"""An example showing how to use vLLM to serve multimodal models 
and run online inference with OpenAI client.

Launch the vLLM server with the following command:

(single image inference with Llava)
vllm serve llava-hf/llava-1.5-7b-hf --chat-template template_llava.jinja

(multi-image inference with Phi-3.5-vision-instruct)
vllm serve microsoft/Phi-3.5-vision-instruct --task generate \
    --trust-remote-code --max-model-len 4096 --limit-mm-per-prompt image=2

(audio inference with Ultravox)
vllm serve fixie-ai/ultravox-v0_3 --max-model-len 4096
"""
import base64

import requests
from openai import OpenAI

from vllm.assets.audio import AudioAsset
from vllm.utils import FlexibleArgumentParser

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

client = OpenAI(
    # defaults to os.environ.get("OPENAI_API_KEY")
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id


def encode_base64_content_from_url(content_url: str) -> str:
    """Encode a content retrieved from a remote url to base64 format."""

    with requests.get(content_url) as response:
        response.raise_for_status()
        result = base64.b64encode(response.content).decode('utf-8')

    return result


# Text-only inference
def run_text_only() -> None:
    chat_completion = client.chat.completions.create(
        messages=[{
            "role": "user",
            "content": "What's the capital of France?"
        }],
        model=model,
        max_completion_tokens=64,
    )

    result = chat_completion.choices[0].message.content
    print("Chat completion output:", result)


# Single-image input inference
def run_single_image() -> None:

    ## Use image url in the payload
    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"
    chat_completion_from_url = client.chat.completions.create(
        messages=[{
            "role":
            "user",
            "content": [
                {
                    "type": "text",
                    "text": "What's in this image?"
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": image_url
                    },
                },
            ],
        }],
        model=model,
        max_completion_tokens=64,
    )

    result = chat_completion_from_url.choices[0].message.content
    print("Chat completion output from image url:", result)

    ## Use base64 encoded image in the payload
    image_base64 = encode_base64_content_from_url(image_url)
    chat_completion_from_base64 = client.chat.completions.create(
        messages=[{
            "role":
            "user",
            "content": [
                {
                    "type": "text",
                    "text": "What's in this image?"
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{image_base64}"
                    },
                },
            ],
        }],
        model=model,
        max_completion_tokens=64,
    )

    result = chat_completion_from_base64.choices[0].message.content
    print("Chat completion output from base64 encoded image:", result)


# Multi-image input inference
def run_multi_image() -> None:
    image_url_duck = "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg"
    image_url_lion = "https://upload.wikimedia.org/wikipedia/commons/7/77/002_The_lion_king_Snyggve_in_the_Serengeti_National_Park_Photo_by_Giles_Laurent.jpg"
    chat_completion_from_url = client.chat.completions.create(
        messages=[{
            "role":
            "user",
            "content": [
                {
                    "type": "text",
                    "text": "What are the animals in these images?"
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": image_url_duck
                    },
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": image_url_lion
                    },
                },
            ],
        }],
        model=model,
        max_completion_tokens=64,
    )

    result = chat_completion_from_url.choices[0].message.content
    print("Chat completion output:", result)


# Audio input inference
def run_audio() -> None:
    # Any format supported by librosa is supported
    audio_url = AudioAsset("winning_call").url

    # Use audio url in the payload
    chat_completion_from_url = client.chat.completions.create(
        messages=[{
            "role":
            "user",
            "content": [
                {
                    "type": "text",
                    "text": "What's in this audio?"
                },
                {
                    "type": "audio_url",
                    "audio_url": {
                        "url": audio_url
                    },
                },
            ],
        }],
        model=model,
        max_completion_tokens=64,
    )

    result = chat_completion_from_url.choices[0].message.content
    print("Chat completion output from audio url:", result)

    audio_base64 = encode_base64_content_from_url(audio_url)
    chat_completion_from_base64 = client.chat.completions.create(
        messages=[{
            "role":
            "user",
            "content": [
                {
                    "type": "text",
                    "text": "What's in this audio?"
                },
                {
                    "type": "audio_url",
                    "audio_url": {
                        # Any format supported by librosa is supported
                        "url": f"data:audio/ogg;base64,{audio_base64}"
                    },
                },
            ],
        }],
        model=model,
        max_completion_tokens=64,
    )

    result = chat_completion_from_base64.choices[0].message.content
    print("Chat completion output from base64 encoded audio:", result)


example_function_map = {
    "text-only": run_text_only,
    "single-image": run_single_image,
    "multi-image": run_multi_image,
    "audio": run_audio,
}


def main(args) -> None:
    chat_type = args.chat_type
    example_function_map[chat_type]()


if __name__ == "__main__":
    parser = FlexibleArgumentParser(
        description='Demo on using OpenAI client for online inference with '
        'multimodal language models served with vLLM.')
    parser.add_argument(
        '--chat-type',
        '-c',
        type=str,
        default="single-image",
        choices=["text-only", "single-image", "multi-image", "audio"],
        help='Conversation type with multimodal data.')
    args = parser.parse_args()
    main(args)
