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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}) |