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

from helpers.rapport_generator import RapportGenerator
from helpers.text_extraction import *

def authenticate(username, password):
    return username == os.getenv("HF_USERNAME") and password == os.getenv("HF_PASSWORD")
    
# Helper Functions
def convert_to_rgb(image_path):
    img = Image.open(image_path)
    rgb_img = img.convert("RGB")
    return rgb_img

def preprocess_image(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    denoised = cv2.fastNlMeansDenoising(binary, None, 30, 7, 21)
    resized = cv2.resize(denoised, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
    return resized

def extract_vertical_blocks(image):
    image_np = np.array(image)
    data = pytesseract.image_to_data(image_np, lang='fra', output_type=Output.DICT)

    blocks = []
    current_block = ""
    current_block_coords = [float('inf'), float('inf'), 0, 0]
    last_bottom = -1
    line_height = 0

    for i in range(len(data['text'])):
        if int(data['conf'][i]) > 0:
            text = data['text'][i]
            x, y, w, h = data['left'][i], data['top'][i], data['width'][i], data['height'][i]

            if line_height == 0:
                line_height = h * 1.2

            if y > last_bottom + line_height:
                if current_block:
                    blocks.append({
                        "text": current_block.strip(),
                        "coords": current_block_coords
                    })
                    current_block = ""
                    current_block_coords = [float('inf'), float('inf'), 0, 0]

            current_block += text + " "
            current_block_coords[0] = min(current_block_coords[0], x)
            current_block_coords[1] = min(current_block_coords[1], y)
            current_block_coords[2] = max(current_block_coords[2], x + w)
            current_block_coords[3] = max(current_block_coords[3], y + h)

            last_bottom = y + h

    if current_block:
        blocks.append({
            "text": current_block.strip(),
            "coords": current_block_coords
        })

    return blocks

def draw_blocks_on_image(image_path, blocks, output_path):
    image = cv2.imread(image_path)
    for block in blocks:
        coords = block['coords']
        cv2.rectangle(image, (coords[0], coords[1]), (coords[2], coords[3]), (0, 0, 255), 2)
    cv2.imwrite(output_path, image)
    return output_path

def process_image(image, output_folder, page_number):
    image = convert_to_rgb(image)
    blocks = extract_vertical_blocks(image)
    base_name = f'page_{page_number + 1}.png'
    image_path = os.path.join(output_folder, base_name)
    image.save(image_path)
    annotated_image_path = os.path.join(output_folder, f'annotated_{base_name}')
    annotated_image_path = draw_blocks_on_image(image_path, blocks, annotated_image_path)
    return blocks, annotated_image_path

def save_extracted_text(blocks, page_number, output_folder):
    text_file_path = os.path.join(output_folder, 'extracted_text.txt')
    with open(text_file_path, 'a', encoding='utf-8') as f:
        f.write(f"[PAGE {page_number}]\n")
        for block in blocks:
            f.write(block['text'] + "\n")
        f.write("[FIN DE PAGE]\n\n")
    return text_file_path

# Gemini Functions
def initialize_gemini():
    try:
        genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
        generation_config = {
            "temperature": 1,
            "top_p": 0.95,
            "top_k": 40,
            "max_output_tokens": 8192,
            "response_mime_type": "text/plain",
        }
        model = genai.GenerativeModel(
            model_name="gemini-1.5-pro",
            generation_config=generation_config,
        )
        return model
    except Exception as e:
        raise gr.Error(f"Error initializing Gemini: {str(e)}")

def create_prompt(extracted_text: str, path_to_data_to_extract: str) -> str:

    # load data to extract
    with open(path_to_data_to_extract, 'r', encoding='utf-8') as file:
        data_to_extract = json.load(file)

    prompt = f"""Tu es un assistant juridique expert en analyse de documents judiciaires français.
    Je vais te fournir le contenu d'un document judiciaire extrait d'un PDF.
    Ta tâche est d'analyser ce texte et d'en extraire les informations suivantes de manière précise :

    {json.dumps(data_to_extract, indent=2, ensure_ascii=False)}

    Voici quelques règles à suivre :
    - Si une information n'est pas présente dans le texte, indique "Non spécifié" pour cette catégorie.
    - Pour les noms des parties (demandeurs et défendeurs, et leurs avocats), liste tous ceux que tu trouves
    - Assure-toi de différencier correctement les demandeurs des défendeurs.
    - Si tu n'es pas sûr d'une information, indique-le clairement.

    Présente tes résultats sous forme de JSON, en utilisant les catégories mentionnées ci-dessus.

    Voici le contenu du document :

    {extracted_text.strip()}

    Analyse ce texte et fournis-moi les informations demandées au format JSON uniquement.""".strip()

    return prompt

def extract_data_with_gemini(text_file_path: str, path_to_data_to_extract: str) -> dict:
    try:
        # Initialize Gemini
        model = initialize_gemini()

        # Read the extracted text
        with open(text_file_path, 'r', encoding='utf-8') as f:
            extracted_text = f.read()

        # Create prompt and get response
        prompt = create_prompt(extracted_text, path_to_data_to_extract)
        response = model.generate_content(prompt)

        # Parse the JSON response
        try:
            # Extract JSON from the response text
            json_str = response.text
            if "json" in json_str.lower():
                json_str = json_str.split("json")[1].split("```")[0]
            elif "```" in json_str:
                json_str = json_str.split("```")[1]
            result = json.loads(json_str)
        except:
            result = {"error": "Failed to parse JSON response", "raw_response": response.text}

        return result
    except Exception as e:
        raise gr.Error(f"Error in Gemini processing: {str(e)}")


            
# 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
        docx_path = os.path.join(temp_dir, "rapport_extraction.docx")
        generator = RapportGenerator(json_path, docx_path)
        generator.generate_report()

        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)")  # Nouvelle sortie
    ],
    title="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=False,
        auth=authenticate
    ).launch()