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| from openai import OpenAI | |
| import os | |
| import time | |
| # from dotenv import dotenv_values | |
| # Load model and API endpoint from environment variables | |
| # config = dotenv_values(".env") | |
| # model = config.get("MODEL") | |
| # api_endpoint = config.get("API_ENDPOINT") | |
| model = "casperhansen/mixtral-instruct-awq" | |
| api_endpoint = "https://irlgzb4izhczxt-8000.proxy.runpod.net" | |
| openai_api_base = api_endpoint + '/v1' | |
| # Initialize the OpenAI client | |
| client = OpenAI( | |
| api_key="EMPTY", # Replace with your actual API key if required | |
| base_url=openai_api_base, | |
| ) | |
| def chat_completion_request(input): | |
| messages = [ | |
| {"role": "user", "content": f"{input}"}, | |
| ] | |
| # Create chat completions using the OpenAI client | |
| chat_response = client.chat.completions.create( | |
| model=model, | |
| messages=messages, | |
| temperature=0, | |
| max_tokens=500 | |
| ) | |
| # Extract the completion text from the response | |
| if chat_response.choices: | |
| completion_text = chat_response.choices[0].message.content | |
| else: | |
| completion_text = None | |
| return completion_text | |
| # # Test the function | |
| # messages = [ | |
| # {"role": "user", "content": "Write a long essay on the topic of spring."} | |
| # ] | |
| # chat_response = chat_completion_request_openai(messages, client) | |
| # messages.append({"role": "assistant", "content": chat_response}) |