import os
import sys
from typing import Iterable, Optional, Tuple, Dict, Any, List
import hashlib
import spaces
import re
import time
import click
import gradio as gr
from io import BytesIO
from PIL import Image
from loguru import logger
from pathlib import Path
import torch
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
from transformers.image_utils import load_image
import fitz
import html2text
import markdown
import tempfile
from gradio.themes import Soft
from gradio.themes.utils import colors, fonts, sizes
# --- Theme and CSS Definition ---
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_700)",
button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)",
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()
# --- Model and App Logic ---
pdf_suffixes = [".pdf"]
image_suffixes = [".png", ".jpeg", ".jpg"]
device = "cuda" if torch.cuda.is_available() else "cpu"
logger.info(f"Using device: {device}")
# Model 1: Logics-Parsing
MODEL_ID_1 = "Logics-MLLM/Logics-Parsing"
logger.info(f"Loading model 1: {MODEL_ID_1}")
processor_1 = AutoProcessor.from_pretrained(MODEL_ID_1, trust_remote_code=True)
model_1 = Qwen2_5_VLForConditionalGeneration.from_pretrained(
MODEL_ID_1,
trust_remote_code=True,
torch_dtype=torch.float16 if device == "cuda" else torch.float32
).to(device).eval()
logger.info(f"Model '{MODEL_ID_1}' loaded successfully.")
# Model 2: Gliese-OCR-7B-Post1.0
MODEL_ID_2 = "prithivMLmods/Gliese-OCR-7B-Post1.0"
logger.info(f"Loading model 2: {MODEL_ID_2}")
processor_2 = AutoProcessor.from_pretrained(MODEL_ID_2, trust_remote_code=True)
model_2 = Qwen2_5_VLForConditionalGeneration.from_pretrained(
MODEL_ID_2,
trust_remote_code=True,
torch_dtype=torch.float16 if device == "cuda" else torch.float32
).to(device).eval()
logger.info(f"Model '{MODEL_ID_2}' loaded successfully.")
# Model 3: olmOCR-7B-0825
MODEL_ID_3 = "allenai/olmOCR-7B-0825"
logger.info(f"Loading model 3: {MODEL_ID_3}")
processor_3 = AutoProcessor.from_pretrained(MODEL_ID_3, trust_remote_code=True)
model_3 = Qwen2_5_VLForConditionalGeneration.from_pretrained(
MODEL_ID_3,
trust_remote_code=True,
torch_dtype=torch.float16 if device == "cuda" else torch.float32
).to(device).eval()
logger.info(f"Model '{MODEL_ID_3}' loaded successfully.")
@spaces.GPU
def parse_page(image: Image.Image, model_name: str) -> str:
if model_name == "Logics-Parsing":
current_processor, current_model = processor_1, model_1
elif model_name == "Gliese-OCR-7B-Post1.0":
current_processor, current_model = processor_2, model_2
elif model_name == "olmOCR-7B-0825":
current_processor, current_model = processor_3, model_3
else:
raise ValueError(f"Unknown model choice: {model_name}")
messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Parse this document page into a clean, structured HTML representation. Preserve the logical structure with appropriate tags for content blocks such as paragraphs (
), headings (
-
), tables (
), figures (), formulas (), and others. Include category tags, and filter out irrelevant elements like headers and footers."}]}]
prompt_full = current_processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = current_processor(text=prompt_full, images=[image.convert("RGB")], return_tensors="pt").to(device)
with torch.no_grad():
generated_ids = current_model.generate(**inputs, max_new_tokens=2048, do_sample=False)
generated_ids = generated_ids[:, inputs['input_ids'].shape[1]:]
output_text = current_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return output_text
def convert_file_to_images(file_path: str, dpi: int = 200) -> List[Image.Image]:
images = []
file_ext = Path(file_path).suffix.lower()
if file_ext in image_suffixes:
images.append(Image.open(file_path).convert("RGB"))
return images
if file_ext not in pdf_suffixes:
raise ValueError(f"Unsupported file type: {file_ext}")
try:
pdf_document = fitz.open(file_path)
zoom = dpi / 72.0
mat = fitz.Matrix(zoom, zoom)
for page_num in range(len(pdf_document)):
page = pdf_document.load_page(page_num)
pix = page.get_pixmap(matrix=mat)
img_data = pix.tobytes("png")
images.append(Image.open(BytesIO(img_data)).convert("RGB"))
pdf_document.close()
except Exception as e:
logger.error(f"Failed to convert PDF using PyMuPDF: {e}")
raise
return images
def get_initial_state() -> Dict[str, Any]:
return {"pages": [], "total_pages": 0, "current_page_index": 0, "page_results": []}
def load_and_preview_file(file_path: Optional[str]) -> Tuple[Optional[Image.Image], str, Dict[str, Any]]:
state = get_initial_state()
if not file_path:
return None, '
No file loaded
', state
try:
pages = convert_file_to_images(file_path)
if not pages:
return None, '
Could not load file
', state
state["pages"] = pages
state["total_pages"] = len(pages)
page_info_html = f'
Page 1 / {state["total_pages"]}
'
return pages[0], page_info_html, state
except Exception as e:
logger.error(f"Failed to load and preview file: {e}")
return None, '
Failed to load preview
', state
async def process_all_pages(state: Dict[str, Any], model_choice: str, progress=gr.Progress(track_tqdm=True)):
if not state or not state["pages"]:
error_msg = "
Please upload a file first.
"
return error_msg, "", "", None, "Error: No file to process", state
logger.info(f'Processing {state["total_pages"]} pages with model: {model_choice}')
start_time = time.time()
try:
page_results = []
for i, page_img in progress.tqdm(enumerate(state["pages"]), desc="Processing Pages"):
html_result = parse_page(page_img, model_choice)
page_results.append({'raw_html': html_result})
state["page_results"] = page_results
full_html_content = "\n\n".join([f'\n{res["raw_html"]}' for i, res in enumerate(page_results)])
full_markdown = html2text.html2text(full_html_content)
with tempfile.NamedTemporaryFile(mode='w', suffix='.md', delete=False, encoding='utf-8') as f:
f.write(full_markdown)
md_path = f.name
parsing_time = time.time() - start_time
cost_time_str = f'Total processing time: {parsing_time:.2f}s'
current_page_results = get_page_outputs(state)
return *current_page_results, md_path, cost_time_str, state
except Exception as e:
logger.error(f"Parsing failed: {e}", exc_info=True)
error_html = f"
An error occurred during processing:
{str(e)}
"
return error_html, "", "", None, f"Error: {str(e)}", state
def navigate_page(direction: str, state: Dict[str, Any]):
if not state or not state["pages"]:
return None, '
No file loaded
', *get_page_outputs(state), state
current_index = state["current_page_index"]
total_pages = state["total_pages"]
if direction == "prev":
new_index = max(0, current_index - 1)
elif direction == "next":
new_index = min(total_pages - 1, current_index + 1)
else:
new_index = current_index
state["current_page_index"] = new_index
image_preview = state["pages"][new_index]
page_info_html = f'
Page {new_index + 1} / {total_pages}
'
page_outputs = get_page_outputs(state)
return image_preview, page_info_html, *page_outputs, state
def get_page_outputs(state: Dict[str, Any]) -> Tuple[str, str, str]:
if not state or not state.get("page_results"):
return "
Process the document to see results.
", "", ""
index = state["current_page_index"]
if index >= len(state["page_results"]):
return "