"""
Example of directly using modal processors

This example demonstrates how to use LightRAG's modal processors directly without going through MinerU.
"""

import asyncio
import argparse
from lightrag.llm.openai import openai_complete_if_cache, openai_embed
from lightrag import LightRAG
from lightrag.utils import EmbeddingFunc
from raganything.modalprocessors import (
    ImageModalProcessor,
    TableModalProcessor,
    EquationModalProcessor,
)

WORKING_DIR = "./rag_storage"


def get_llm_model_func(api_key: str, base_url: str = None):
    return (
        lambda prompt,
        system_prompt=None,
        history_messages=[],
        **kwargs: openai_complete_if_cache(
            "gpt-4o-mini",
            prompt,
            system_prompt=system_prompt,
            history_messages=history_messages,
            api_key=api_key,
            base_url=base_url,
            **kwargs,
        )
    )


def get_vision_model_func(api_key: str, base_url: str = None):
    return (
        lambda prompt,
        system_prompt=None,
        history_messages=[],
        image_data=None,
        **kwargs: openai_complete_if_cache(
            "gpt-4o",
            "",
            system_prompt=None,
            history_messages=[],
            messages=[
                {"role": "system", "content": system_prompt} if system_prompt else None,
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": prompt},
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": f"data:image/jpeg;base64,{image_data}"
                            },
                        },
                    ],
                }
                if image_data
                else {"role": "user", "content": prompt},
            ],
            api_key=api_key,
            base_url=base_url,
            **kwargs,
        )
        if image_data
        else openai_complete_if_cache(
            "gpt-4o-mini",
            prompt,
            system_prompt=system_prompt,
            history_messages=history_messages,
            api_key=api_key,
            base_url=base_url,
            **kwargs,
        )
    )


async def process_image_example(lightrag: LightRAG, vision_model_func):
    """Example of processing an image"""
    # Create image processor
    image_processor = ImageModalProcessor(
        lightrag=lightrag, modal_caption_func=vision_model_func
    )

    # Prepare image content
    image_content = {
        "img_path": "image.jpg",
        "img_caption": ["Example image caption"],
        "img_footnote": ["Example image footnote"],
    }

    # Process image
    description, entity_info = await image_processor.process_multimodal_content(
        modal_content=image_content,
        content_type="image",
        file_path="image_example.jpg",
        entity_name="Example Image",
    )

    print("Image Processing Results:")
    print(f"Description: {description}")
    print(f"Entity Info: {entity_info}")


async def process_table_example(lightrag: LightRAG, llm_model_func):
    """Example of processing a table"""
    # Create table processor
    table_processor = TableModalProcessor(
        lightrag=lightrag, modal_caption_func=llm_model_func
    )

    # Prepare table content
    table_content = {
        "table_body": """
        | Name | Age | Occupation |
        |------|-----|------------|
        | John | 25  | Engineer   |
        | Mary | 30  | Designer   |
        """,
        "table_caption": ["Employee Information Table"],
        "table_footnote": ["Data updated as of 2024"],
    }

    # Process table
    description, entity_info = await table_processor.process_multimodal_content(
        modal_content=table_content,
        content_type="table",
        file_path="table_example.md",
        entity_name="Employee Table",
    )

    print("\nTable Processing Results:")
    print(f"Description: {description}")
    print(f"Entity Info: {entity_info}")


async def process_equation_example(lightrag: LightRAG, llm_model_func):
    """Example of processing a mathematical equation"""
    # Create equation processor
    equation_processor = EquationModalProcessor(
        lightrag=lightrag, modal_caption_func=llm_model_func
    )

    # Prepare equation content
    equation_content = {"text": "E = mc^2", "text_format": "LaTeX"}

    # Process equation
    description, entity_info = await equation_processor.process_multimodal_content(
        modal_content=equation_content,
        content_type="equation",
        file_path="equation_example.txt",
        entity_name="Mass-Energy Equivalence",
    )

    print("\nEquation Processing Results:")
    print(f"Description: {description}")
    print(f"Entity Info: {entity_info}")


async def initialize_rag(api_key: str, base_url: str = None):
    rag = LightRAG(
        working_dir=WORKING_DIR,
        embedding_func=EmbeddingFunc(
            embedding_dim=3072,
            max_token_size=8192,
            func=lambda texts: openai_embed(
                texts,
                model="text-embedding-3-large",
                api_key=api_key,
                base_url=base_url,
            ),
        ),
        llm_model_func=lambda prompt,
        system_prompt=None,
        history_messages=[],
        **kwargs: openai_complete_if_cache(
            "gpt-4o-mini",
            prompt,
            system_prompt=system_prompt,
            history_messages=history_messages,
            api_key=api_key,
            base_url=base_url,
            **kwargs,
        ),
    )

    await rag.initialize_storages()  # Auto-initializes pipeline_status
    return rag


def main():
    """Main function to run the example"""
    parser = argparse.ArgumentParser(description="Modal Processors Example")
    parser.add_argument("--api-key", required=True, help="OpenAI API key")
    parser.add_argument("--base-url", help="Optional base URL for API")
    parser.add_argument(
        "--working-dir", "-w", default=WORKING_DIR, help="Working directory path"
    )

    args = parser.parse_args()

    # Run examples
    asyncio.run(main_async(args.api_key, args.base_url))


async def main_async(api_key: str, base_url: str = None):
    # Initialize LightRAG
    lightrag = await initialize_rag(api_key, base_url)

    # Get model functions
    llm_model_func = get_llm_model_func(api_key, base_url)
    vision_model_func = get_vision_model_func(api_key, base_url)

    # Run examples
    await process_image_example(lightrag, vision_model_func)
    await process_table_example(lightrag, llm_model_func)
    await process_equation_example(lightrag, llm_model_func)


if __name__ == "__main__":
    main()
