openai_api.py

import base64
import os

from dotenv import load_dotenv

load_dotenv()

from openai import OpenAI

client = OpenAI(api_key=os.environ.get('OPENAI_API_KEY'))

system_message = "You are a helpful assistant"

def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')
    
def encode_frame(frame):
    return base64.b64encode(frame).decode('utf-8')
    
def encode_images(image_paths, frames=False):
    if frames is True:
        return [f"data:image/jpeg;base64,{encode_frame(synthesized_frame)}" for synthesized_frame in image_paths]    
    return [f"data:image/jpeg;base64,{encode_image(image_path)}" for image_path in image_paths]

def test_gpt():
    return chatgpt("What is the meaning of life?")

def chatgpt(query, model="gpt-4-1106-preview"):
    response = client.chat.completions.create(
            messages=[
                {
                    "role": "user",
                    "content": query,
                }
            ],
            model=model
        )
    
    response = response.choices[0].message.content
    return response 

def test_vision_api():
    client = OpenAI()

    response = client.chat.completions.create(
        model="gpt-4-vision-preview",
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What’s in this image?"},
                    {
                        "type": "image_url",
                        "image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
                    },
                ],
            }
        ],
        max_tokens=300,
    )

    return response.choices[0].message.content


def vision_api(images, prompt):
    content = [{
        "type": "text",
        "text": prompt
    }]
    for image in images:
        content.append({
            "type": "image_url",
            "image_url": image
        })
    response = client.chat.completions.create(
        model="gpt-4-vision-preview",
        messages=[
            {
                "role": "user",
                "content": content,
            }
        ],
        max_tokens=1000,
    )
    return response.choices[0].message.content


if __name__ == "__main__":
    # response = test_vision_api()
    response = test_gpt()
    print(response)