src/streamlit_app.py

import streamlit as st
import json
import numpy as np
import joblib
from collections import defaultdict
from transformers import AutoTokenizer, AutoModel
from sklearn.metrics.pairwise import cosine_similarity
import torch
import re

# ---------------------------
# LegalBERT-based compliance checker
# ---------------------------
class GDPRComplianceChecker:
    def __init__(self, model_name="nlpaueb/bert-base-uncased-eurlex"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModel.from_pretrained(model_name)
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device).eval()

    def get_embeddings(self, texts):
        embeddings = []
        for text in texts:
            inputs = self.tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
            inputs = {k: v.to(self.device) for k, v in inputs.items()}
            with torch.no_grad():
                output = self.model(**inputs)
                embedding = output.last_hidden_state[:, 0, :].cpu().numpy()
                embeddings.append(embedding[0])
        return np.array(embeddings)

    def chunk_policy_text(self, text, chunk_size=500):
        paragraphs = re.split(r'\n{2,}|\.\s+', text)
        chunks, current = [], ""
        for para in paragraphs:
            if len(current) + len(para) < chunk_size:
                current += " " + para
            else:
                chunks.append(current.strip())
                current = para
        if current:
            chunks.append(current.strip())
        return [chunk for chunk in chunks if len(chunk) > 50]

    def load_gdpr_articles(self, gdpr_json):
        gdpr_map, texts = {}, []
        for article in gdpr_json:
            number, title = article["article_number"], article["article_title"]
            body = " ".join([f"{k} {v}" for sec in article["sections"] for k, v in sec.items()])
            full_text = f"Article {number}: {title}. {body}"
            gdpr_map[number] = {"title": title, "text": full_text}
            texts.append(full_text)
        embeddings = self.get_embeddings(texts)
        return gdpr_map, embeddings

    def calculate_compliance_score(self, policy_text, gdpr_map, gdpr_embeddings):
        chunks = self.chunk_policy_text(policy_text)
        if not chunks:
            return {"error": "Policy has no meaningful chunks."}
        chunk_embeddings = self.get_embeddings(chunks)
        sim_matrix = cosine_similarity(gdpr_embeddings, chunk_embeddings)

        article_scores = {}
        presence_threshold = 0.35
        total_score, counted_articles = 0, 0

        for i, (art_num, art_data) in enumerate(gdpr_map.items()):
            max_sim = np.max(sim_matrix[i])
            best_idx = np.argmax(sim_matrix[i])

            if max_sim < presence_threshold:
                continue

            score_pct = min(100, max(0, (max_sim - presence_threshold) / (1 - presence_threshold) * 100))
            article_scores[art_num] = {
                "article_title": art_data["title"],
                "compliance_percentage": round(score_pct, 2),
                "similarity_score": round(max_sim, 4),
                "matched_text_snippet": chunks[best_idx][:300] + "..."
            }
            total_score += score_pct
            counted_articles += 1

        overall = round(total_score / counted_articles, 2) if counted_articles else 0
        return {
            "overall_compliance_percentage": overall,
            "relevant_articles_analyzed": counted_articles,
            "total_policy_chunks": len(chunks),
            "article_scores": article_scores
        }

# ---------------------------
# Streamlit interface
# ---------------------------
st.set_page_config(page_title="GDPR Compliance Checker", layout="wide")
st.title("🛡️ GDPR Compliance Checker")

# Fixe Dateipfade
gdpr_path = "/app/src/gdpr_articles_baseline.json"
policy_path = "/app/src/sephora_com_policy.txt"

# Laden der Daten
with open(gdpr_path, "r", encoding="utf-8") as f:
    gdpr_data = json.load(f)

with open(policy_path, "r", encoding="utf-8") as f:
    policy_text = f.read()

# Automatische Analyse
with st.spinner("Analyzing using LegalBERT (Eurlex)..."):
    checker = GDPRComplianceChecker()
    gdpr_map, gdpr_embeddings = checker.load_gdpr_articles(gdpr_data)
    result = checker.calculate_compliance_score(policy_text, gdpr_map, gdpr_embeddings)

# Ergebnisse anzeigen
if result:
    st.subheader(f"✅ Overall Compliance Score: {result['overall_compliance_percentage']}%")
    st.markdown("---")
    st.subheader("📋 Detailed Article Breakdown")
    for art_num, data in sorted(result['article_scores'].items(), key=lambda x: -x[1]['compliance_percentage']):
        with st.expander(f"Article {art_num} - {data['article_title']} ({data['compliance_percentage']}%)"):
            st.write(f"**Similarity Score**: {data['similarity_score']}")
            st.write(f"**Matched Text**:\n\n{data['matched_text_snippet']}")