import os
import time
from threading import Thread
import re
from PIL import Image, ImageDraw

import gradio as gr
import spaces
import torch

from transformers import (
    Qwen2_5_VLForConditionalGeneration,
    AutoProcessor,
    TextIteratorStreamer,
)

# Constants for text generation
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

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

# Load Lumian2-VLR-7B-Thinking
MODEL_ID_Y = "prithivMLmods/Lumian2-VLR-7B-Thinking"
processor = AutoProcessor.from_pretrained(MODEL_ID_Y, trust_remote_code=True)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_Y,
    trust_remote_code=True,
    torch_dtype=torch.float16
).to(device).eval()

def parse_model_output(text: str):
    """
    Parses the model output to extract the answer and bounding box coordinates.
    """
    # Extract coordinates from the <think> block
    think_match = re.search(r"<think>(.*?)</think>", text, re.DOTALL)
    coordinates = []
    if think_match:
        think_content = think_match.group(1)
        # Find all occurrences of (x, y) coordinates
        coords_raw = re.findall(r'\((\d+),\s*(\d+)\)', think_content)
        coordinates = [(int(x), int(y)) for x, y in coords_raw]

    # Extract the answer from the <answer> block
    answer_match = re.search(r"<answer>(.*?)</answer>", text, re.DOTALL)
    answer = answer_match.group(1).strip() if answer_match else text

    return answer, coordinates

def draw_bounding_boxes(image: Image.Image, coordinates: list, box_size: int = 60, use_dotted_style: bool = False):
    """
    Draws square bounding boxes on the image at the given coordinates.
    """
    if not coordinates:
        return image

    img_with_boxes = image.copy()
    draw = ImageDraw.Draw(img_with_boxes, "RGBA")
    
    half_box = box_size // 2

    for (x, y) in coordinates:
        # Define the bounding box corners
        x1 = x - half_box
        y1 = y - half_box
        x2 = x + half_box
        y2 = y + half_box
        
        if use_dotted_style:
            # "Dotted like seaborn" - a semi-transparent fill with a solid outline
            fill_color = (0, 100, 255, 60)  # Light blue, semi-transparent
            outline_color = (0, 0, 255) # Solid blue
            draw.rectangle([x1, y1, x2, y2], fill=fill_color, outline=outline_color, width=2)
        else:
            # Default solid box
            outline_color = (255, 0, 0) # Red
            draw.rectangle([x1, y1, x2, y2], outline=outline_color, width=3)
            
    return img_with_boxes

@spaces.GPU
def generate_image(text: str, image: Image.Image,
                   max_new_tokens: int,
                   temperature: float,
                   top_p: float,
                   top_k: int,
                   repetition_penalty: float,
                   draw_boxes: bool,
                   use_dotted_style: bool):
    """
    Generates responses and draws bounding boxes based on model output.
    Yields raw text, markdown-formatted text, and the processed image.
    """
    if image is None:
        yield "Please upload an image.", "Please upload an image.", None
        return

    # Yield the original image immediately for the output display
    yield "", "", image

    messages = [{
        "role": "user",
        "content": [
            {"type": "image", "image": 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,
        truncation=False,
        max_length=MAX_INPUT_TOKEN_LENGTH
    ).to(device)
    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
    generation_kwargs = {
        **inputs,
        "streamer": streamer,
        "max_new_tokens": max_new_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "top_k": top_k,
        "repetition_penalty": repetition_penalty,
        "do_sample": True
    }
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    
    buffer = ""
    for new_text in streamer:
        buffer += new_text
        time.sleep(0.01)
        # During generation, yield text updates but keep the original image
        yield buffer, buffer, image

    # After generation is complete, parse the output and draw boxes
    final_answer, coordinates = parse_model_output(buffer)
    
    output_image = image
    if draw_boxes and coordinates:
        output_image = draw_bounding_boxes(image, coordinates, use_dotted_style=use_dotted_style)
    
    # Yield the final result with the processed image
    yield buffer, final_answer, output_image

# Define examples for image inference
image_examples = [
    ["Explain the content in detail.", "images/D.jpg"],
    ["Explain the content (ocr).", "images/O.jpg"],
    ["What is the core meaning of the poem?", "images/S.jpg"],
    ["Provide a detailed caption for the image.", "images/A.jpg"],
    ["Explain the pie-chart in detail.", "images/2.jpg"],
    ["Jsonify Data.", "images/1.jpg"],
]

css = """
.submit-btn {
    background-color: #2980b9 !important;
    color: white !important;
}
.submit-btn:hover {
    background-color: #3498db !important;
}
.canvas-output {
    border: 2px solid #4682B4;
    border-radius: 10px;
    padding: 20px;
}
"""

# Create the Gradio Interface
with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
    gr.Markdown("# **Lumian2-VLR-7B-Thinking Image Inference**")
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("## Image Inference")
            image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
            image_upload = gr.Image(type="pil", label="Image")
            image_submit = gr.Button("Submit", elem_classes="submit-btn")
            
            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=0.6)
                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=1.2)
                
            gr.Examples(
                examples=image_examples,
                inputs=[image_query, image_upload]
            )

        with gr.Column(scale=2):
            gr.Markdown("## Output")
            with gr.Tabs():
                with gr.TabItem("Image with Bounding Box"):
                    image_output = gr.Image(label="Processed Image")
                with gr.TabItem("Raw Text"):
                    output = gr.Textbox(label="Raw Model Output", interactive=False, lines=10)
                with gr.TabItem("Parsed Answer"):
                    markdown_output = gr.Markdown(label="Parsed Answer")

            gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Qwen2.5-VL/discussions)")

            gr.Markdown(
                """> [Lumian2-VLR-7B-Thinking](https://huggingface.co/prithivMLmods/Lumian2-VLR-7B-Thinking): The Lumian2-VLR-7B-Thinking model is a high-fidelity vision-language reasoning (experimental model) system designed for fine-grained multimodal understanding. Built on Qwen2.5-VL-7B-Instruct, this model enhances image captioning, and document comprehension through explicit grounded reasoning. It produces structured reasoning traces aligned with visual coordinates, enabling explainable multimodal reasoning."""
            )

            with gr.Row():
                draw_boxes_checkbox = gr.Checkbox(label="Draw Bounding Boxes", value=True)
                dotted_style_checkbox = gr.Checkbox(label="Use Dotted Style for Boxes", value=False)


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

if __name__ == "__main__":
    demo.queue(max_size=50).launch(share=True)```