"""
Web-based Annotation Interface for Privacy Inferences
Modified for Hugging Face Spaces deployment
Run: python annotation_app.py
Then open: http://localhost:7860
"""
from flask import Flask, render_template, request, jsonify, send_file, session, redirect, url_for
import json
from pathlib import Path
from datetime import datetime
import os
from functools import wraps
import zipfile
import io
from collections import defaultdict
app = Flask(__name__)
# Security configuration
# Configuration Flask
# Security configuration - Simplified for HF Spaces
app.secret_key = os.environ.get('SECRET_KEY', 'votre-cle-secrete-tres-longue-a-changer-123456789')
# Configuration (ligne ~28)
RESULTS_DIR = Path("results")
ANNOTATIONS_DIR = Path("annotations")
ANNOTATIONS_DIR.mkdir(exist_ok=True)
MONTHS = [
'JANUARY', 'FEBRUARY', 'MARCH', 'APRIL', 'MAY', 'JUNE',
'JULY', 'AUGUST', 'SEPTEMBER', 'OCTOBER', 'NOVEMBER', 'DECEMBER'
]
CATEGORIES = ['health', 'religion', 'family', 'routines', 'work', 'leisure', 'economics']
# Admin password - DOIT รTRE DรFINI AVANT LE DEBUG
ADMIN_PASSWORD = os.environ.get('ADMIN_PASSWORD', 'antoine2025')
# Debug: Print config at startup - MAINTENANT C'EST BON
print("="*70)
print("๐ AUTHENTICATION CONFIG")
print("="*70)
print(f"SECRET_KEY configured: {'Yes' if app.config.get('SECRET_KEY') else 'No'}")
print(f"ADMIN_PASSWORD configured: {'Yes' if ADMIN_PASSWORD else 'No'}")
print(f"Session cookie secure: {app.config.get('SESSION_COOKIE_SECURE')}")
print("="*70)
# ============================================================================
# AUTHENTICATION
# ============================================================================
def login_required(f):
"""Decorator to protect routes"""
@wraps(f)
def decorated_function(*args, **kwargs):
# Check session AND cookie
if not session.get('logged_in') and request.cookies.get('auth_token') != 'authenticated':
return redirect(url_for('login'))
# If cookie is valid but session is empty, restore session
if not session.get('logged_in') and request.cookies.get('auth_token') == 'authenticated':
session['logged_in'] = True
return f(*args, **kwargs)
return decorated_function
@app.route('/login', methods=['GET', 'POST'])
def login():
"""Login page"""
# If already logged in, redirect to index
if session.get('logged_in') or request.cookies.get('auth_token') == 'authenticated':
return redirect(url_for('index'))
if request.method == 'POST':
password = request.form.get('password', '')
print(f"๐ Login attempt") # Debug
if password == ADMIN_PASSWORD:
session['logged_in'] = True
print("โ Login successful! Setting cookie...") # Debug
# Create response with redirect
response = redirect(url_for('index'))
# Set a persistent cookie as backup
response.set_cookie(
'auth_token',
'authenticated',
max_age=3600, # 1 hour
httponly=True,
samesite='Lax'
)
return response
else:
print("โ Login failed - wrong password") # Debug
error_message = "Mot de passe incorrect"
else:
error_message = None
# GET request or failed login - show login form
return f'''
Connexion - Privacy Annotation
๐ Privacy Annotation
{'
' + error_message + '
' if error_message else ''}
Interface d'annotation pour รฉvaluation des modรจles LLM
'''
@app.route('/logout')
def logout():
"""Logout"""
session.clear()
response = redirect(url_for('login'))
response.set_cookie('auth_token', '', max_age=0) # Delete cookie
return response
# ============================================================================
# HELPER FUNCTIONS
# ============================================================================
def get_available_models():
"""Get list of available model directories."""
if not RESULTS_DIR.exists():
return []
return [d.name for d in RESULTS_DIR.iterdir() if d.is_dir() and not d.name.startswith('.')]
def load_result(model_name, month):
"""Load a specific result file."""
filepath = RESULTS_DIR / model_name / "2017" / f"2017_{month}_P1.json"
if not filepath.exists():
return None
with open(filepath, 'r') as f:
return json.load(f)
def get_annotation_status():
"""Get status of all annotations."""
status = {}
models = get_available_models()
for model in models:
status[model] = {}
for month in MONTHS:
ann_file = ANNOTATIONS_DIR / f"{model}_{month}_annotations.json"
status[model][month] = ann_file.exists()
return status
def extract_inferences(response, category):
"""Extract inferences for a specific category."""
inferences = []
lines = response.split('\n')
in_category = False
for line in lines:
# Check if entering this category
if category.lower() in line.lower() and (':' in line or category in line):
in_category = True
continue
# Check if entering new category
if any(cat.lower() in line.lower() for cat in CATEGORIES if cat != category):
in_category = False
# Extract inference
if in_category and line.strip():
cleaned = line.strip().lstrip('โข-*0123456789.) ')
if len(cleaned) > 15:
inferences.append(cleaned)
return inferences
# ============================================================================
# MAIN ROUTES
# ============================================================================
@app.route('/')
# @login_required # โ COMMENTร TEMPORAIREMENT POUR DEBUG
def index():
"""Main page - show annotation dashboard."""
try:
models = get_available_models()
status = get_annotation_status()
print(f"๐ Models found: {models}") # Debug
print(f"๐ Status: {status}") # Debug
return render_template('index.html',
models=models,
months=MONTHS,
status=status)
except Exception as e:
print(f"โ ERROR in index(): {e}") # Debug
import traceback
traceback.print_exc()
return f"
Error loading dashboard
{e}
{traceback.format_exc()}
", 500
@app.route('/annotate//')
#@login_required
def annotate(model, month):
"""Annotation page for specific model and month - Simplified version"""
print(f"๐ Annotate called: model={model}, month={month}")
# Load result
result = load_result(model, month)
if not result:
print(f"โ Result not found for {model}/{month}")
return f"
Error: Result not found for {model}/{month}
", 404
response_text = result.get('response', '')
print(f"โ Result loaded. Response length: {len(response_text)} chars")
# Check if already annotated
ann_file = ANNOTATIONS_DIR / f"{model}_{month}_annotations.json"
existing_annotation = None
if ann_file.exists():
with open(ann_file, 'r') as f:
existing_annotation = json.load(f)
# Extract inferences by category
category_inferences = {}
for category in CATEGORIES:
inferences = extract_inferences(response_text, category)
category_inferences[category] = inferences
print(f" {category}: {len(inferences)} inferences")
# Count annotations
annotation_count = 0
if existing_annotation:
for cat_anns in existing_annotation.get('annotations', {}).values():
annotation_count += len(cat_anns)
# Build HTML directly (no template needed)
html = f'''
{model.upper()} - {month} 2017