embodiedgen/embodied_gen/utils/gpt_clients.py

# Project EmbodiedGen
#
# Copyright (c) 2025 Horizon Robotics. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied. See the License for the specific language governing
# permissions and limitations under the License.


import base64
import logging
import os
from io import BytesIO
from typing import Optional

import yaml
from openai import AzureOpenAI, OpenAI  # pip install openai
from PIL import Image
from tenacity import (
    retry,
    stop_after_attempt,
    stop_after_delay,
    wait_random_exponential,
)
from embodied_gen.utils.process_media import combine_images_to_base64

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


class GPTclient:
    """A client to interact with the GPT model via OpenAI or Azure API."""

    def __init__(
        self,
        endpoint: str,
        api_key: str,
        model_name: str = "yfb-gpt-4o",
        api_version: str = None,
        verbose: bool = False,
    ):
        if api_version is not None:
            self.client = AzureOpenAI(
                azure_endpoint=endpoint,
                api_key=api_key,
                api_version=api_version,
            )
        else:
            self.client = OpenAI(
                base_url=endpoint,
                api_key=api_key,
            )

        self.endpoint = endpoint
        self.model_name = model_name
        self.image_formats = {".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif"}
        self.verbose = verbose
        logger.info(f"Using GPT model: {self.model_name}.")

    @retry(
        wait=wait_random_exponential(min=1, max=20),
        stop=(stop_after_attempt(10) | stop_after_delay(30)),
    )
    def completion_with_backoff(self, **kwargs):
        return self.client.chat.completions.create(**kwargs)

    def query(
        self,
        text_prompt: str,
        image_base64: Optional[list[str | Image.Image]] = None,
        system_role: Optional[str] = None,
    ) -> Optional[str]:
        """Queries the GPT model with a text and optional image prompts.

        Args:
            text_prompt (str): The main text input that the model responds to.
            image_base64 (Optional[List[str]]): A list of image base64 strings
                or local image paths or PIL.Image to accompany the text prompt.
            system_role (Optional[str]): Optional system-level instructions
                that specify the behavior of the assistant.

        Returns:
            Optional[str]: The response content generated by the model based on
                the prompt. Returns `None` if an error occurs.
        """
        if system_role is None:
            system_role = "You are a highly knowledgeable assistant specializing in physics, engineering, and object properties."  # noqa

        content_user = [
            {
                "type": "text",
                "text": text_prompt,
            },
        ]

        # Process images if provided
        if image_base64 is not None:
            image_base64 = (
                image_base64
                if isinstance(image_base64, list)
                else [image_base64]
            )
            for img in image_base64:
                if isinstance(img, Image.Image):
                    buffer = BytesIO()
                    img.save(buffer, format=img.format or "PNG")
                    buffer.seek(0)
                    image_binary = buffer.read()
                    img = base64.b64encode(image_binary).decode("utf-8")
                elif (
                    len(os.path.splitext(img)) > 1
                    and os.path.splitext(img)[-1].lower() in self.image_formats
                ):
                    if not os.path.exists(img):
                        raise FileNotFoundError(f"Image file not found: {img}")
                    with open(img, "rb") as f:
                        img = base64.b64encode(f.read()).decode("utf-8")

                content_user.append(
                    {
                        "type": "image_url",
                        "image_url": {"url": f"data:image/png;base64,{img}"},
                    }
                )

        payload = {
            "messages": [
                {"role": "system", "content": system_role},
                {"role": "user", "content": content_user},
            ],
            "temperature": 0.1,
            "max_tokens": 500,
            "top_p": 0.1,
            "frequency_penalty": 0,
            "presence_penalty": 0,
            "stop": None,
        }
        payload.update({"model": self.model_name})

        response = None
        try:
            response = self.completion_with_backoff(**payload)
            response = response.choices[0].message.content
        except Exception as e:
            logger.error(f"Error GPTclint {self.endpoint} API call: {e}")
            response = None

        if self.verbose:
            logger.info(f"Prompt: {text_prompt}")
            logger.info(f"Response: {response}")

        return response


with open("embodied_gen/utils/gpt_config.yaml", "r") as f:
    config = yaml.safe_load(f)

agent_type = config["agent_type"]
agent_config = config.get(agent_type, {})

# Prefer environment variables, fallback to YAML config
endpoint = os.environ.get("ENDPOINT", agent_config.get("endpoint"))
api_key = os.environ.get("API_KEY", agent_config.get("api_key"))
api_version = os.environ.get("API_VERSION", agent_config.get("api_version"))
model_name = os.environ.get("MODEL_NAME", agent_config.get("model_name"))

GPT_CLIENT = GPTclient(
    endpoint=endpoint,
    api_key=api_key,
    api_version=api_version,
    model_name=model_name,
)

if __name__ == "__main__":
    if "openrouter" in GPT_CLIENT.endpoint:
        response = GPT_CLIENT.query(
            text_prompt="What is the content in each image?",
            image_base64=combine_images_to_base64(
                [
                    "apps/assets/example_image/sample_02.jpg",
                    "apps/assets/example_image/sample_03.jpg",
                ]
            ),  # input raw image_path if only one image
        )
        print(response)
    else:
        response = GPT_CLIENT.query(
            text_prompt="What is the content in the images?",
            image_base64=[
                Image.open("apps/assets/example_image/sample_02.jpg"),
                Image.open("apps/assets/example_image/sample_03.jpg"),
            ],
        )
        print(response)

        # test2: text prompt
        response = GPT_CLIENT.query(
            text_prompt="What is the capital of China?"
        )
        print(response)