#!/usr/bin/env python3
import os
import uuid
import json
import requests
from loguru import logger

from magic_pdf.data.dataset import PymuDocDataset
from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
from magic_pdf.data.io.s3 import S3Writer
from magic_pdf.data.data_reader_writer.base import DataWriter

from inference_svm_model import SVMModel

class Processor:
    def __init__(self):
        self.s3_writer = S3Writer(
            ak=os.getenv("S3_ACCESS_KEY"),
            sk=os.getenv("S3_SECRET_KEY"),
            bucket=os.getenv("S3_BUCKET_NAME"),
            endpoint_url=os.getenv("S3_ENDPOINT"),
        )

        self.svm_model = SVMModel()

        with open("/home/user/magic-pdf.json", "r") as f:
            config = json.load(f)

        self.layout_mode = config["layout-config"]["model"]
        self.formula_enable = config["formula-config"]["enable"]
        self.table_enable = config["table-config"]["enable"]
        self.language = "en"

        endpoint = os.getenv("S3_ENDPOINT", "").rstrip("/")
        bucket = os.getenv("S3_BUCKET_NAME", "")
        self.prefix = f"{endpoint}/{bucket}/document-extracts/"

    def process(self, file_url: str, key: str) -> str:
        logger.info("Processing file: {}", file_url)
        response = requests.get(file_url)
        if response.status_code != 200:
            raise Exception(f"Failed to download PDF: {file_url}")
        pdf_bytes = response.content

        dataset = PymuDocDataset(pdf_bytes)
        inference = doc_analyze(
            dataset,
            ocr=True,
            lang=self.language,
            layout_model=self.layout_mode,
            formula_enable=self.formula_enable,
            table_enable=self.table_enable
        )

        image_writer = ImageWriter(self.s3_writer, self.svm_model)

        pipe_result = inference.pipe_ocr_mode(image_writer, lang=self.language)

        md_content = pipe_result.get_markdown(self.prefix + key + "/")

        # Remove references to images classified as "irrelevant"
        final_markdown = image_writer.remove_redundant_images(md_content)
        return final_markdown

class ImageWriter(DataWriter):
    """
    Receives each extracted image. Classifies it, uploads if relevant, or flags
    it for removal if irrelevant.
    """
    def __init__(self, s3_writer: S3Writer, svm_model: SVMModel):
        self.s3_writer = s3_writer
        self.svm_model = svm_model
        self._redundant_images_paths = []

    def write(self, path: str, data: bytes) -> None:
        label_str = self.svm_model.classify_image(data)

        if label_str == 1:
            # Upload to S3
            self.s3_writer.write(path, data)
        else:
            self._redundant_images_paths.append(path)

    def remove_redundant_images(self, md_content: str) -> str:
        for path in self._redundant_images_paths:
            md_content = md_content.replace(f"![]({path})", "")
        return md_content

if __name__ == "__main__":
    processor = Processor()

    single_url = "https://example.com/somefile.pdf"
    markdown_result = processor.process(single_url)
    print("Single file Markdown:\n", markdown_result)

    multiple_urls = ["https://example.com/file1.pdf", "https://example.com/file2.pdf"]
    batch_results = processor.process_batch(multiple_urls)
    print("Batch results:", batch_results)