app.py

import gradio as gr
import os
import shutil
import fitz
from PIL import Image
import numpy as np
import cv2
import pytesseract
from pytesseract import Output
import zipfile
from pdf2image import convert_from_path
import google.generativeai as genai
import json

from docx import Document
from docx.shared import Pt, RGBColor, Inches
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.enum.section import WD_SECTION
from docx.oxml import OxmlElement
from docx.oxml.ns import qn
from typing import Dict, Any, List, Union  # Ajout des imports typing nécessaires
import logging

# helpers functions
from helpers.rapport_generator import *
from helpers.text_extraction import *
from helpers.gemini_functions import *

def authenticate(username, password):
    return username == os.getenv("HF_USERNAME") and password == os.getenv("HF_PASSWORD")
            
# Main Processing Function
def process_pdf(pdf_file):
    template_dir = os.path.join(os.getcwd(), "templates")
    temp_dir = os.path.join(os.getcwd(), "temp_processing")
    output_dir = os.path.join(temp_dir, 'output_images')

    if os.path.exists(temp_dir):
        shutil.rmtree(temp_dir)
    os.makedirs(output_dir, exist_ok=True)

    path_to_data_to_extract = os.path.join(template_dir, "data_to_extract.json")
    text_file_path = os.path.join(output_dir, 'extracted_text.txt')  

    try:
        # Convert PDF to images and process
        images = convert_from_path(pdf_file.name)
        annotated_images = []

        # Process each page
        for i, img in enumerate(images):
            temp_img_path = os.path.join(temp_dir, f'temp_page_{i}.png')
            img.save(temp_img_path)
            blocks, annotated_image_path = process_image(temp_img_path, output_dir, i)
            annotated_images.append(annotated_image_path)
            save_extracted_text(blocks, i + 1, output_dir)

        # Create ZIP file
        zip_path = os.path.join(temp_dir, "annotated_images.zip")
        with zipfile.ZipFile(zip_path, 'w') as zipf:
            for img_path in annotated_images:
                zipf.write(img_path, os.path.basename(img_path))

        # Process with Gemini
        extracted_data = extract_data_with_gemini(text_file_path, path_to_data_to_extract)

        # Save extracted data to JSON file
        json_path = os.path.join(temp_dir, "extracted_data.json")
        with open(json_path, 'w', encoding='utf-8') as f:
            json.dump(extracted_data, f, ensure_ascii=False, indent=2)

        # Generate DOCX report
        try:
            docx_path = os.path.join(temp_dir, "rapport_extraction.docx")
            generator = RapportGenerator(json_path, docx_path)
            generator.generate_report()
        except Exception as e:
            raise gr.Error(f"Error processing rapport: {str(e)}")

        return text_file_path, zip_path, json_path, docx_path

    except Exception as e:
        raise gr.Error(f"Error processing PDF: {str(e)}")

# Gradio Interface
css = """
.gradio-container {
    font-family: 'IBM Plex Sans', sans-serif;
}
.gr-button {
    color: white;
    border-radius: 8px;
    background: linear-gradient(45deg, #7928CA, #FF0080);
    border: none;
}
"""

demo = gr.Interface(
    fn=process_pdf,
    inputs=[
        gr.File(
            label="Télécharger un document PDF",
            file_types=[".pdf"],
            type="filepath"
        )
    ],
    outputs=[
        gr.File(label="Texte extrait (TXT)"),
        gr.File(label="Images annotées (ZIP)"),
        gr.File(label="Données extraites (JSON)"),
        gr.File(label="Rapport généré (DOCX)") 
    ],
    title="ORDONNANCE DE REFERE<br>Extraction de texte PDF et création d'un rapport DOCX",
    description="""
    Téléchargez un document PDF pour :
    1. Extraire le contenu textuel
    2. Obtenir des images annotées montrant les blocs de texte détectés
    3. Extraire des données structurées grâce à une analyse IA
    4. Générer un rapport formaté au format DOCX

    Prend en charge les documents multi-pages et les documents juridiques français.
    """,
    css=css,
    examples=[],
    cache_examples=False,
    theme=gr.themes.Soft()
)

# Launch the app
if __name__ == "__main__":
    demo.launch(
        debug=True,
        auth=authenticate
    ).launch()