import gradio as gr
import spaces
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer
from qwen_vl_utils import process_vision_info
import torch
from PIL import Image
import os
import uuid
import io
from threading import Thread
from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.platypus import SimpleDocTemplate, Image as RLImage, Paragraph, Spacer
from reportlab.lib.units import inch
from reportlab.pdfbase import pdfmetrics
from reportlab.pdfbase.ttfonts import TTFont
import docx
from docx.enum.text import WD_ALIGN_PARAGRAPH

# Define model options
MODEL_OPTIONS = {
    "Qwen2VL Base": "Qwen/Qwen2-VL-2B-Instruct",
    "Latex OCR": "prithivMLmods/Qwen2-VL-OCR-2B-Instruct",
    "Math Prase": "prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct",
    "Text Analogy Ocrtest": "prithivMLmods/Qwen2-VL-Ocrtest-2B-Instruct"
}

# Preload models and processors into CUDA
models = {}
processors = {}
for name, model_id in MODEL_OPTIONS.items():
    print(f"Loading {name}...")
    models[name] = Qwen2VLForConditionalGeneration.from_pretrained(
        model_id,
        trust_remote_code=True,
        torch_dtype=torch.float16
    ).to("cuda").eval()
    processors[name] = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

# Get valid image extensions from PIL
image_extensions = Image.registered_extensions()

def identify_and_save_blob(blob_path):
    """Identifies if the blob is an image and saves it."""
    try:
        with open(blob_path, 'rb') as file:
            blob_content = file.read()
            try:
                Image.open(io.BytesIO(blob_content)).verify()  # Check if it's a valid image
                extension = ".png"  # Default to PNG for saving
                media_type = "image"
            except (IOError, SyntaxError):
                raise ValueError("Unsupported media type. Please upload a valid image.")

            filename = f"temp_{uuid.uuid4()}_media{extension}"
            with open(filename, "wb") as f:
                f.write(blob_content)

            return filename, media_type

    except FileNotFoundError:
        raise ValueError(f"The file {blob_path} was not found.")
    except Exception as e:
        raise ValueError(f"An error occurred while processing the file: {e}")

def get_media_file(media_input):
    """
    Ensures that the media input is a file path.
    If it is a PIL image, it saves it temporarily and returns the file path.
    """
    if isinstance(media_input, str):
        return media_input  # Already a file path
    else:
        if not isinstance(media_input, Image.Image):
            # Convert numpy array to PIL image if needed
            media_input = Image.fromarray(media_input)
        temp_filename = f"temp_{uuid.uuid4()}.png"
        media_input.save(temp_filename)
        return temp_filename

@spaces.GPU
def qwen_inference(model_name, media_input, text_input=None):
    """Handles inference for the selected model."""
    model = models[model_name]
    processor = processors[model_name]

    # Determine media type and obtain a file path if needed
    if isinstance(media_input, str):
        media_path = media_input
        if media_path.endswith(tuple(image_extensions.keys())):
            media_type = "image"
        else:
            try:
                media_path, media_type = identify_and_save_blob(media_input)
            except Exception as e:
                raise ValueError("Unsupported media type. Please upload a valid image.")
    else:
        # media_input is a PIL image (or numpy array) coming from gr.Image
        media_path = get_media_file(media_input)
        media_type = "image"

    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": media_type,
                    media_type: media_path
                },
                {"type": "text", "text": text_input},
            ],
        }
    ]

    text = processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    image_inputs, _ = process_vision_info(messages)
    inputs = processor(
        text=[text],
        images=image_inputs,
        padding=True,
        return_tensors="pt",
    ).to("cuda")

    streamer = TextIteratorStreamer(
        processor.tokenizer, skip_prompt=True, skip_special_tokens=True
    )
    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)

    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()

    buffer = ""
    for new_text in streamer:
        buffer += new_text
        # Remove <|im_end|> or similar tokens from the output
        buffer = buffer.replace("<|im_end|>", "")
        yield buffer

def format_plain_text(output_text):
    """Formats the output text as plain text without LaTeX delimiters."""
    plain_text = output_text.replace("\\(", "").replace("\\)", "").replace("\\[", "").replace("\\]", "")
    return plain_text

def generate_document(media_input, output_text, file_format, font_choice, font_size, line_spacing, alignment, image_size):
    """Generates a document with the input image and plain text output."""
    # Ensure media_input is a file path.
    media_path = get_media_file(media_input)
    plain_text = format_plain_text(output_text)
    if file_format == "pdf":
        return generate_pdf(media_path, plain_text, font_choice, font_size, line_spacing, alignment, image_size)
    elif file_format == "docx":
        return generate_docx(media_path, plain_text, font_choice, font_size, line_spacing, alignment, image_size)

def generate_pdf(media_path, plain_text, font_choice, font_size, line_spacing, alignment, image_size):
    """Generates a PDF document."""
    filename = f"output_{uuid.uuid4()}.pdf"
    doc = SimpleDocTemplate(
        filename,
        pagesize=A4,
        rightMargin=inch,
        leftMargin=inch,
        topMargin=inch,
        bottomMargin=inch
    )
    styles = getSampleStyleSheet()
    styles["Normal"].fontName = font_choice
    styles["Normal"].fontSize = int(font_size)
    styles["Normal"].leading = int(font_size) * line_spacing
    styles["Normal"].alignment = {
        "Left": 0,
        "Center": 1,
        "Right": 2,
        "Justified": 4
    }[alignment]

    # Register font (assumes font files are available in a folder named "font")
    font_path = f"font/{font_choice}"
    pdfmetrics.registerFont(TTFont(font_choice, font_path))

    story = []

    # Add image with size adjustment
    image_sizes = {
        "Small": (200, 200),
        "Medium": (400, 400),
        "Large": (600, 600)
    }
    img = RLImage(media_path, width=image_sizes[image_size][0], height=image_sizes[image_size][1])
    story.append(img)
    story.append(Spacer(1, 12))

    # Add plain text output
    text_para = Paragraph(plain_text, styles["Normal"])
    story.append(text_para)

    doc.build(story)
    return filename

def generate_docx(media_path, plain_text, font_choice, font_size, line_spacing, alignment, image_size):
    """Generates a DOCX document."""
    filename = f"output_{uuid.uuid4()}.docx"
    doc = docx.Document()

    # Add image with size adjustment
    image_sizes = {
        "Small": docx.shared.Inches(2),
        "Medium": docx.shared.Inches(4),
        "Large": docx.shared.Inches(6)
    }
    doc.add_picture(media_path, width=image_sizes[image_size])
    doc.add_paragraph()

    # Add plain text output
    paragraph = doc.add_paragraph()
    paragraph.paragraph_format.line_spacing = line_spacing
    paragraph.paragraph_format.alignment = {
        "Left": WD_ALIGN_PARAGRAPH.LEFT,
        "Center": WD_ALIGN_PARAGRAPH.CENTER,
        "Right": WD_ALIGN_PARAGRAPH.RIGHT,
        "Justified": WD_ALIGN_PARAGRAPH.JUSTIFY
    }[alignment]
    run = paragraph.add_run(plain_text)
    run.font.name = font_choice
    run.font.size = docx.shared.Pt(int(font_size))

    doc.save(filename)
    return filename

# CSS for output styling
css = """
  #output {
    height: 400px; 
    overflow: auto; 
    border: 1px solid #ccc; 
  }
.submit-btn {
    background-color: #cf3434  !important;
    color: white !important;
}
.submit-btn:hover {
    background-color: #ff2323 !important;
}
.download-btn {
    background-color: #35a6d6 !important;
    color: white !important;
}
.download-btn:hover {
    background-color: #22bcff !important;
}
"""

# Gradio app setup
with gr.Blocks(css=css) as demo:
    gr.Markdown("# Qwen2VL: Compact Vision & Language Processing")

    with gr.Tab(label="Image Input"):
        with gr.Row():
            with gr.Column():
                model_choice = gr.Dropdown(
                    label="Model Selection",
                    choices=list(MODEL_OPTIONS.keys()),
                    value="Latex OCR"
                )
                # Using gr.Image instead of gr.File for image upload
                input_media = gr.Image(
                    label="Upload Image", type="pil"
                )
                text_input = gr.Textbox(label="Question", placeholder="Ask a question about the image...")
                submit_btn = gr.Button(value="Submit", elem_classes="submit-btn")
            with gr.Column():
                output_text = gr.Textbox(label="Output Text", lines=10)
                plain_text_output = gr.Textbox(label="Standardized Plain Text", lines=10)

        submit_btn.click(
            qwen_inference, [model_choice, input_media, text_input], [output_text]
        ).then(
            lambda output_text: format_plain_text(output_text), [output_text], [plain_text_output]
        )

        # Add examples directly usable by clicking
        with gr.Row():
            gr.Examples(
                examples=[
                    ["examples/4.png", "solve the problem", "Math Prase"],
                    ["examples/1.png", "summarize the letter", "Text Analogy Ocrtest"],
                    ["examples/2.jpg", "Summarize the full image in detail", "Latex OCR"],
                    ["examples/3.png", "Describe the photo", "Qwen2VL Base"],
                ],
                inputs=[input_media, text_input, model_choice],
                outputs=[output_text, plain_text_output],
                fn=lambda img, question, model: qwen_inference(model, img, question),
                cache_examples=False,
            )
        with gr.Row():
            with gr.Column():
                line_spacing = gr.Dropdown(
                    choices=[0.5, 1.0, 1.15, 1.5, 2.0, 2.5, 3.0],
                    value=1.5,
                    label="Line Spacing"
                )
                font_size = gr.Dropdown(
                    choices=["8", "10", "12", "14", "16", "18", "20", "22", "24"],
                    value="12",
                    label="Font Size"
                )
                font_choice = gr.Dropdown(
                    choices=[
                        "DejaVuMathTeXGyre.ttf", 
                        "FiraCode-Medium.ttf", 
                        "InputMono-Light.ttf",
                        "JetBrainsMono-Thin.ttf", 
                        "ProggyCrossed Regular Mac.ttf", 
                        "SourceCodePro-Black.ttf", 
                        "arial.ttf", 
                        "calibri.ttf", 
                        "mukta-malar-extralight.ttf", 
                        "noto-sans-arabic-medium.ttf", 
                        "times new roman.ttf",
                        "ANGSA.ttf",
                        "Book-Antiqua.ttf",
                        "CONSOLA.TTF",
                        "COOPBL.TTF",
                        "Rockwell-Bold.ttf",
                        "Candara Light.TTF",
                        "Carlito-Regular.ttf Carlito-Regular.ttf",
                        "Castellar.ttf",
                        "Courier New.ttf",
                        "LSANS.TTF",
                        "Lucida Bright Regular.ttf",
                        "TRTempusSansITC.ttf",
                        "Verdana.ttf",
                        "bell-mt.ttf",
                        "eras-itc-light.ttf",
                        "fonnts.com-aptos-light.ttf",
                        "georgia.ttf",
                        "segoeuithis.ttf",
                        "youyuan.TTF",
                        "TfPonetoneExpanded-7BJZA.ttf",
                    ],
                    value="youyuan.TTF",
                    label="Font Choice"
                )
                alignment = gr.Dropdown(
                    choices=["Left", "Center", "Right", "Justified"],
                    value="Justified",
                    label="Text Alignment"
                )
                image_size = gr.Dropdown(
                    choices=["Small", "Medium", "Large"],
                    value="Small",
                    label="Image Size"
                )
                file_format = gr.Radio(["pdf", "docx"], label="File Format", value="pdf")
        with gr.Row():
            get_document_btn = gr.Button(value="Get Document", elem_classes="download-btn")
        get_document_btn.click(
            generate_document,
            [input_media, output_text, file_format, font_choice, font_size, line_spacing, alignment, image_size],
            gr.File(label="Download Document")
        )

demo.launch(debug=True)