113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
import zipfile
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import requests
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import base64
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from PIL import Image
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import pytesseract
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import io
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import xml.etree.ElementTree as ET
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import base64
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import io
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import pymupdf
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from pathlib import Path
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# === Configuration API ===
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api_url = "https://hackathon-api.mlo.sehlat.io/game/start"
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api_key = "OwogAztgWRdPfT2wWe7Xevdw98tdJelatlk82K6bozw"
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team_name = "Welch"
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headers = {
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"x-api-key": api_key,
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"Content-Type": "application/json"
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}
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payload = {
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"player_name": team_name
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}
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response = requests.post(api_url, headers=headers, json=payload)
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response_data = response.json()
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print(response_data)
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# === Traitement du passeport ===
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passport_b64 = response_data["client_data"]["passport"]
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image_bytes = base64.b64decode(passport_b64)
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image = Image.open(io.BytesIO(image_bytes))
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extracted_text = pytesseract.image_to_string(image, lang='eng')
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print("=== Texte extrait depuis le passeport ===\n")
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print(extracted_text)
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# === Traitement du profil DOCX ===
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profile_b64 = response_data["client_data"]["profile"]
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profile_bytes = base64.b64decode(profile_b64)
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zip_file = zipfile.ZipFile(io.BytesIO(profile_bytes))
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document_path = "word/document.xml"
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styles_path = "word/styles.xml"
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# Charger la carte des styles pour retrouver les titres
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style_map = {}
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if styles_path in zip_file.namelist():
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styles_xml = zip_file.read(styles_path)
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styles_tree = ET.fromstring(styles_xml)
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ns = {"w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main"}
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for style in styles_tree.findall(".//w:style", ns):
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style_id = style.attrib.get(f"{{{ns['w']}}}styleId")
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name_elem = style.find("w:name", ns)
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if name_elem is not None:
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style_name = name_elem.attrib.get(f"{{{ns['w']}}}val", "")
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style_map[style_id] = style_name
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# Parse du document principal
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if document_path in zip_file.namelist():
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document_xml = zip_file.read(document_path)
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tree = ET.fromstring(document_xml)
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ns = {"w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main"}
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print("\n=== Texte structuré extrait depuis le profil ===\n")
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for para in tree.findall(".//w:p", ns):
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texts = [node.text for node in para.findall(".//w:t", ns) if node.text]
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if not texts:
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continue
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full_text = " ".join(texts).strip()
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# Détecter le style (Heading1, Heading2, etc.)
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p_style = para.find(".//w:pStyle", ns)
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if p_style is not None:
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style_id = p_style.attrib.get(f"{{{ns['w']}}}val")
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style_name = style_map.get(style_id, "")
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if "Heading1" in style_name:
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print(f"\n# {full_text}\n")
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elif "Heading2" in style_name:
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print(f"\n## {full_text}\n")
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elif "Heading3" in style_name:
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print(f"\n### {full_text}\n")
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else:
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print(full_text)
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else:
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print(full_text)
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else:
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print("Le fichier 'word/document.xml' est introuvable dans le profil.")
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# === Traitement du fichier account (PDF) avec PyMuPDF ===
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print("\n=== Texte extrait depuis le fichier ACCOUNT (via PyMuPDF) ===\n")
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import base64
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from pdf2image import convert_from_bytes
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import pytesseract
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# Décode ton PDF base64
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account_b64 = response_data["client_data"].get("account")
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pdf_bytes = base64.b64decode(account_b64)
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# Convertit chaque page en image
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images = convert_from_bytes(pdf_bytes)
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# Applique OCR avec pytesseract
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for i, image in enumerate(images):
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text = pytesseract.image_to_string(image, lang="eng")
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print(f"--- Page {i + 1} ---")
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print(text)
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