app.py

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 time
from threading import Thread
from io import BytesIO
import uuid
import tempfile

import gradio as gr
import requests
import torch
from PIL import Image
import fitz

from transformers import (
    Qwen2_5_VLForConditionalGeneration,
    Qwen2VLForConditionalGeneration,
    AutoModelForCausalLM,
    AutoModelForVision2Seq,
    AutoModelForImageTextToText,
    AutoModel,
    AutoProcessor,
    TextIteratorStreamer,
    AutoTokenizer,
)

from transformers.image_utils import load_image

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

# --- Constants and Model Setup ---
MAX_INPUT_TOKEN_LENGTH = 4096
device = torch.device("cuda" 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)

# --- Model Loading ---
MODEL_ID_M = "LiquidAI/LFM2-VL-450M"
processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
model_m = AutoModelForImageTextToText.from_pretrained(
    MODEL_ID_M, trust_remote_code=True, torch_dtype=torch.float16
).to(device).eval()

MODEL_ID_T = "LiquidAI/LFM2-VL-1.6B"
processor_t = AutoProcessor.from_pretrained(MODEL_ID_T, trust_remote_code=True)
model_t = AutoModelForImageTextToText.from_pretrained(
    MODEL_ID_T, trust_remote_code=True, torch_dtype=torch.float16
).to(device).eval()

MODEL_ID_C = "HuggingFaceTB/SmolVLM-Instruct-250M"
processor_c = AutoProcessor.from_pretrained(MODEL_ID_C, trust_remote_code=True)
model_c = AutoModelForVision2Seq.from_pretrained(
    MODEL_ID_C, trust_remote_code=True, torch_dtype=torch.float16, _attn_implementation="flash_attention_2"
).to(device).eval()

MODEL_ID_G = "echo840/MonkeyOCR-pro-1.2B"
SUBFOLDER = "Recognition"
processor_g = AutoProcessor.from_pretrained(
    MODEL_ID_G, trust_remote_code=True, subfolder=SUBFOLDER
)
model_g = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_G, trust_remote_code=True, subfolder=SUBFOLDER, torch_dtype=torch.float16
).to(device).eval()

MODEL_ID_I = "UCSC-VLAA/VLAA-Thinker-Qwen2VL-2B"
processor_i = AutoProcessor.from_pretrained(MODEL_ID_I, trust_remote_code=True)
model_i = Qwen2VLForConditionalGeneration.from_pretrained(
    MODEL_ID_I, trust_remote_code=True, torch_dtype=torch.float16
).to(device).eval()

MODEL_ID_A = "nanonets/Nanonets-OCR-s"
processor_a = AutoProcessor.from_pretrained(MODEL_ID_A, trust_remote_code=True)
model_a = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_A, trust_remote_code=True, torch_dtype=torch.float16
).to(device).eval()

MODEL_ID_X = "prithivMLmods/Megalodon-OCR-Sync-0713"
processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True)
model_x = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_X, trust_remote_code=True, torch_dtype=torch.float16
).to(device).eval()

MODEL_ID_Z = "Vchitect/ShotVL-3B"
processor_z = AutoProcessor.from_pretrained(MODEL_ID_Z, trust_remote_code=True)
model_z = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_Z, trust_remote_code=True, torch_dtype=torch.float16
).to(device).eval()

# --- Moondream2 Model Loading ---
MODEL_ID_MD = "vikhyatk/moondream2"
REVISION_MD = "2025-06-21"
moondream = AutoModelForCausalLM.from_pretrained(
  MODEL_ID_MD,
  revision=REVISION_MD,
  trust_remote_code=True,
  torch_dtype=torch.float16,
  device_map={"": "cuda"},
)
tokenizer_md = AutoTokenizer.from_pretrained(MODEL_ID_MD, revision=REVISION_MD)

# --- Qwen2.5-VL-3B-Abliterated-Caption-it ---
MODEL_ID_N = "prithivMLmods/Qwen2.5-VL-3B-Abliterated-Caption-it"
processor_n = AutoProcessor.from_pretrained(MODEL_ID_N, trust_remote_code=True)
model_n = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_N, trust_remote_code=True, torch_dtype=torch.float16
).to(device).eval()

# --- LMM-R1-MGT-PerceReason ---
MODEL_ID_F = "VLM-Reasoner/LMM-R1-MGT-PerceReason"
processor_f = AutoProcessor.from_pretrained(MODEL_ID_F, trust_remote_code=True)
model_f = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_F, trust_remote_code=True, torch_dtype=torch.float16
).to(device).eval()

# TencentBAC/TBAC-VLR1-3B
MODEL_ID_G = "TencentBAC/TBAC-VLR1-3B"
processor_g = AutoProcessor.from_pretrained(MODEL_ID_G, trust_remote_code=True)
model_g = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_G, trust_remote_code=True, torch_dtype=torch.float16
).to(device).eval()

# OCRFlux-3B
MODEL_ID_V = "ChatDOC/OCRFlux-3B"
processor_v = AutoProcessor.from_pretrained(MODEL_ID_V, trust_remote_code=True)
model_v = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_V, trust_remote_code=True, torch_dtype=torch.float16
).to(device).eval()

# --- PDF Generation and Preview Utility Function ---
def generate_and_preview_pdf(image: Image.Image, text_content: str, font_size: int, line_spacing: float, alignment: str, image_size: str):
    """
    Generates a PDF, saves it, and then creates image previews of its pages.
    Returns the path to the PDF and a list of paths to the preview images.
    """
    if image is None or not text_content or not text_content.strip():
        raise gr.Error("Cannot generate PDF. Image or text content is missing.")

    # --- 1. Generate the PDF ---
    temp_dir = tempfile.gettempdir()
    pdf_filename = os.path.join(temp_dir, f"output_{uuid.uuid4()}.pdf")
    doc = SimpleDocTemplate(
        pdf_filename,
        pagesize=A4,
        rightMargin=inch, leftMargin=inch,
        topMargin=inch, bottomMargin=inch
    )
    styles = getSampleStyleSheet()
    style_normal = styles["Normal"]
    style_normal.fontSize = int(font_size)
    style_normal.leading = int(font_size) * line_spacing
    style_normal.alignment = {"Left": 0, "Center": 1, "Right": 2, "Justified": 4}[alignment]

    story = []

    img_buffer = BytesIO()
    image.save(img_buffer, format='PNG')
    img_buffer.seek(0)
    
    page_width, _ = A4
    available_width = page_width - 2 * inch
    image_widths = {
        "Small": available_width * 0.3,
        "Medium": available_width * 0.6,
        "Large": available_width * 0.9,
    }
    img_width = image_widths[image_size]
    img = RLImage(img_buffer, width=img_width, height=image.height * (img_width / image.width))
    story.append(img)
    story.append(Spacer(1, 12))

    cleaned_text = re.sub(r'#+\s*', '', text_content).replace("*", "")
    text_paragraphs = cleaned_text.split('\n')
    
    for para in text_paragraphs:
        if para.strip():
            story.append(Paragraph(para, style_normal))

    doc.build(story)

    # --- 2. Render PDF pages as images for preview ---
    preview_images = []
    try:
        pdf_doc = fitz.open(pdf_filename)
        for page_num in range(len(pdf_doc)):
            page = pdf_doc.load_page(page_num)
            pix = page.get_pixmap(dpi=150)
            preview_img_path = os.path.join(temp_dir, f"preview_{uuid.uuid4()}_p{page_num}.png")
            pix.save(preview_img_path)
            preview_images.append(preview_img_path)
        pdf_doc.close()
    except Exception as e:
        print(f"Error generating PDF preview: {e}")
        
    return pdf_filename, preview_images


# --- Core Application Logic ---
@spaces.GPU
def process_document_stream(
    model_name: str, 
    image: Image.Image, 
    prompt_input: str, 
    max_new_tokens: int,
    temperature: float,
    top_p: float,
    top_k: int,
    repetition_penalty: float
):
    """
    Main generator function that handles model inference tasks with advanced generation parameters.
    """
    if image is None:
        yield "Please upload an image.", ""
        return
    if not prompt_input or not prompt_input.strip():
        yield "Please enter a prompt.", ""
        return

    if model_name == "Moondream2(vision)":
        image_embeds = moondream.encode_image(image)
        answer = moondream.answer_question(
            image_embeds=image_embeds,
            question=prompt_input,
            tokenizer=tokenizer_md
        )
        yield answer, answer
        return

    if model_name == "LFM2-VL-450M(fast)": processor, model = processor_m, model_m
    elif model_name == "LFM2-VL-1.6B(fast)": processor, model = processor_t, model_t
    elif model_name == "ShotVL-3B(cinematic)": processor, model = processor_z, model_z
    elif model_name == "SmolVLM-Instruct-250M(smol)": processor, model = processor_c, model_c
    elif model_name == "MonkeyOCR-pro-1.2B(ocr)": processor, model = processor_g, model_g
    elif model_name == "VLAA-Thinker-Qwen2VL-2B(reason)": processor, model = processor_i, model_i
    elif model_name == "Nanonets-OCR-s(ocr)": processor, model = processor_a, model_a
    elif model_name == "Megalodon-OCR-Sync-0713(ocr)": processor, model = processor_x, model_x
    elif model_name == "Qwen2.5-VL-3B-Abliterated-Caption-it(caption)": processor, model = processor_n, model_n
    elif model_name == "LMM-R1-MGT-PerceReason(reason)": processor, model = processor_f, model_f 
    elif model_name == "TBAC-VLR1-3B(open-r1)": processor, model = processor_g, model_g
    elif model_name == "OCRFlux-3B(ocr)": processor, model = processor_v, modelv
    else:
        yield "Invalid model selected.", ""
        return

    messages = [{"role": "user", "content": [{"type": "image", "image": image}, {"type": "text", "text": prompt_input}]}]
    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=True, 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
        buffer = buffer.replace("<|im_end|>", "")
        time.sleep(0.01)
        yield buffer , buffer

    yield buffer, buffer


# --- Gradio UI Definition ---
def create_gradio_interface():
    """Builds and returns the Gradio web interface."""
    css = """
    .main-container { max-width: 1400px; margin: 0 auto; }
    .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; }
    #gallery { min-height: 400px; }
    """
    with gr.Blocks(theme="bethecloud/storj_theme", css=css) as demo:
        gr.HTML("""
        <div class="title" style="text-align: center">
            <h1>Tiny VLMs Lab🧪</h1>
            <p style="font-size: 1.1em; color: #6b7280; margin-bottom: 0.6em;">
                Tiny VLMs for Image Content Extraction and Understanding
            </p>
        </div>
        """)

        with gr.Row():
            # Left Column (Inputs)
            with gr.Column(scale=1):
                model_choice = gr.Dropdown(
                    choices=["LFM2-VL-450M(fast)", "LFM2-VL-1.6B(fast)", "SmolVLM-Instruct-250M(smol)", "Moondream2(vision)", "ShotVL-3B(cinematic)", "Megalodon-OCR-Sync-0713(ocr)", 
                             "VLAA-Thinker-Qwen2VL-2B(reason)", "MonkeyOCR-pro-1.2B(ocr)", "Qwen2.5-VL-3B-Abliterated-Caption-it(caption)", "Nanonets-OCR-s(ocr)",
                             "LMM-R1-MGT-PerceReason(reason)", "OCRFlux-3B(ocr)", "TBAC-VLR1-3B(open-r1)"],
                    label="Select Model", value= "LFM2-VL-450M(fast)"
                )
                prompt_input = gr.Textbox(label="Query Input", placeholder="✦︎ Enter your query", value="Describe the image!")
                image_input = gr.Image(label="Upload Image", type="pil", sources=['upload'])
                
                with gr.Accordion("Advanced Settings", open=False):
                    max_new_tokens = gr.Slider(minimum=512, maximum=8192, value=4096, step=256, label="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.Markdown("### PDF Export Settings")
                    font_size = gr.Dropdown(choices=["8", "10", "12", "14", "16", "18"], value="12", label="Font Size")
                    line_spacing = gr.Dropdown(choices=[1.0, 1.15, 1.5, 2.0], value=1.15, label="Line Spacing")
                    alignment = gr.Dropdown(choices=["Left", "Center", "Right", "Justified"], value="Justified", label="Text Alignment")
                    image_size = gr.Dropdown(choices=["Small", "Medium", "Large"], value="Medium", label="Image Size in PDF")

                process_btn = gr.Button("🚀 Process Image", variant="primary", elem_classes=["process-button"], size="lg")
                clear_btn = gr.Button("🗑️ Clear All", variant="secondary")

            # Right Column (Outputs)
            with gr.Column(scale=2):
                with gr.Tabs() as tabs:
                    with gr.Tab("📝 Extracted Content"):
                        raw_output_stream = gr.Textbox(label="Raw Model Output Stream", interactive=False, lines=15, show_copy_button=True)
                        with gr.Row():
                            examples = gr.Examples(
                                examples=["examples/1.png", "examples/2.png", "examples/3.png",
                                          "examples/4.png", "examples/5.png", "examples/6.png"],
                                inputs=image_input, label="Examples"
                            )
                        gr.Markdown("[Report-Bug💻](https://huggingface.co/spaces/prithivMLmods/Tiny-VLMs-Lab/discussions) | [prithivMLmods🤗](https://huggingface.co/prithivMLmods)")
                    
                    with gr.Tab("📰 README.md"):
                        with gr.Accordion("(Result.md)", open=True): 
                            markdown_output = gr.Markdown()

                    with gr.Tab("📋 PDF Preview"):
                        generate_pdf_btn = gr.Button("📄 Generate PDF & Render", variant="primary")
                        pdf_output_file = gr.File(label="Download Generated PDF", interactive=False)
                        pdf_preview_gallery = gr.Gallery(label="PDF Page Preview", show_label=True, elem_id="gallery", columns=2, object_fit="contain", height="auto")

        # Event Handlers
        def clear_all_outputs():
            return None, "", "Raw output will appear here.", "", None, None

        process_btn.click(
            fn=process_document_stream,
            inputs=[model_choice, image_input, prompt_input, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
            outputs=[raw_output_stream, markdown_output]
        )
        
        generate_pdf_btn.click(
            fn=generate_and_preview_pdf,
            inputs=[image_input, raw_output_stream, font_size, line_spacing, alignment, image_size],
            outputs=[pdf_output_file, pdf_preview_gallery]
        )

        clear_btn.click(
            clear_all_outputs,
            outputs=[image_input, prompt_input, raw_output_stream, markdown_output, pdf_output_file, pdf_preview_gallery]
        )
    return demo

if __name__ == "__main__":
    demo = create_gradio_interface()
    demo.queue(max_size=50).launch(share=True, ssr_mode=False, show_error=True)