import spaces
import json
import math
import os
import traceback
from io import BytesIO
from typing import Any, Dict, List, Optional, Tuple
import re
import warnings

import fitz  # PyMuPDF
import gradio as gr
import requests
import torch
from PIL import Image, ImageDraw, ImageFont
from transformers import AutoModelForCausalLM, AutoProcessor, VisionEncoderDecoderModel
from huggingface_hub import snapshot_download
from qwen_vl_utils import process_vision_info

# Suppress the FutureWarning for cleaner output (optional)
warnings.filterwarnings(
    "ignore",
    category=FutureWarning,
    message="Both `num_logits_to_keep` and `logits_to_keep` are set"
)

# JavaScript for theme refresh
js_func = """
function refresh() {
    const url = new URL(window.location);
    if (url.searchParams.get('__theme') !== 'dark') {
        url.searchParams.set('__theme', 'dark');
        window.location.href = url.href;
    }
}
"""

# Constants
MIN_PIXELS = 3136
MAX_PIXELS = 11289600
IMAGE_FACTOR = 28

# Prompt for dots.ocr
prompt = """Please output the layout information from the PDF image, including each layout element's bbox, its category, and the corresponding text content within the bbox.

1. Bbox format: [x1, y1, x2, y2]
2. Layout Categories: ['Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title']
3. Text Extraction & Formatting Rules:
    - Picture: omit the text field
    - Formula: format as LaTeX
    - Table: format as HTML
    - Others: format as Markdown
4. Constraints:
    - Use original text, no translation
    - Sort elements by human reading order
5. Final Output: Single JSON object
"""

# Model loading functions
def load_model(model_name):
    if model_name == "dots.ocr":
        model_id = "rednote-hilab/dots.ocr"
        model_path = "./models/dots-ocr-local"
        snapshot_download(
            repo_id=model_id,
            local_dir=model_path,
            local_dir_use_symlinks=False,
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            attn_implementation="flash_attention_2",
            torch_dtype=torch.bfloat16,
            device_map="auto",
            trust_remote_code=True
        )
        processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
    elif model_name == "Dolphin":
        model_id = "ByteDance/Dolphin"
        processor = AutoProcessor.from_pretrained(model_id)
        model = VisionEncoderDecoderModel.from_pretrained(model_id)
        model.eval()
        device = "cuda" if torch.cuda.is_available() else "cpu"
        model.to(device)
        model = model.half()  # Use half precision
    else:
        raise ValueError(f"Unknown model: {model_name}")
    return model, processor

# Inference functions
def inference_dots_ocr(model, processor, image, prompt, max_new_tokens):
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "text", "text": prompt}
            ]
        }
    ]
    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    image_inputs, video_inputs = process_vision_info(messages)
    inputs = processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to(model.device)
    with torch.no_grad():
        generated_ids = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            do_sample=False  # Temperature removed previously to fix another warning
        )
    generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
    output_text = processor.batch_decode(
        generated_ids_trimmed,
        skip_special_tokens=True,
        clean_up_tokenization_spaces=False
    )
    return output_text[0] if output_text else ""

def inference_dolphin(model, processor, image):
    pixel_values = processor(image, return_tensors="pt").pixel_values.to(model.device).half()
    generated_ids = model.generate(pixel_values)
    generated_text = processor.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return generated_text

# Load models at startup
models = {
    "dots.ocr": load_model("dots.ocr"),
    "Dolphin": load_model("Dolphin")
}

# Global state for PDF handling
pdf_cache = {
    "images": [],
    "current_page": 0,
    "total_pages": 0,
    "file_type": None,
    "is_parsed": False,
    "results": []
}

# Utility functions
def round_by_factor(number: int, factor: int) -> int:
    return round(number / factor) * factor

def smart_resize(height: int, width: int, factor: int = 28, min_pixels: int = 3136, max_pixels: int = 11289600):
    if max(height, width) / min(height, width) > 200:
        raise ValueError(f"Aspect ratio must be < 200, got {max(height, width) / min(height, width)}")
    h_bar = max(factor, round_by_factor(height, factor))
    w_bar = max(factor, round_by_factor(width, factor))
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = round_by_factor(height / beta, factor)
        w_bar = round_by_factor(width / beta, factor)
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = round_by_factor(height * beta, factor)
        w_bar = round_by_factor(width * beta, factor)
    return h_bar, w_bar

def fetch_image(image_input, min_pixels: int = None, max_pixels: int = None):
    if isinstance(image_input, str):
        if image_input.startswith(("http://", "https://")):
            response = requests.get(image_input)
            image = Image.open(BytesIO(response.content)).convert('RGB')
        else:
            image = Image.open(image_input).convert('RGB')
    elif isinstance(image_input, Image.Image):
        image = image_input.convert('RGB')
    else:
        raise ValueError(f"Invalid image input type: {type(image_input)}")
    if min_pixels or max_pixels:
        min_pixels = min_pixels or MIN_PIXELS
        max_pixels = max_pixels or MAX_PIXELS
        height, width = smart_resize(image.height, image.width, factor=IMAGE_FACTOR, min_pixels=min_pixels, max_pixels=max_pixels)
        image = image.resize((width, height), Image.LANCZOS)
    return image

def load_images_from_pdf(pdf_path: str) -> List[Image.Image]:
    images = []
    try:
        pdf_document = fitz.open(pdf_path)
        for page_num in range(len(pdf_document)):
            page = pdf_document.load_page(page_num)
            mat = fitz.Matrix(2.0, 2.0)
            pix = page.get_pixmap(matrix=mat)
            img_data = pix.tobytes("ppm")
            image = Image.open(BytesIO(img_data)).convert('RGB')
            images.append(image)
        pdf_document.close()
    except Exception as e:
        print(f"Error loading PDF: {e}")
        return []
    return images

def draw_layout_on_image(image: Image.Image, layout_data: List[Dict]) -> Image.Image:
    img_copy = image.copy()
    draw = ImageDraw.Draw(img_copy)
    colors = {
        'Caption': '#FF6B6B', 'Footnote': '#4ECDC4', 'Formula': '#45B7D1', 'List-item': '#96CEB4',
        'Page-footer': '#FFEAA7', 'Page-header': '#DDA0DD', 'Picture': '#FFD93D', 'Section-header': '#6C5CE7',
        'Table': '#FD79A8', 'Text': '#74B9FF', 'Title': '#E17055'
    }
    try:
        font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 12)
    except Exception:
        font = ImageFont.load_default()
    try:
        for item in layout_data:
            if 'bbox' in item and 'category' in item:
                bbox = item['bbox']
                category = item['category']
                color = colors.get(category, '#000000')
                draw.rectangle(bbox, outline=color, width=2)
                label = category
                label_bbox = draw.textbbox((0, 0), label, font=font)
                label_width = label_bbox[2] - label_bbox[0]
                label_height = label_bbox[3] - label_bbox[1]
                label_x = bbox[0]
                label_y = max(0, bbox[1] - label_height - 2)
                draw.rectangle([label_x, label_y, label_x + label_width + 4, label_y + label_height + 2], fill=color)
                draw.text((label_x + 2, label_y + 1), label, fill='white', font=font)
    except Exception as e:
        print(f"Error drawing layout: {e}")
    return img_copy

def is_arabic_text(text: str) -> bool:
    if not text:
        return False
    header_pattern = r'^#{1,6}\s+(.+)$'
    paragraph_pattern = r'^(?!#{1,6}\s|!\[|```|\||\s*[-*+]\s|\s*\d+\.\s)(.+)$'
    content_text = []
    for line in text.split('\n'):
        line = line.strip()
        if not line:
            continue
        header_match = re.match(header_pattern, line, re.MULTILINE)
        if header_match:
            content_text.append(header_match.group(1))
            continue
        if re.match(paragraph_pattern, line, re.MULTILINE):
            content_text.append(line)
    if not content_text:
        return False
    combined_text = ' '.join(content_text)
    arabic_chars = 0
    total_chars = 0
    for char in combined_text:
        if char.isalpha():
            total_chars += 1
            if ('\u0600' <= char <= '\u06FF') or ('\u0750' <= char <= '\u077F') or ('\u08A0' <= char <= '\u08FF'):
                arabic_chars += 1
    return total_chars > 0 and (arabic_chars / total_chars) > 0.5

def layoutjson2md(image: Image.Image, layout_data: List[Dict], text_key: str = 'text') -> str:
    import base64
    markdown_lines = []
    try:
        sorted_items = sorted(layout_data, key=lambda x: (x.get('bbox', [0, 0, 0, 0])[1], x.get('bbox', [0, 0, 0, 0])[0]))
        for item in sorted_items:
            category = item.get('category', '')
            text = item.get(text_key, '')
            bbox = item.get('bbox', [])
            if category == 'Picture':
                if bbox and len(bbox) == 4:
                    try:
                        x1, y1, x2, y2 = [max(0, int(x)) if i < 2 else min(image.width if i % 2 == 0 else image.height, int(x)) for i, x in enumerate(bbox)]
                        if x2 > x1 and y2 > y1:
                            cropped_img = image.crop((x1, y1, x2, y2))
                            buffer = BytesIO()
                            cropped_img.save(buffer, format='PNG')
                            img_data = base64.b64encode(buffer.getvalue()).decode()
                            markdown_lines.append(f'<image-card alt="Image" src="data:image/png;base64,{img_data}" ></image-card>\n')
                        else:
                            markdown_lines.append('<image-card alt="Image" src="Image region detected" ></image-card>\n')
                    except Exception as e:
                        print(f"Error processing image region: {e}")
                        markdown_lines.append('<image-card alt="Image" src="Image detected" ></image-card>\n')
                else:
                    markdown_lines.append('<image-card alt="Image" src="Image detected" ></image-card>\n')
            elif not text:
                continue
            elif category == 'Title':
                markdown_lines.append(f"# {text}\n")
            elif category == 'Section-header':
                markdown_lines.append(f"## {text}\n")
            elif category == 'Text':
                markdown_lines.append(f"{text}\n")
            elif category == 'List-item':
                markdown_lines.append(f"- {text}\n")
            elif category == 'Table':
                if text.strip().startswith('<'):
                    markdown_lines.append(f"{text}\n")
                else:
                    markdown_lines.append(f"**Table:** {text}\n")
            elif category == 'Formula':
                if text.strip().startswith('$') or '\\' in text:
                    markdown_lines.append(f"$$  \n{text}\n  $$\n")  # Fixed formatting, removed extra spaces
                else:
                    markdown_lines.append(f"**Formula:** {text}\n")
            elif category == 'Caption':
                markdown_lines.append(f"*{text}*\n")
            elif category == 'Footnote':
                markdown_lines.append(f"^{text}^\n")
            elif category in ['Page-header', 'Page-footer']:
                continue
            else:
                markdown_lines.append(f"{text}\n")
            markdown_lines.append("")
    except Exception as e:
        print(f"Error converting to markdown: {e}")
        return str(layout_data)
    return "\n".join(markdown_lines)

def load_file_for_preview(file_path: str) -> Tuple[Optional[Image.Image], str]:
    global pdf_cache
    if not file_path or not os.path.exists(file_path):
        return None, "No file selected"
    file_ext = os.path.splitext(file_path)[1].lower()
    try:
        if file_ext == '.pdf':
            images = load_images_from_pdf(file_path)
            if not images:
                return None, "Failed to load PDF"
            pdf_cache.update({
                "images": images,
                "current_page": 0,
                "total_pages": len(images),
                "file_type": "pdf",
                "is_parsed": False,
                "results": []
            })
            return images[0], f"Page 1 / {len(images)}"
        elif file_ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']:
            image = Image.open(file_path).convert('RGB')
            pdf_cache.update({
                "images": [image],
                "current_page": 0,
                "total_pages": 1,
                "file_type": "image",
                "is_parsed": False,
                "results": []
            })
            return image, "Page 1 / 1"
        else:
            return None, f"Unsupported file format: {file_ext}"
    except Exception as e:
        print(f"Error loading file: {e}")
        return None, f"Error loading file: {str(e)}"

@spaces.GPU()
def process_document(file_path, model_choice, max_tokens, min_pix, max_pix):
    global pdf_cache
    if not file_path:
        return None, "Please upload a file first.", None
    model, processor = models[model_choice]
    image, page_info = load_file_for_preview(file_path)
    if image is None:
        return None, page_info, None
    if pdf_cache["file_type"] == "pdf":
        all_results = []
        for i, img in enumerate(pdf_cache["images"]):
            if model_choice == "dots.ocr":
                raw_output = inference_dots_ocr(model, processor, img, prompt, max_tokens)
                try:
                    layout_data = json.loads(raw_output)
                    processed_image = draw_layout_on_image(img, layout_data)
                    markdown_content = layoutjson2md(img, layout_data)
                    result = {
                        'processed_image': processed_image,
                        'markdown_content': markdown_content,
                        'layout_result': layout_data
                    }
                except Exception:
                    result = {
                        'processed_image': img,
                        'markdown_content': raw_output,
                        'layout_result': None
                    }
            else:  # Dolphin
                text = inference_dolphin(model, processor, img)
                result = f"## Page {i+1}\n\n{text}" if text else "No text extracted"
            all_results.append(result)
        pdf_cache["results"] = all_results
        pdf_cache["is_parsed"] = True
        first_result = all_results[0]
        if model_choice == "dots.ocr":
            markdown_update = gr.update(value=first_result['markdown_content'], rtl=is_arabic_text(first_result['markdown_content']))
            return first_result['processed_image'], markdown_update, first_result['layout_result']
        else:
            markdown_update = gr.update(value=first_result, rtl=is_arabic_text(first_result))
            return None, markdown_update, None
    else:
        if model_choice == "dots.ocr":
            raw_output = inference_dots_ocr(model, processor, image, prompt, max_tokens)
            try:
                layout_data = json.loads(raw_output)
                processed_image = draw_layout_on_image(image, layout_data)
                markdown_content = layoutjson2md(image, layout_data)
                result = {
                    'processed_image': processed_image,
                    'markdown_content': markdown_content,
                    'layout_result': layout_data
                }
            except Exception:
                result = {
                    'processed_image': image,
                    'markdown_content': raw_output,
                    'layout_result': None
                }
            pdf_cache["results"] = [result]
        else:  # Dolphin
            text = inference_dolphin(model, processor, image)
            result = text if text else "No text extracted"
            pdf_cache["results"] = [result]
        pdf_cache["is_parsed"] = True
        if model_choice == "dots.ocr":
            markdown_update = gr.update(value=result['markdown_content'], rtl=is_arabic_text(result['markdown_content']))
            return result['processed_image'], markdown_update, result['layout_result']
        else:
            markdown_update = gr.update(value=result, rtl=is_arabic_text(result))
            return None, markdown_update, None

def turn_page(direction: str) -> Tuple[Optional[Image.Image], str, Any, Optional[Image.Image], Optional[Dict]]:
    global pdf_cache
    if not pdf_cache["images"]:
        return None, '<div class="page-info">No file loaded</div>', "No results yet", None, None
    if direction == "prev":
        pdf_cache["current_page"] = max(0, pdf_cache["current_page"] - 1)
    elif direction == "next":
        pdf_cache["current_page"] = min(pdf_cache["total_pages"] - 1, pdf_cache["current_page"] + 1)
    index = pdf_cache["current_page"]
    current_image_preview = pdf_cache["images"][index]
    page_info_html = f'<div class="page-info">Page {index + 1} / {pdf_cache["total_pages"]}</div>'
    if pdf_cache["is_parsed"] and index < len(pdf_cache["results"]):
        result = pdf_cache["results"][index]
        if isinstance(result, dict):  # dots.ocr
            markdown_content = result.get('markdown_content', 'No content available')
            processed_img = result.get('processed_image', None)
            layout_json = result.get('layout_result', None)
        else:  # Dolphin
            markdown_content = result
            processed_img = None
            layout_json = None
    else:
        markdown_content = "Page not processed yet"
        processed_img = None
        layout_json = None
    markdown_update = gr.update(value=markdown_content, rtl=is_arabic_text(markdown_content))
    return current_image_preview, page_info_html, markdown_update, processed_img, layout_json

def create_gradio_interface():
    css = """
    .main-container { max-width: 1400px; margin: 0 auto; }
    .header-text { text-align: center; color: #2c3e50; margin-bottom: 20px; }
    .process-button { 
        border: none !important; 
        color: white !important; 
        font-weight: bold !important; 
        background-color: blue !important;} 
    .process-button:hover { 
        background-color: darkblue !important;
        transform: translateY(-2px) !important; 
        box-shadow: 0 4px 8px rgba(0,0,0,0.2) !important; }
    .info-box { border: 1px solid #dee2e6; border-radius: 8px; padding: 15px; margin: 10px 0; }
    .page-info { text-align: center; padding: 8px 16px; border-radius: 20px; font-weight: bold; margin: 10px 0; }
    .model-status { padding: 10px; border-radius: 8px; margin: 10px 0; text-align: center; font-weight: bold; }
    .status-ready { background: #d1edff; color: #0c5460; border: 1px solid #b8daff; }
    """
    with gr.Blocks(theme="bethecloud/storj_theme", css=css, js=js_func) as demo:
        gr.HTML("""
        <div class="title" style="text-align: center">
            <h1>Dot<span style="color: red;">●</span><strong></strong>OCR vs Dolphin🐬</h1>
            <p style="font-size: 1.1em; color: #6b7280; margin-bottom: 0.6em;">
                Advanced vision-language model for image/PDF to markdown document processing
            </p>
        </div>
        """)
        with gr.Row():
            with gr.Column(scale=1):
                file_input = gr.File(
                    label="Upload Image or PDF",
                    file_types=[".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".pdf"],
                    type="filepath"
                )
                image_preview = gr.Image(label="Preview", type="pil", interactive=False, height=300)
                with gr.Row():
                    prev_page_btn = gr.Button("⬅ Previous", size="md")
                    page_info = gr.HTML('<div class="page-info">No file loaded</div>')
                    next_page_btn = gr.Button("Next ➡", size="md")
                model_choice = gr.Radio(
                    choices=["dots.ocr", "Dolphin"],
                    label="Select Model",
                    value="dots.ocr"
                )
                with gr.Accordion("Advanced Settings", open=False):
                    max_new_tokens = gr.Slider(minimum=1000, maximum=32000, value=24000, step=1000, label="Max New Tokens")
                    min_pixels = gr.Number(value=MIN_PIXELS, label="Min Pixels")
                    max_pixels = gr.Number(value=MAX_PIXELS, label="Max Pixels")
                process_btn = gr.Button("🔥 Process Document", variant="primary", elem_classes=["process-button"], size="lg")
                clear_btn = gr.Button("Clear Document", variant="secondary")
            with gr.Column(scale=2):
                with gr.Tabs():
                    with gr.Tab("✦︎ Processed Image"):
                        processed_image = gr.Image(label="Image with Layout Detection", type="pil", interactive=False, height=500)
                    with gr.Tab("🀥 Extracted Content"):
                        markdown_output = gr.Markdown(value="Click 'Process Document' to see extracted content...", height=500)
                    with gr.Tab("⏲ Layout JSON"):
                        json_output = gr.JSON(label="Layout Analysis Results", value=None)
                with gr.Row():
                    examples = gr.Examples(
                        examples=["examples/sample_image1.png", "examples/sample_image2.png", "examples/sample_pdf.pdf"],
                        inputs=file_input,
                        label="Example Documents"
                    )
        def handle_file_upload(file_path):
            image, page_info = load_file_for_preview(file_path)
            return image, page_info
        
        def clear_all():
            global pdf_cache
            pdf_cache = {"images": [], "current_page": 0, "total_pages": 0, "file_type": None, "is_parsed": False, "results": []}
            return None, None, '<div class="page-info">No file loaded</div>', None, "Click 'Process Document' to see extracted content...", None
        
        file_input.change(handle_file_upload, inputs=[file_input], outputs=[image_preview, page_info])
        prev_page_btn.click(lambda: turn_page("prev"), outputs=[image_preview, page_info, markdown_output, processed_image, json_output])
        next_page_btn.click(lambda: turn_page("next"), outputs=[image_preview, page_info, markdown_output, processed_image, json_output])
        process_btn.click(
            process_document,
            inputs=[file_input, model_choice, max_new_tokens, min_pixels, max_pixels],
            outputs=[processed_image, markdown_output, json_output]
        )
        clear_btn.click(
            clear_all,
            outputs=[file_input, image_preview, page_info, processed_image, markdown_output, json_output]
        )
    return demo

if __name__ == "__main__":
    demo = create_gradio_interface()
    demo.queue(max_size=30).launch(share=True, debug=True, show_error=True)