages=prompt, | |
| reasoning_effort="medium" | |
| ) | |
| return self.parse_chat_completions_api_response(response) | |
| except openai.APIConnectionError as e: | |
| print("The server could not be reached") | |
| print(e.__cause__) # an underlying Exception, likely raised within httpx. | |
| time.sleep(60) | |
| return self.make_openai_chat_completions_api_call(prompt) | |
| except openai.RateLimitError as e: | |
| print("Rate limit error hit") | |
| exit() | |
| except openai.NotFoundError as e: | |
| print("Model not found") | |
| exit() | |
| except openai.APIStatusError as e: | |
| print("Another non-200-range status code was received") | |
| print(e.status_code) | |
| print(e) | |
| time.sleep(60) | |
| return self.make_openai_chat_completions_api_call(prompt) | |
| def parse_chat_completions_api_response(self, response): | |
| # print(response.model_dump()) | |
| choices = response.choices | |
| main_response = choices[0].message | |
| main_response_message, main_response_role = main_response.content, main_response.role | |
| return main_response_message, response | |
| def call_claude(self, claude_prompt=""): | |
| try: | |
| message = self.client.messages.create( | |
| model=self.model_name, | |
| max_tokens=self.max_tokens, | |
| temperature=self.temp, | |
| system="", | |
| messages=[ | |
| { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "type": "text", | |
| "text": claude_prompt | |
| } | |
| ] | |
| } | |
| ] | |
| ) | |
| except Exception as e: | |
| breakpoint() | |
| print(e) | |
| time.sleep(30) | |
| call_claude(self, claude_prompt) | |
| if message.content[0].type == "text": | |
| return message.content[0].text, message | |
| else: | |
| return "Error", message | |
| def run_inference(self, prompt=[]): | |
| if self.model_family == "OpenAI": | |
| response_text, response = self.make_openai_chat_completions_api_call(prompt) | |
| elif self.model_family == "Anthropic": | |
| response_text, response = self.call_claude(prompt) | |
| return response_text |