import os
import sys
import random
import uuid
import json
import time
from threading import Thread
from typing import Iterable
from huggingface_hub import snapshot_download

import gradio as gr
import spaces
import torch
import numpy as np
from PIL import Image
import cv2

from transformers import (
    Qwen2_5_VLForConditionalGeneration,
    Qwen3VLForConditionalGeneration,
    AutoModelForImageTextToText,
    AutoModelForCausalLM,
    AutoProcessor,
    TextIteratorStreamer,
)

from transformers.image_utils import load_image
from gradio.themes import Soft
from gradio.themes.utils import colors, fonts, sizes

colors.steel_blue = colors.Color(
    name="steel_blue",
    c50="#EBF3F8",
    c100="#D3E5F0",
    c200="#A8CCE1",
    c300="#7DB3D2",
    c400="#529AC3",
    c500="#4682B4",
    c600="#3E72A0",
    c700="#36638C",
    c800="#2E5378",
    c900="#264364",
    c950="#1E3450",
)

class SteelBlueTheme(Soft):
    def __init__(
        self,
        *,
        primary_hue: colors.Color | str = colors.gray,
        secondary_hue: colors.Color | str = colors.steel_blue,
        neutral_hue: colors.Color | str = colors.slate,
        text_size: sizes.Size | str = sizes.text_lg,
        font: fonts.Font | str | Iterable[fonts.Font | str] = (
            fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
        ),
        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
            fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
        ),
    ):
        super().__init__(
            primary_hue=primary_hue,
            secondary_hue=secondary_hue,
            neutral_hue=neutral_hue,
            text_size=text_size,
            font=font,
            font_mono=font_mono,
        )
        super().set(
            background_fill_primary="*primary_50",
            background_fill_primary_dark="*primary_900",
            body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
            body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
            button_primary_text_color="white",
            button_primary_text_color_hover="white",
            button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
            button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
            button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_800)",
            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_500)",
            button_secondary_text_color="black",
            button_secondary_text_color_hover="white",
            button_secondary_background_fill="linear-gradient(90deg, *primary_300, *primary_300)",
            button_secondary_background_fill_hover="linear-gradient(90deg, *primary_400, *primary_400)",
            button_secondary_background_fill_dark="linear-gradient(90deg, *primary_500, *primary_600)",
            button_secondary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)",
            slider_color="*secondary_500",
            slider_color_dark="*secondary_600",
            block_title_text_weight="600",
            block_border_width="3px",
            block_shadow="*shadow_drop_lg",
            button_primary_shadow="*shadow_drop_lg",
            button_large_padding="11px",
            color_accent_soft="*primary_100",
            block_label_background_fill="*primary_200",
        )

steel_blue_theme = SteelBlueTheme()

css = """
#main-title h1 {
    font-size: 2.3em !important;
}
#output-title h2 {
    font-size: 2.2em !important;
}

/* RadioAnimated Styles */
.ra-wrap{ width: fit-content; }
.ra-inner{
  position: relative; display: inline-flex; align-items: center; gap: 0; padding: 6px;
  background: var(--neutral-200); border-radius: 9999px; overflow: hidden;
}
.ra-input{ display: none; }
.ra-label{
  position: relative; z-index: 2; padding: 8px 16px;
  font-family: inherit; font-size: 14px; font-weight: 600;
  color: var(--neutral-500); cursor: pointer; transition: color 0.2s; white-space: nowrap;
}
.ra-highlight{
  position: absolute; z-index: 1; top: 6px; left: 6px;
  height: calc(100% - 12px); border-radius: 9999px;
  background: white; box-shadow: 0 2px 4px rgba(0,0,0,0.1);
  transition: transform 0.2s, width 0.2s;
}
.ra-input:checked + .ra-label{ color: black; }

/* Dark mode adjustments for Radio */
.dark .ra-inner { background: var(--neutral-800); }
.dark .ra-label { color: var(--neutral-400); }
.dark .ra-highlight { background: var(--neutral-600); }
.dark .ra-input:checked + .ra-label { color: white; }

#gpu-duration-container {
    padding: 10px;
    border-radius: 8px;
    background: var(--background-fill-secondary);
    border: 1px solid var(--border-color-primary);
    margin-top: 10px;
}
"""

MAX_MAX_NEW_TOKENS = 4096
DEFAULT_MAX_NEW_TOKENS = 2048
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES"))
print("torch.__version__ =", torch.__version__)
print("torch.version.cuda =", torch.version.cuda)
print("cuda available:", torch.cuda.is_available())
print("cuda device count:", torch.cuda.device_count())
if torch.cuda.is_available():
    print("current device:", torch.cuda.current_device())
    print("device name:", torch.cuda.get_device_name(torch.cuda.current_device()))

print("Using device:", device)

class RadioAnimated(gr.HTML):
    def __init__(self, choices, value=None, **kwargs):
        if not choices or len(choices) < 2:
            raise ValueError("RadioAnimated requires at least 2 choices.")
        if value is None:
            value = choices[0]

        uid = uuid.uuid4().hex[:8]
        group_name = f"ra-{uid}"

        inputs_html = "\n".join(
            f"""
            <input class="ra-input" type="radio" name="{group_name}" id="{group_name}-{i}" value="{c}">
            <label class="ra-label" for="{group_name}-{i}">{c}</label>
            """
            for i, c in enumerate(choices)
        )

        html_template = f"""
        <div class="ra-wrap" data-ra="{uid}">
          <div class="ra-inner">
            <div class="ra-highlight"></div>
            {inputs_html}
          </div>
        </div>
        """

        js_on_load = r"""
        (() => {
          const wrap = element.querySelector('.ra-wrap');
          const inner = element.querySelector('.ra-inner');
          const highlight = element.querySelector('.ra-highlight');
          const inputs = Array.from(element.querySelectorAll('.ra-input'));

          if (!inputs.length) return;

          const choices = inputs.map(i => i.value);

          function setHighlightByIndex(idx) {
            const n = choices.length;
            const pct = 100 / n;
            highlight.style.width = `calc(${pct}% - 6px)`;
            highlight.style.transform = `translateX(${idx * 100}%)`;
          }

          function setCheckedByValue(val, shouldTrigger=false) {
            const idx = Math.max(0, choices.indexOf(val));
            inputs.forEach((inp, i) => { inp.checked = (i === idx); });
            setHighlightByIndex(idx);

            props.value = choices[idx];
            if (shouldTrigger) trigger('change', props.value);
          }

          setCheckedByValue(props.value ?? choices[0], false);

          inputs.forEach((inp) => {
            inp.addEventListener('change', () => {
              setCheckedByValue(inp.value, true);
            });
          });
        })();
        """

        super().__init__(
            value=value,
            html_template=html_template,
            js_on_load=js_on_load,
            **kwargs
        )

def apply_gpu_duration(val: str):
    return int(val)

MODEL_ID_V = "datalab-to/chandra"
processor_v = AutoProcessor.from_pretrained(MODEL_ID_V, trust_remote_code=True)
model_v = Qwen3VLForConditionalGeneration.from_pretrained(
    MODEL_ID_V,
    attn_implementation="kernels-community/flash-attn2",
    trust_remote_code=True,
    torch_dtype=torch.float16
).to(device).eval()

MODEL_ID_X = "nanonets/Nanonets-OCR2-3B"
processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True)
model_x = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_X,
    attn_implementation="kernels-community/flash-attn2",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
).to(device).eval()

MODEL_PATH_D = "prithivMLmods/Dots.OCR-Latest-BF16" # -> alt of [rednote-hilab/dots.ocr]
processor_d = AutoProcessor.from_pretrained(MODEL_PATH_D, trust_remote_code=True)
model_d = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH_D,
    attn_implementation="kernels-community/flash-attn2",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
).eval()

MODEL_ID_M = "allenai/olmOCR-2-7B-1025"
processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_M,
    attn_implementation="kernels-community/flash-attn2",
    trust_remote_code=True,
    torch_dtype=torch.float16
).to(device).eval()

def calc_timeout_image(model_name: str, text: str, image: Image.Image,
                       max_new_tokens: int, temperature: float, top_p: float,
                       top_k: int, repetition_penalty: float, gpu_timeout: int):
    """Calculate GPU timeout duration for image inference."""
    try:
        return int(gpu_timeout)
    except:
        return 60

@spaces.GPU(duration=calc_timeout_image)
def generate_image(model_name: str, text: str, image: Image.Image,
                   max_new_tokens: int, temperature: float, top_p: float,
                   top_k: int, repetition_penalty: float, gpu_timeout: int = 60):
    """
    Generates responses using the selected model for image input.
    Yields raw text and Markdown-formatted text.
    """
    if model_name == "olmOCR-2-7B-1025":
        processor = processor_m
        model = model_m
    elif model_name == "Nanonets-OCR2-3B":
        processor = processor_x
        model = model_x
    elif model_name == "Chandra-OCR":
        processor = processor_v
        model = model_v
    elif model_name == "Dots.OCR":
        processor = processor_d
        model = model_d
    else:
        yield "Invalid model selected.", "Invalid model selected."
        return

    if image is None:
        yield "Please upload an image.", "Please upload an image."
        return

    messages = [{
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": text},
        ]
    }]
    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

    inputs = processor(
        text=[prompt_full],
        images=[image],
        return_tensors="pt",
        padding=True).to(device)

    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
    generation_kwargs = {
        **inputs,
        "streamer": streamer,
        "max_new_tokens": max_new_tokens,
        "do_sample": True,
        "temperature": temperature,
        "top_p": top_p,
        "top_k": top_k,
        "repetition_penalty": repetition_penalty,
    }
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    buffer = ""
    for new_text in streamer:
        buffer += new_text
        buffer = buffer.replace("<|im_end|>", "")
        time.sleep(0.01)
        yield buffer, buffer

image_examples = [
    ["Convert to Markdown.", "examples/3.jpg"],
    ["Perform OCR on the image. [Markdown]", "examples/1.jpg"],
    ["Extract the contents. [Markdown].", "examples/2.jpg"],
]

with gr.Blocks() as demo:
    gr.Markdown("# **Multimodal OCR3**", elem_id="main-title")
    with gr.Row():
        with gr.Column(scale=2):
            image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
            image_upload = gr.Image(type="pil", label="Upload Image", height=290)

            image_submit = gr.Button("Submit", variant="primary")
            gr.Examples(
                examples=image_examples,
                inputs=[image_query, image_upload]
            )
            
            with gr.Accordion("Advanced options", open=False):
                max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
                temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.7)
                top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9)
                top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50)
                repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.1)
                
        with gr.Column(scale=3):
            gr.Markdown("## Output", elem_id="output-title")
            output = gr.Textbox(label="Raw Output Stream", interactive=True, lines=15)
            with gr.Accordion("(Result.md)", open=False):
                markdown_output = gr.Markdown(label="(Result.Md)")

            model_choice = gr.Radio(
                choices=["Nanonets-OCR2-3B", "Chandra-OCR", "Dots.OCR", "olmOCR-2-7B-1025"],
                label="Select Model",
                value="Nanonets-OCR2-3B"
            )
            
            with gr.Row(elem_id="gpu-duration-container"):
                with gr.Column():
                    gr.Markdown("**GPU Duration (seconds)**")
                    radioanimated_gpu_duration = RadioAnimated(
                        choices=["60", "90", "120", "180", "240", "300"],
                        value="60",
                        elem_id="radioanimated_gpu_duration"
                    )
                    gpu_duration_state = gr.Number(value=60, visible=False)
            
            gr.Markdown("*Note: Higher GPU duration allows for longer processing but consumes more GPU quota.*")
            
    radioanimated_gpu_duration.change(
        fn=apply_gpu_duration,
        inputs=radioanimated_gpu_duration,
        outputs=[gpu_duration_state],
        api_visibility="private"
    )

    image_submit.click(
        fn=generate_image,
        inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty, gpu_duration_state],
        outputs=[output, markdown_output]
    )

if __name__ == "__main__":
    demo.queue(max_size=50).launch(css=css, theme=steel_blue_theme, mcp_server=True, ssr_mode=False, show_error=True)