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# app.py | |
import os | |
import json | |
import gradio as gr | |
from gradio_pdf import PDF | |
import logging | |
from model import model_initialized | |
from pdf_processor import to_pdf, to_markdown, file_to_pdf | |
from config import config | |
from tts import text_to_speech, generate_audio # Import TTS module | |
from initializer import initialize_app | |
# Set up logging with ANSI escape codes for colored output | |
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") | |
def log_info(message: str): | |
logging.info(f"\033[92m{message}\033[0m") # Green for info | |
def log_error(message: str): | |
logging.error(f"\033[91m{message}\033[0m") # Red for errors | |
# Run the initialization once. | |
initialize_app() | |
# Load header HTML content | |
try: | |
with open("header.html", "r") as file: | |
header = file.read() | |
log_info("Header loaded successfully.") | |
except Exception as e: | |
log_error(f"Failed to load header.html. Error: {e}") | |
header = "<h1>Header not found</h1>" | |
try: | |
# Load the language options from the JSON file | |
with open('language_options.json', 'r') as file: | |
data = json.load(file) | |
# Create the all_lang list by concatenating the different language lists | |
all_lang = ['','auto'] + data["other_lang"] + data["latin_lang"] + data["arabic_lang"] + data["cyrillic_lang"] + data["devanagari_lang"] | |
except Exception as e: | |
log_error(f"Filed to load file language_options.json. Error: {e}") | |
all_lang = ['es', 'en'] | |
with gr.Blocks() as demo: | |
gr.HTML(header) | |
with gr.Row(): | |
with gr.Column(variant='panel', scale=5): | |
file_input = gr.File( | |
label="Please upload a PDF or image", | |
file_types=[".pdf", ".png", ".jpeg", ".jpg" ,"webp"]) | |
max_pages = gr.Slider(1, 20,config.get("max_pages_default", config.get("max_pages", 10)), step=1, label='Max convert pages') | |
with gr.Row(): | |
layout_mode = gr.Dropdown( | |
["layoutlmv3", "doclayout_yolo"], | |
label="Layout model", | |
value=config.get("layout_model_default", "layoutlmv3") | |
) | |
language = gr.Dropdown( | |
all_lang, | |
label="Language", | |
value=config.get("language_default", config.get("language", "auto")) | |
) | |
with gr.Row(): | |
formula_enable = gr.Checkbox(label="Enable formula recognition", value=True) | |
is_ocr = gr.Checkbox(label="Force enable OCR", value=False) | |
table_enable = gr.Checkbox(label="Enable table recognition", value=True) | |
with gr.Row(): | |
convert_button = gr.Button("Convert") | |
clear_button = gr.ClearButton(value="Clear") | |
pdf_display = PDF(label='PDF preview', interactive=False, visible=True, height=800) | |
with gr.Accordion("Examples:"): | |
example_root = os.path.join(os.path.dirname(__file__), "examples") | |
examples = [os.path.join(example_root, f) for f in os.listdir(example_root) if f.endswith("pdf")] | |
gr.Examples(examples=examples, inputs=file_input) | |
with gr.Column(variant='panel', scale=5): | |
output_file = gr.File(label="Convert result", interactive=False) | |
with gr.Tabs(): | |
with gr.Tab("Markdown rendering"): | |
md_render = gr.Markdown(label="Markdown rendering", height=1100, show_copy_button=True, line_breaks=True) | |
with gr.Tab("Markdown text"): | |
md_text = gr.TextArea(lines=45, show_copy_button=True) | |
# Audio component for TTS playback | |
audio_output = gr.Audio(label="Read Aloud", type="filepath") | |
read_button = gr.Button("Read Aloud") | |
file_input.change(fn=file_to_pdf, inputs=file_input, outputs=pdf_display) | |
convert_button.click( | |
fn=to_markdown, | |
inputs=[file_input, max_pages, is_ocr, layout_mode, formula_enable, table_enable, language], | |
outputs=[md_render, md_text, output_file, pdf_display] | |
) | |
# When "Read Aloud" is clicked, generate audio from the markdown text | |
read_button.click( | |
fn=generate_audio, | |
inputs=md_text, | |
outputs=audio_output | |
) | |
clear_button.add([file_input, md_render, pdf_display, md_text, output_file, is_ocr]) | |
if __name__ == "__main__": | |
import subprocess | |
print("Checking and downloading models if necessary...") | |
subprocess.run(["python", "download_models.py"]) | |
print("Models are ready!") | |
demo.launch(ssr_mode=True) | |