import base64
import os
import shutil
import uuid

import fitz  # PyMuPDF
import gradio as gr
import torch
from PIL import Image, ImageEnhance
from transformers import AutoConfig, AutoModel, AutoTokenizer

from got_ocr import got_ocr

# 初始化模型和分词器
# model_name = "stepfun-ai/GOT-OCR2_0"
model_name = "ucaslcl/GOT-OCR2_0"
device = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True, low_cpu_mem_usage=True, device_map="cuda", use_safetensors=True)
model = model.eval().to(device)
model.config.pad_token_id = tokenizer.eos_token_id

UPLOAD_FOLDER = "./uploads"
RESULTS_FOLDER = "./results"

# 确保必要的文件夹存在
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
os.makedirs(RESULTS_FOLDER, exist_ok=True)


def pdf_to_images(pdf_path):
    images = []
    pdf_document = fitz.open(pdf_path)
    for page_num in range(len(pdf_document)):
        page = pdf_document.load_page(page_num)
        # 进一步增加分辨率和缩放比例
        zoom = 10  # 增加缩放比例到4
        mat = fitz.Matrix(zoom, zoom)
        pix = page.get_pixmap(matrix=mat, alpha=False)
        img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)

        # 增对比度
        enhancer = ImageEnhance.Contrast(img)
        img = enhancer.enhance(1.5)  # 增加50%的对比度

        images.append(img)
    pdf_document.close()
    return images


def process_pdf(pdf_file):
    if pdf_file is None:
        return None

    temp_pdf_path = os.path.join(UPLOAD_FOLDER, f"{uuid.uuid4()}.pdf")

    # 使用 shutil 复制上传的件到临时位置
    shutil.copy(pdf_file.name, temp_pdf_path)

    images = pdf_to_images(temp_pdf_path)
    os.remove(temp_pdf_path)

    # 将图像保存为临时文件并返回文件路径列表
    image_paths = []
    for i, img in enumerate(images):
        img_path = os.path.join(RESULTS_FOLDER, f"page_{i+1}.png")
        img.save(img_path, "PNG")
        image_paths.append(img_path)

    return image_paths


def on_image_select(evt: gr.SelectData):
    if evt.index is not None:
        return evt.index
    return None


# 更新perform_ocr函数的输入参数
def perform_ocr(image_gallery, got_mode, fine_grained_type, color, box):
    if len(image_gallery) == 0:
        return "请先上传PDF文件", None

    results = []
    progress = gr.Progress()

    for i, image_info in enumerate(progress.tqdm(image_gallery)):
        selected_image = image_info[0]

        ocr_color = color if fine_grained_type == "color" else ""
        ocr_box = box if fine_grained_type == "box" else ""

        result, _ = got_ocr(
            model,
            tokenizer,
            selected_image,
            got_mode=got_mode,
            fine_grained_mode=fine_grained_type,
            ocr_color=ocr_color,
            ocr_box=ocr_box,
        )
        results.append(f"第 {i+1} 页结果:\n{result}\n\n")

    combined_result = "".join(results)

    # 生成下载链接
    encoded_result = base64.b64encode(combined_result.encode("utf-8")).decode("utf-8")
    download_link = f'<a href="data:text/plain;base64,{encoded_result}" download="ocr_result.txt">下载完整OCR结果</a>'

    return gr.Markdown(f"{download_link}\n\n{combined_result[:1000]}..."), combined_result


def task_update(task):
    if "fine-grained" in task:
        return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]
    else:
        return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)]


def fine_grained_update(fine_grained_type):
    if fine_grained_type == "color":
        return [gr.update(visible=True), gr.update(visible=False)]
    elif fine_grained_type == "box":
        return [gr.update(visible=False), gr.update(visible=True)]
    else:
        return [gr.update(visible=False), gr.update(visible=False)]


with gr.Blocks() as demo:
    pdf_input = gr.File(label="上传PDF文件")
    image_gallery = gr.Gallery(
        label="PDF页面预览",
        columns=3,
        height=600,
        object_fit="contain",
        preview=True,
    )
    pdf_input.upload(fn=process_pdf, inputs=pdf_input, outputs=image_gallery)

    task_dropdown = gr.Dropdown(
        choices=["plain texts OCR", "format texts OCR", "plain multi-crop OCR", "format multi-crop OCR", "plain fine-grained OCR", "format fine-grained OCR"],
        label="选择GOT模式",
        value="format texts OCR",
    )
    fine_grained_dropdown = gr.Dropdown(choices=["box", "color"], label="fine-grained类型", visible=False)
    color_dropdown = gr.Dropdown(choices=["red", "green", "blue"], label="颜色列表", visible=False)
    box_input = gr.Textbox(label="输入框: [x1,y1,x2,y2]", placeholder="例如: [0,0,100,100]", visible=False)

    ocr_button = gr.Button("开始OCR识别")
    ocr_result = gr.Markdown(label="OCR结果预览")
    full_result = gr.State()

    task_dropdown.change(task_update, inputs=[task_dropdown], outputs=[fine_grained_dropdown, color_dropdown, box_input])
    fine_grained_dropdown.change(fine_grained_update, inputs=[fine_grained_dropdown], outputs=[color_dropdown, box_input])

    ocr_button.click(
        fn=perform_ocr,
        inputs=[image_gallery, task_dropdown, fine_grained_dropdown, color_dropdown, box_input],
        outputs=[ocr_result, full_result],
    )

if __name__ == "__main__":
    demo.launch()