diff --git a/.env.example b/.env.example index ea4280e..9d2f37a 100644 --- a/.env.example +++ b/.env.example @@ -1,3 +1,4 @@ OPENAI_API_KEY= OPENAI_MODEL= +GEMINI_API_KEY= LOG_LEVEL=INFO diff --git a/README.md b/README.md index 195284e..0fe843c 100644 --- a/README.md +++ b/README.md @@ -16,8 +16,13 @@ and **aggregate** category scores into an overall score with strengths, risks, r > **Part of [Happy Hacking Space](https://github.com/HappyHackingSpace) - A community-driven > organization focused on security, AI, and software development.** +## Architecture & How It Works + +![Privacy Policy Analyzer System Workflow](docs/workflow.svg) + ## Features +- **Multi-LLM Provider Engine**: Seamlessly switch between **Google Gemini** (using official `google-genai` SDK) and **OpenAI GPT** models based on model name prefix. - **Auto-discovery**: Common paths → robots.txt/sitemaps → footer links. - **HTTP-first extraction**: `trafilatura` (clean text) or `BeautifulSoup` fallback; **Selenium** for dynamic pages. - **Structured scoring (JSON)**: Per-category (0–10) scores + rationales; aggregated to 0–100 overall in `scoring.py`. @@ -48,6 +53,7 @@ privacy-policy-analyzer/ ├── pyproject.toml # Project configuration ├── requirements.txt # Legacy requirements ├── .env.example # Environment template +├── RESULTS.md # Benchmarks & real-world scores (GitHub, TikTok, Meta) ├── .gitignore ├── LICENSE └── README.md @@ -56,7 +62,7 @@ privacy-policy-analyzer/ ## Requirements - Python **3.10.11 or higher** -- An **OpenAI API key** +- An **OpenAI API key** (for GPT models) and/or **Google Gemini API key** (for Gemini models) - (Optional) **Chrome/Chromium** on the machine (Selenium fallback; driver auto-installs) ## Installation @@ -100,8 +106,10 @@ Copy `.env.example` → `.env` and set your credentials: ``` OPENAI_API_KEY=sk-************************ -# Optional (overrides default): OPENAI_MODEL=gpt-4o + +# Required if using Gemini models (e.g. gemini-2.5-flash) +GEMINI_API_KEY=AIzaSy********************* ``` ## Usage diff --git a/RESULTS.MD b/RESULTS.MD new file mode 100644 index 0000000..05635f0 --- /dev/null +++ b/RESULTS.MD @@ -0,0 +1,71 @@ +# Privacy Policy Analyzer - Test Results + +This document contains the analysis results for **Facebook** and **TikTok** privacy policies using the newly refactored production-grade CLI tool. + +--- + +## 1. Facebook Analysis Results + +* **Target Website:** `https://facebook.com` +* **Resolved Privacy URL:** `https://www.facebook.com/privacy/policy/?entry_point=facebook_page_footer` +* **LLM Model Used:** `openai/gpt-oss-120b:free` (OpenRouter) +* **Total Chunks Analyzed:** 6 (5 valid chunks aggregated) + +### Score & Overview +* **Overall Privacy Score:** `42.16 / 100` (Moderate risk profile) +* **Confidence Level:** `1.0` +* **Total Red Flags Identified:** `20` + +### Top Strengths +1. **Lawful Basis & Purpose:** `5.8 / 10` +2. **Transparency & Notice:** `5.8 / 10` +3. **Collection & Minimization:** `5.2 / 10` + +### Top Risks +1. **Retention & Deletion:** `2.8 / 10` (High Risk) +2. **User Rights & Redress:** `3.2 / 10` (High Risk) +3. **Sensitive Children, Ads & Profiling:** `3.2 / 10` (High Risk) + +--- + +## 2. TikTok Analysis Results + +* **Target Website:** `https://tiktok.com` +* **Resolved Privacy URL:** `https://www.tiktok.com/legal/privacy-policy-row` +* **LLM Model Used:** `openai/gpt-oss-120b:free` (OpenRouter) +* **Total Chunks Analyzed:** 4 (2 valid chunks aggregated) + +### Score & Overview +* **Overall Privacy Score:** `49.9 / 100` (Moderate risk profile, slightly better than Facebook) +* **Confidence Level:** `1.0` +* **Total Red Flags Identified:** `9` + +### Top Strengths +1. **Lawful Basis & Purpose:** `6.5 / 10` +2. **Third Parties & Processors:** `6.5 / 10` +3. **User Rights & Redress:** `6.0 / 10` + +### Top Risks +1. **Retention & Deletion:** `3.0 / 10` (High Risk) +2. **Security & Breach:** `3.0 / 10` (High Risk) +3. **Cross Border Transfers:** `3.5 / 10` (Medium-High Risk) + +--- + +## 3. Performance & System Benchmarks + +| Metric | Facebook (`https://facebook.com`) | TikTok (`https://tiktok.com`) | +| :--- | :--- | :--- | +| **Discovery Time** | 1.60s | 5.11s (Discovered via Sitemap) | +| **Fetching Time** | 3.50s | 1.97s | +| **LLM Analysis Time** | 114.72s | 59.91s | +| **Total Duration** | 119.83s | 66.99s | +| **Average Per Chunk** | 19.12s | 14.98s | +| **Text Characters** | 48,763 chars | 31,001 chars | + +--- + +## 4. Key Takeaways +1. **Robust Sitemap Discovery:** The new scoring and sitemap heuristics successfully located TikTok's exact privacy policy at `https://www.tiktok.com/legal/privacy-policy-row` under 5.11s. +2. **Parallel Execution & Rate Limits:** The multi-threaded pipeline analyzed multiple policy chunks simultaneously. The integrated `tenacity` exponential retry logic flawlessly handled rate limits on the free tier backend. +3. **Clean Output Architecture:** Standardized error outputs (`click.secho(..., err=True)`) kept stdout completely clean, allowing accurate piping of the output JSON string. diff --git a/RESULTS.md b/RESULTS.md new file mode 100644 index 0000000..e91e62a --- /dev/null +++ b/RESULTS.md @@ -0,0 +1,80 @@ +# Privacy Policy Analysis Results + +This document records the empirical results of evaluating real-world privacy policies using the Privacy Policy Analyzer. Analyses were conducted using directly supported LLM providers on **Google Gemini** (`gemini-2.5-flash`). + +--- + +## 📊 Summary of Analyzed Policies + +| Platform | Domain | Resolved Policy URL | Overall Score | Red Flags | Confidence | Analysis Time | +| :--- | :--- | :--- | :---: | :---: | :---: | :---: | +| **GitHub** | `github.com` | [GitHub Privacy Statement](https://docs.github.com/site-policy/privacy-policies/github-privacy-statement) | **44.40 / 100** | 8 | 100% | 23.66s | +| **TikTok** | `tiktok.com` | [TikTok Privacy Policy](https://www.tiktok.com/legal/privacy-policy) | **41.41 / 100** | 15 | 100% | 36.90s | +| **Facebook** | `facebook.com` | [Meta Privacy Policy](https://www.facebook.com/privacy/policy/) | **40.15 / 100** | 9 | 100% | 28.62s | + +--- + +## 🔍 Detailed Breakdown per Platform + +### 1. GitHub (Score: 44.40/100) + +> [!NOTE] +> GitHub provides relatively transparent data sharing disclosures and clear lawful bases, but scores extremely poorly on retention timelines and cross-border safeguards in user-facing summaries. + +* **Top Strengths:** + 1. **Third Parties & Processors** (`8.5/10`) — Detailed processor disclosures and joint-controller roles are clearly demarcated. + 2. **Transparency & Notice** (`8.0/10`) — Standard simplified language with comprehensive version notices and clear contacts. + 3. **Lawful Basis & Purpose** (`7.5/10`) — Well-articulated data processing purposes and legitimate business interests. +* **Top Risks:** + 1. **Retention & Deletion** (`0.0/10`) — Criticized for indefinite retention clauses (e.g., "retained as long as your account is active"). + 2. **Cross-Border Transfers** (`0.5/10`) — Extremely vague transfer mechanism safety disclosures in main text chunks. + 3. **Security & Breach** (`1.0/10`) — Lack of concrete technical and organizational safety measures detailed in the text excerpt analyzed. + +--- + +### 2. TikTok (Score: 41.41/100) + +> [!WARNING] +> TikTok suffers from a massive volume of Red Flags (15 distinct flags detected). While transparent about *why* they collect data, their security standards and user-redress frameworks remain highly problematic under global scoring rubrics. + +* **Top Strengths:** + 1. **Lawful Basis & Purpose** (`7.0/10`) — Purposes of processing are detailed but heavily geared toward personalized content. + 2. **Secondary Use & Limits** (`6.67/10`) — Stated boundaries on auxiliary features. + 3. **Transparency & Notice** (`6.67/10`) — Highly structured layout despite massive length. +* **Top Risks:** + 1. **Security & Breach** (`0.0/10`) — Analyzed chunks contained zero actionable breach details or concrete technical guardrails. + 2. **Retention & Deletion** (`2.0/10`) — "Keep as long as necessary" phrasing used frequently without specific schedules. + 3. **User Rights & Redress** (`2.67/10`) — Complex mechanisms to object or request erasure for global users outside EEA/California jurisdictions. + +--- + +### 3. Facebook / Meta (Score: 40.15/100) + +> [!CAUTION] +> Meta's privacy policy is highly structured and written in plain language (high transparency scoring), yet the underlying substance scores heavily against user protection—particularly on indefinite data retention and aggressive cross-border transfers. + +* **Top Strengths:** + 1. **Transparency & Notice** (`7.0/10`) — Excellent usage of plain-language headings, bullet points, and interactive cookie control guidance. + 2. **Lawful Basis & Purpose** (`5.67/10`) — Categorized lists of purposes. + 3. **Secondary Use & Limits** (`5.67/10`) — Stated boundaries on ad rendering vs. diagnostic telemetry. +* **Top Risks:** + 1. **Retention & Deletion** (`0.67/10`) — Extremely low scoring due to massive loops of indefinite storage policies. + 2. **Security & Breach** (`1.67/10`) — Highly generic security assertions with no robust technical benchmarks. + 3. **Cross-Border Transfers** (`2.0/10`) — Aggressive globally dispersed storage notices with standard contractual clauses (SCC) obscured in deeply nested external links. + +--- + +## 🚀 Execution Commands + +To reproduce these exact results, configure your `.env` with a `GEMINI_API_KEY` and run: + +```bash +# Analyze GitHub +uv run python src/main.py --url https://github.com --model gemini-2.5-flash --report summary --max-chunks 2 + +# Analyze TikTok +uv run python src/main.py --url https://tiktok.com --model gemini-2.5-flash --report summary --max-chunks 3 + +# Analyze Facebook +uv run python src/main.py --url https://facebook.com --model gemini-2.5-flash --report summary --max-chunks 3 +``` diff --git a/docs/workflow.svg b/docs/workflow.svg new file mode 100644 index 0000000..fc2d714 --- /dev/null +++ b/docs/workflow.svg @@ -0,0 +1 @@ +Privacy Policy Analyzer - System WorkflowReceive User Target URLno-discover Flag set?YesNoUse target URL as-isAuto-Discovery Pipeline1. Heuristic Sitemap Scan(Query robots.txt & default sitemaps)Policy URL found?YesNoReturn sitemap URL2. Page Anchor Link Scan(Fetch HTML homepage and parse links)Filter links by privacy keywordsScore links (based on priority index & anchor text)Verify candidate (lightweight HTTP existence check)Pick highest scored verified linkText Extraction EngineFetch page content via HTTPHTTP successful & text found?YesNoExtract main text via Trafilatura / BeautifulSoupFallback to Headless Selenium DriverRender dynamic page & extract visible textAnalysis & AI CoreSplit text into paragraph-aware chunksParallel executionusing ThreadPoolExecutorand Tenacity robust retriesProcess Chunk 1Process Chunk 2Process Chunk NPerform structured LLM audit(Auto-select Gemini or OpenAI backend)AggregatorCollect chunk analysis objectsAggregate category scores(Calculate weighted averages)Extract top strengths, risks & red flagsPrint output report (JSON or human-readable) \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 375ca5d..5a0c864 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,6 +19,8 @@ dependencies = [ "selenium>=4.35.0", "chromedriver-autoinstaller>=0.6.4", "requests>=2.32.5", + "click>=8.1.0", + "google-genai>=1.0.0", ] [dependency-groups] diff --git a/src/analyzer/scoring.py b/src/analyzer/scoring.py index 96c42bc..285f519 100644 --- a/src/analyzer/scoring.py +++ b/src/analyzer/scoring.py @@ -19,17 +19,39 @@ def _avg(nums: list[float]) -> float: + """Calculate the average of a list of floats. + + Args: + nums: A list of float numbers. + + Returns: + The average value as a float, or 0.0 if the list is empty. + """ return sum(nums) / len(nums) if nums else 0.0 +def _get_score_descending(kv: tuple[str, float]) -> float: + """Helper key function to sort categories by score in descending order.""" + return -kv[1] + + +def _get_score_ascending(kv: tuple[str, float]) -> float: + """Helper key function to sort categories by score in ascending order.""" + return kv[1] + + def aggregate_chunk_results(chunk_json_list: list[dict[str, Any]]) -> dict[str, Any]: - """ - Aggregates per-chunk JSON results into a weighted overall report. - Expects each item to contain: - - "scores": {category: int 0..10} - - "rationales": {category: str} - - optional "red_flags": [str] - - optional "notes": [str] + """Aggregates per-chunk JSON results into a weighted overall report. + + Args: + chunk_json_list: A list of chunk evaluation results from LLM. Each item must contain + "scores" (dict mapping category keys to int 0..10), "rationales" (dict mapping + category keys to string explanations), optional "red_flags" (list of strings), + and optional "notes" (list of strings). + + Returns: + A aggregated report dictionary with overall score, confidence, category scores, + top strengths, top risks, red flags, and recommendations. """ per_cat: dict[str, list[float]] = {k: [] for k in _REQUIRED_KEYS} rationales: dict[str, list[str]] = {k: [] for k in _REQUIRED_KEYS} @@ -73,11 +95,11 @@ def aggregate_chunk_results(chunk_json_list: list[dict[str, Any]]) -> dict[str, strengths: list[tuple[str, float]] = sorted( ((k, v["score"]) for k, v in category_scores.items()), - key=lambda kv: -kv[1], + key=_get_score_descending, )[:3] risks: list[tuple[str, float]] = sorted( ((k, v["score"]) for k, v in category_scores.items()), - key=lambda kv: kv[1], + key=_get_score_ascending, )[:3] return { diff --git a/src/main.py b/src/main.py index acd9e8f..a3e1534 100644 --- a/src/main.py +++ b/src/main.py @@ -1,20 +1,25 @@ -import argparse +import time import gzip import io import json import os import pathlib +import re import sys import xml.etree.ElementTree as ET -from typing import Any, Dict, List, Optional, Tuple +from typing import Any from urllib.parse import urljoin, urlparse from analyzer.prompts import SYSTEM_SCORER, build_user_prompt from analyzer.scoring import aggregate_chunk_results +import click import requests from bs4 import BeautifulSoup from dotenv import load_dotenv from langchain_text_splitters import RecursiveCharacterTextSplitter +from google import genai +from google.genai import types as genai_types from openai import OpenAI +from tenacity import retry, stop_after_attempt, wait_random_exponential from selenium import webdriver from selenium.webdriver.chrome.options import Options @@ -38,6 +43,23 @@ load_dotenv() +# --------------------------------------------------------------------------- +# Priority-based regex patterns for privacy URL discovery. +# Lower index = higher priority. The resolver picks the URL matching the +# lowest-index pattern, so "privacy-policy" (idx 0) always beats a generic +# "privacy" (idx 6) hub page. +# --------------------------------------------------------------------------- +_PRIVACY_REGEX_PATTERNS = [ + re.compile(r"privacy-policy\b", re.IGNORECASE), # 1. Target: Full legal text + re.compile(r"\bprivacy/policy\b", re.IGNORECASE), # 2 + re.compile(r"privacy-policy-[a-z]+", re.IGNORECASE), # 3 + re.compile(r"\bdata-protection\b", re.IGNORECASE), # 4 + re.compile(r"\bsecurity-policy\b", re.IGNORECASE), # 5 + re.compile(r"\blegal-notice\b", re.IGNORECASE), # 6 + re.compile(r"\bprivacy\b", re.IGNORECASE), # 7. Target: Menu/Hub page + re.compile(r"\blegal\b", re.IGNORECASE), # 8 + re.compile(r"\bterms\b", re.IGNORECASE), # 9 +] _PRIVACY_CUES = ( "privacy", @@ -64,6 +86,11 @@ "privatsphäre", ) +_PRIVACY_KEYWORDS = [ + "privacy policy", + "privacy-policy", +] + _COMMON_PATHS = [ "/privacy", "/privacy-policy", @@ -77,15 +104,42 @@ "/tr/gizlilik-politikasi", ] +# Selenium timeouts (seconds) +_SELENIUM_PAGE_LOAD_TIMEOUT = 10 +_SELENIUM_WAIT_TIMEOUT = 5 + +# Minimum character thresholds for extracted text +_MIN_TEXT_LENGTH_MAIN = 100 #
tag must exceed this to be considered valid +_MIN_TEXT_LENGTH_POLICY = 400 # extracted policy text must exceed this to be kept + +# Priority/discovery configurations +_NO_MATCH_PRIORITY = 999 +_MAX_SPECIFIC_PRIORITY = 2 # Priorities <= 2 are considered highly specific policy pages def _is_privacy_like(s: str) -> bool: - """Heuristic check for privacy-related terms in a string.""" + """Heuristic check for privacy-related terms in a string. + + Args: + s: The string to check for privacy cues. + + Returns: + True if any privacy cue is found in the string, False otherwise. + """ s = (s or "").lower() return any(k in s for k in _PRIVACY_CUES) -def _http_get(url: str, timeout: int = 15) -> Optional[requests.Response]: - """HTTP GET with basic headers and redirects allowed.""" +def _http_get(url: str, timeout: int = 5) -> requests.Response | None: + """HTTP GET with basic headers and redirects allowed. + + Args: + url: The target URL to send the GET request to. + timeout: Timeout in seconds for the request. Defaults to 5. + + Returns: + The requests.Response object if status is OK (< 400) and contains text, + otherwise None. + """ try: r = requests.get( url, @@ -102,14 +156,31 @@ def _http_get(url: str, timeout: int = 15) -> Optional[requests.Response]: return None -def _fetch_text(url: str, timeout: int = 12) -> Optional[str]: - """Fetch raw text content via GET.""" +def _fetch_text(url: str, timeout: int = 5) -> str | None: + """Fetch raw text content via GET. + + Args: + url: The target URL to fetch text from. + timeout: Timeout in seconds for the request. Defaults to 5. + + Returns: + The response text content as a string, or None if the request failed. + """ r = _http_get(url, timeout=timeout) return r.text if r else None -def _head_ok(url: str, timeout: int = 8) -> bool: - """Lightweight existence probe using HEAD; redirects considered OK.""" +def _head_ok(url: str, timeout: int = 3) -> bool: + """Lightweight existence probe using HEAD; redirects considered OK. + + Args: + url: The target URL to probe. + timeout: Timeout in seconds for the request. Defaults to 3. + + Returns: + True if the URL exists and returns a successful status code (2xx) or + a redirect status code (3xx), False otherwise. + """ try: r = requests.head( url, @@ -130,26 +201,41 @@ def _head_ok(url: str, timeout: int = 8) -> bool: return False -def _extract_text_http(url: str) -> Optional[str]: +def _extract_text_http(url: str) -> str | None: + """Fetch text from
tag, fallback to . + + Args: + url: The target URL to extract text from. + + Returns: + The extracted policy text if its length is greater than or equal to + _MIN_TEXT_LENGTH_POLICY, otherwise None. + """ if _HAS_TRAFILATURA: try: downloaded = trafilatura.fetch_url(url) if downloaded: text = trafilatura.extract(downloaded, include_formatting=False) or "" t = text.strip() - return t if len(t) >= 400 else None + return t if len(t) >= _MIN_TEXT_LENGTH_POLICY else None except Exception: pass r = _http_get(url) if not r: return None + soup = BeautifulSoup(r.text, "html.parser") - body = soup.find("body") - t = body.get_text("\n").strip() if body else "" - return t if len(t) >= 400 else None + + # Extraction Logic: Prefer
, fallback to + content_element = soup.find("main") + if not content_element or not content_element.get_text(strip=True): + content_element = soup.find("body") + + t = content_element.get_text("\n").strip() if content_element else "" + return t if len(t) >= _MIN_TEXT_LENGTH_POLICY else None -def fetch_content_with_selenium(url: str) -> Optional[str]: +def fetch_content_with_selenium(url: str) -> str | None: """Return visible text using headless Chrome; robust for dynamic pages.""" chromedriver_autoinstaller.install() opts = Options() @@ -162,12 +248,25 @@ def fetch_content_with_selenium(url: str) -> Optional[str]: "--user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) " "AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" ) + opts.add_argument("--blink-settings=imagesEnabled=false") # do not load images + opts.page_load_strategy = 'eager' # do not wait for full load driver = webdriver.Chrome(options=opts) try: + driver.set_page_load_timeout(_SELENIUM_PAGE_LOAD_TIMEOUT) driver.get(url) - WebDriverWait(driver, 12).until( + WebDriverWait(driver, _SELENIUM_WAIT_TIMEOUT).until( EC.presence_of_element_located((By.TAG_NAME, "body")) ) + + # Selenium Extraction: Prefer
, fallback to + try: + content_element = driver.find_element(By.TAG_NAME, "main") + text = content_element.get_attribute("innerText") + if text and len(text.strip()) > _MIN_TEXT_LENGTH_MAIN: + return text + except Exception: + pass + return driver.find_element(By.TAG_NAME, "body").get_attribute("innerText") except Exception: return None @@ -175,7 +274,7 @@ def fetch_content_with_selenium(url: str) -> Optional[str]: driver.quit() -def fetch_policy_text(url: str, prefer: str = "auto") -> Optional[str]: +def fetch_policy_text(url: str, prefer: str = "auto") -> str | None: """Fetch policy text using HTTP first; fallback to Selenium if needed.""" if prefer in ("auto", "http"): t = _extract_text_http(url) @@ -187,19 +286,22 @@ def fetch_policy_text(url: str, prefer: str = "auto") -> Optional[str]: def _light_verify(url: str) -> bool: - """Low-cost check that a URL likely points to a privacy policy page.""" - t = _extract_text_http(url) - if not t: - return False - low = t[:3000].lower() - return any(k in low for k in _PRIVACY_CUES) and len(t) >= 500 + """Low-cost check that a URL likely points to a privacy policy page. + Args: + url: The candidate URL to verify. -def _get_sitemaps_from_robots(base_url: str) -> List[str]: + Returns: + True if the URL passes the lightweight HEAD existence probe, False otherwise. + """ + return _head_ok(url, timeout=3) #only check for existence + + +def _get_sitemaps_from_robots(base_url: str) -> list[str]: """Extract sitemap URLs from robots.txt; also try the default /sitemap.xml.""" parsed = urlparse(base_url) robots = f"{parsed.scheme}://{parsed.netloc}/robots.txt" - out: List[str] = [] + out: list[str] = [] txt = _fetch_text(robots) if txt: for line in txt.splitlines(): @@ -219,7 +321,7 @@ def _get_sitemaps_from_robots(base_url: str) -> List[str]: return uniq -def _fetch_sitemap_urls(url: str, max_urls: int = 50) -> List[str]: +def _fetch_sitemap_urls(url: str, max_urls: int = 50) -> list[str]: """Return privacy-like URLs found in the sitemap (gz and index supported).""" r = _http_get(url) if not r: @@ -235,7 +337,7 @@ def _fetch_sitemap_urls(url: str, max_urls: int = 50) -> List[str]: except Exception: return [] ns = {"sm": "http://www.sitemaps.org/schemas/sitemap/0.9"} - urls: List[str] = [] + urls: list[str] = [] if root.tag.endswith("sitemapindex"): for i, loc in enumerate(root.findall(".//sm:loc", ns)): if i >= 5: @@ -259,87 +361,267 @@ def _fetch_sitemap_urls(url: str, max_urls: int = 50) -> List[str]: return uniq -def _discover_candidates_from_html(start_url: str) -> List[str]: - """Collect privacy-like links from the HTML of the given page.""" - r = _http_get(start_url) - if not r: - return [] - soup = BeautifulSoup(r.text, "html.parser") - links: List[str] = [] - for a in soup.find_all("a", href=True): - text = (a.get_text(" ") or "") + " " + a["href"] # type: ignore[operator, index] - if _is_privacy_like(text): - links.append(urljoin(r.url, a["href"])) # type: ignore[index] - seen, uniq = set(), [] - for u in links: - if u not in seen: - seen.add(u) - uniq.append(u) - return uniq +def _get_url_priority(url: str) -> int: + """Return the priority index of a URL based on regex patterns. Lower is better. + Args: + url: The URL to evaluate. -def _extract_text_quality(url: str) -> Tuple[Optional[str], Optional[str]]: - """Extract and sanity-check text content for policy-ness.""" - if _HAS_TRAFILATURA: + Returns: + The priority index (0-based) based on the matching regex pattern, + or _NO_MATCH_PRIORITY if no pattern matches. + """ + for idx, pattern in enumerate(_PRIVACY_REGEX_PATTERNS): + if pattern.search(url): + return idx + return _NO_MATCH_PRIORITY + + +def _get_priority_key(item: tuple[int, str]) -> int: + """Helper key function to sort matches by priority index (ascending).""" + return item[0] + + +def find_best_policy_url(html_content: str, base_url: str) -> tuple[str, int] | None: + """Finds the single best-matching URL for a privacy-related link on the page. + + Args: + html_content: The HTML content of the page to search for links. + base_url: The base URL used to resolve relative links. + + Returns: + A tuple of (url, priority_index) if matches are found, otherwise None. + """ + if not html_content: + return None + + soup = BeautifulSoup(html_content, "html.parser") + + # Store tuples of (priority_index, full_url) + matches: list[tuple[int, str]] = [] + seen_urls = set() + + for a in soup.find_all("a", href=True): + href = a["href"] try: - downloaded = trafilatura.fetch_url(url) - if not downloaded: - return None, None - text = trafilatura.extract(downloaded, include_formatting=False) or "" - t = text.strip() - if len(t) >= 500 and _is_privacy_like(t[:2000]): - return t, url - return None, url - except Exception: - return None, None - r = _http_get(url) - if not r: - return None, None - soup = BeautifulSoup(r.text, "html.parser") - body = soup.find("body") - t = (body.get_text("\n").strip() if body else "")[:4000] - if len(t) >= 500 and _is_privacy_like(t): - return t, r.url - return None, r.url + full_url = urljoin(base_url, href) + except Exception as e: + click.secho(f" DEBUG: URL join error for '{href}': {e}", fg="yellow", dim=True, err=True) + continue + + if not full_url.startswith("http"): + continue + + if full_url in seen_urls: + continue + seen_urls.add(full_url) + + # Check regex priority + priority_idx = _get_url_priority(full_url) + if priority_idx < _NO_MATCH_PRIORITY: + matches.append((priority_idx, full_url)) + + if not matches: + return None + + # Sort by priority index (ascending), get the best match + matches.sort(key=_get_priority_key) + return (matches[0][1], matches[0][0]) -def resolve_privacy_url(input_url: str) -> Tuple[str, Optional[str]]: - """Resolve a likely privacy policy URL starting from any given page.""" +def _improve_candidate(candidate_url: str) -> str: + """If the found candidate is a generic 'hub' page (e.g. /privacy), + + fetch it and check if it links to a more specific policy (e.g. /privacy-policy). + """ + # 0=privacy-policy, 1=privacy/policy, 2=privacy-policy-x + # Generally, if we found something better than generic 'privacy' (index 6), we stick with it. + # But let's say anything > _MAX_SPECIFIC_PRIORITY is worth checking deeper. + priority = _get_url_priority(candidate_url) + if priority <= _MAX_SPECIFIC_PRIORITY: + return candidate_url + + click.secho(f" DEBUG: Candidate '{candidate_url}' is generic (Priority {priority}). Checking for deep links...", fg="yellow", dim=True, err=True) + r = _http_get(candidate_url) + if not r: + return candidate_url + + match_info = find_best_policy_url(r.text, r.url) + if match_info: + deep_url, deep_priority = match_info + if deep_priority < priority: + click.secho(f" DEBUG: Upgraded to deep link '{deep_url}' (Priority {deep_priority})", fg="yellow", dim=True, err=True) + return deep_url + + return candidate_url + + +def _collect_link_candidates(html_content: str, base_url: str, limit: int = 100) -> list[tuple[str, str]]: + """Collect all privacy-related link candidates from HTML. + + Args: + html_content: The HTML page content to extract links from. + base_url: The base URL of the page to resolve relative URLs. + limit: Maximum number of candidate links to collect. Defaults to 100. + + Returns: + List of (full_url, anchor_text) tuples, de-duplicated. + """ + if not html_content: + return [] + + soup = BeautifulSoup(html_content, "html.parser") + candidates: dict[str, str] = {} # url -> best_anchor_text + + for a in soup.find_all("a", href=True): + href = a["href"] + try: + full_url = urljoin(base_url, href) + except Exception as e: + click.secho(f" DEBUG: URL join error for '{href}': {e}", fg="yellow", dim=True, err=True) + continue + + if not full_url.startswith("http"): + continue + + # Get anchor text + anchor_text = (a.get_text(strip=True) or "").lower() + + # select candidates based on privacy-likeness or priority instead of just any link + url_priority = _get_url_priority(full_url) + anchor_priority = _get_url_priority(anchor_text) + if ( + _is_privacy_like(full_url) + or _is_privacy_like(anchor_text) + or url_priority < _NO_MATCH_PRIORITY + or anchor_priority < _NO_MATCH_PRIORITY + ): + existing = candidates.get(full_url, "") + if ( + not existing + or (any(kw in anchor_text for kw in _PRIVACY_KEYWORDS) and not any(kw in existing for kw in _PRIVACY_KEYWORDS)) + ): + candidates[full_url] = anchor_text + if len(candidates) >= limit: + break + + return [(url, text) for url, text in candidates.items()] + + +def _score_candidate(url: str, anchor_text: str = "") -> tuple[int, int]: + """Score a candidate URL and anchor text. + + Args: + url: The candidate URL to score. + anchor_text: The anchor text associated with the candidate URL. Defaults to "". + + Returns: + A tuple of (priority_index, anchor_bonus) where lower values are better. + anchor_bonus is -1 if anchor explicitly matches privacy keywords, otherwise 0. + """ + url_priority = _get_url_priority(url) + anchor_bonus = 0 + + # Give extra credit if anchor text explicitly mentions "privacy policy" + anchor_lower = (anchor_text or "").lower() + if any(kw in anchor_lower for kw in _PRIVACY_KEYWORDS): + anchor_bonus = -1 # Lower is better, so -1 boosts the score + + return (url_priority, anchor_bonus) + + +def _pick_best_verified_candidate(candidates: list[tuple[str, str]], max_verify: int = 5) -> str | None: + """Score candidates, verify top ones, and return the best verified candidate URL. + + Args: + candidates: A list of (url, anchor_text) candidates. + max_verify: Maximum number of candidates to verify. Defaults to 5. + + Returns: + The best verified privacy policy URL, or None if no candidate is verified. + """ + if not candidates: + return None + + # Score all candidates + scored = [(url, text, _score_candidate(url, text)) for url, text in candidates] + # Sort by (priority, anchor_bonus), ascending + scored.sort(key=lambda x: (x[2][0], x[2][1])) + + # Verify top candidates (up to max_verify) + for i, (url, text, score) in enumerate(scored[:max_verify]): + # if the score is already very good, skip verification + if score[0] <= 1: + return url + if _light_verify(url): + click.secho(f" DEBUG: Selected URL '{url}' from {len(candidates)} candidates (score: {score})", fg="yellow", dim=True, err=True) + return url + + return None + + +def resolve_privacy_url(input_url: str) -> tuple[str, str | None]: + """Resolve a likely privacy policy URL using link-based discovery, sitemaps, or common paths. + + Args: + input_url: The initial URL provided to find a privacy policy for. + + Returns: + A tuple of (resolved_url, original_input_url). + """ + # If input looks like privacy policy already if _is_privacy_like(input_url): return input_url, None - + parsed = urlparse(input_url) base = f"{parsed.scheme}://{parsed.netloc}".rstrip("/") - - path_heads: List[str] = [] - for p in _COMMON_PATHS: - cand = base + p - if _head_ok(cand) or _light_verify(cand): - if _light_verify(cand): - return cand, input_url - path_heads.append(cand) - - for cand in path_heads: - if _light_verify(cand): - return cand, input_url - + + # === PHASE 1: Link-based discovery === + # Collect links from input page and homepage + candidates_set: dict[str, str] = {} # url -> anchor_text + + for page_url in (input_url, base): + if not (resp := _http_get(page_url)): + continue + + for url, text in _collect_link_candidates(resp.text, resp.url, limit=100): + candidates_set.setdefault(url, text) + + if candidates_set: + candidates_list = [(url, text) for url, text in candidates_set.items()] + best_url = _pick_best_verified_candidate(candidates_list, max_verify=5) + if best_url: + return best_url, input_url + + # === PHASE 2: Sitemap-based discovery === for sm in _get_sitemaps_from_robots(base): for cand in _fetch_sitemap_urls(sm, max_urls=50): if _light_verify(cand): + click.secho(f" DEBUG: Found via sitemap: {cand}", fg="yellow", dim=True, err=True) return cand, input_url - - for cand in _discover_candidates_from_html(input_url): - text, _ = _extract_text_quality(cand) - if text: - return cand, input_url + + # === PHASE 3: Common paths (last resort) === + for candidate_url in (base + path for path in _COMMON_PATHS): + if _light_verify(candidate_url): + click.secho(f" DEBUG: Found via common path: {candidate_url}", fg="yellow", dim=True, err=True) + return candidate_url, input_url return input_url, None def split_text_into_chunks( text: str, chunk_size: int = 3500, chunk_overlap: int = 350 -) -> List[str]: - """Split text into chunks using paragraph-first recursive boundaries.""" +) -> list[str]: + """Split text into chunks using paragraph-first recursive boundaries. + + Args: + text: The text to split. + chunk_size: Target size of each chunk in characters. Defaults to 3500. + chunk_overlap: Overlap between adjacent chunks in characters. Defaults to 350. + + Returns: + A list of split text chunks. + """ splitter = RecursiveCharacterTextSplitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap, @@ -348,12 +630,68 @@ def split_text_into_chunks( return splitter.split_text(text or "") -def analyze_chunk_json(text_chunk: str, model: str) -> Optional[Dict[str, Any]]: - """Analyze a text chunk with the LLM and return one JSON object.""" +@retry( + stop=stop_after_attempt(5), + wait=wait_random_exponential(min=2, max=30), + reraise=True +) +def _analyze_chunk_gemini(text_chunk: str, model: str) -> dict[str, Any] | None: + """Analyze a text chunk using the Google Gemini API. + + Args: + text_chunk: The text content chunk to analyze. + model: The Gemini model name to use. + + Returns: + The scored and analyzed dictionary if successful, None otherwise. + + Raises: + RuntimeError: If GEMINI_API_KEY environment variable is not configured. + """ + api_key = os.getenv("GEMINI_API_KEY") + if not api_key: + raise RuntimeError("GEMINI_API_KEY is not set. Configure your .env file.") + + client = genai.Client(api_key=api_key) + resp = client.models.generate_content( + model=model, + contents=build_user_prompt(text_chunk), + config=genai_types.GenerateContentConfig( + system_instruction=SYSTEM_SCORER, + response_mime_type="application/json", + temperature=0.0, + max_output_tokens=8192, + ), + ) + content = (resp.text or "").strip() + try: + return json.loads(content) # type: ignore[no-any-return] + except Exception: + return None + + +@retry( + stop=stop_after_attempt(5), + wait=wait_random_exponential(min=2, max=30), + reraise=True +) +def _analyze_chunk_openai(text_chunk: str, model: str) -> dict[str, Any] | None: + """Analyze a text chunk with the OpenAI API and return one JSON object. + + Args: + text_chunk: The text content chunk to analyze. + model: The OpenAI-compatible model name to use. + + Returns: + The scored and analyzed dictionary if successful, None otherwise. + + Raises: + RuntimeError: If OPENAI_API_KEY environment variable is not configured. + """ api_key = os.getenv("OPENAI_API_KEY") if not api_key: raise RuntimeError("OPENAI_API_KEY is not set. Configure your .env file.") - client = OpenAI(api_key=api_key) + client = OpenAI(api_key=api_key, base_url=os.getenv("OPENAI_BASE_URL")) resp = client.chat.completions.create( model=model, messages=[ @@ -361,7 +699,7 @@ def analyze_chunk_json(text_chunk: str, model: str) -> Optional[Dict[str, Any]]: {"role": "user", "content": build_user_prompt(text_chunk)}, ], temperature=0, - max_tokens=600, + max_tokens=2000, response_format={"type": "json_object"}, ) content = (resp.choices[0].message.content or "").strip() @@ -371,137 +709,143 @@ def analyze_chunk_json(text_chunk: str, model: str) -> Optional[Dict[str, Any]]: return None -def main() -> None: - parser = argparse.ArgumentParser( - description="Privacy Policy Analyzer (auto-discovery + JSON scoring)" - ) - parser.add_argument("--url", type=str, help="Site or policy URL to analyze") - parser.add_argument( - "--model", - type=str, - default=os.getenv("OPENAI_MODEL", "gpt-4o"), - help="OpenAI chat model, e.g., gpt-4o", - ) - parser.add_argument( - "--chunk-size", type=int, default=3500, help="Character-based chunk size" - ) - parser.add_argument( - "--chunk-overlap", type=int, default=350, help="Overlap between chunks" - ) - parser.add_argument( - "--max-chunks", - type=int, - default=30, - help="Hard cap for analyzed chunks (tail chunks are merged).", - ) - parser.add_argument( - "--report", - type=str, - choices=["summary", "detailed", "full"], - default="summary", - help="Report detail level", - ) - parser.add_argument( - "--fetch", - type=str, - choices=["auto", "http", "selenium"], - default="auto", - help="Fetch method preference", - ) - parser.add_argument( - "--no-discover", - action="store_true", - help="Skip auto-discovery and analyze the given URL as-is", - ) +def analyze_chunk_json(text_chunk: str, model: str) -> dict[str, Any] | None: + """Analyze a text chunk with the LLM and return one JSON object. + + Automatically selects the backend based on the model name: + - Models starting with ``gemini`` → Google Gemini API (GEMINI_API_KEY) + - All other models → OpenAI-compatible API (OPENAI_API_KEY + OPENAI_BASE_URL) + + Args: + text_chunk: The text content chunk to analyze. + model: The LLM model name to use. + + Returns: + The scored and analyzed dictionary if successful, None otherwise. + """ + if model.lower().startswith("gemini"): + return _analyze_chunk_gemini(text_chunk, model) + return _analyze_chunk_openai(text_chunk, model) + + +@click.command() +@click.option("--url", prompt="Enter a site (or privacy policy) URL", help="Site or policy URL to analyze.") +@click.option("--model", default=lambda: os.getenv("OPENAI_MODEL", "gpt-4o"), help="LLM model name.") +@click.option("--chunk-size", default=10000, type=int, help="Character-based chunk size.") +@click.option("--chunk-overlap", default=350, type=int, help="Overlap between chunks.") +@click.option("--max-chunks", default=30, type=int, help="Hard cap for analyzed chunks.") +@click.option("--report", type=click.Choice(["summary", "detailed", "full"]), default="summary", help="Report detail level.") +@click.option("--fetch", "fetch_method", type=click.Choice(["auto", "http", "selenium"]), default="auto", help="Fetch method preference.") +@click.option("--no-discover", is_flag=True, help="Skip auto-discovery; analyze the given URL as-is.") +def main( + url: str, + model: str, + chunk_size: int, + chunk_overlap: int, + max_chunks: int, + report: str, + fetch_method: str, + no_discover: bool, +) -> None: + """Privacy Policy Analyzer \u2013 auto-discovery + JSON scoring.""" + from concurrent.futures import ThreadPoolExecutor + + start_total = time.time() + + # --- Phase 1: Resolve URL --- + click.secho("\n[1/3] Resolving Privacy URL...", fg="cyan") + start_discovery = time.time() + resolved_url, _ = (url, None) if no_discover else resolve_privacy_url(url) + discovery_time = time.time() - start_discovery + click.echo(f" Resolved to: {click.style(resolved_url, fg='green')} ({discovery_time:.2f}s)") + + # --- Phase 2: Fetch content --- + click.secho("[2/3] Fetching Policy Content...", fg="cyan") + start_fetch = time.time() + content = fetch_policy_text(resolved_url, prefer=fetch_method) + fetch_time = time.time() - start_fetch + click.echo(f" Content length: {len(content) if content else 0} chars ({fetch_time:.2f}s)") - args = parser.parse_args() - input_url = args.url or input("Enter a site (or privacy policy) URL: ").strip() - - resolved_url, _ = ( - (input_url, None) if args.no_discover else resolve_privacy_url(input_url) - ) - - content = fetch_policy_text(resolved_url, prefer=args.fetch) if not content: - print( - json.dumps( - { - "status": "error", - "reason": "fetch_failed", - "url": input_url, - "resolved_url": resolved_url, - } - ) - ) - return + click.secho("Error: Failed to fetch policy content.", fg="red", err=True) + click.echo(json.dumps({"status": "error", "reason": "fetch_failed", "url": url, "resolved_url": resolved_url})) + raise SystemExit(1) - chunks = split_text_into_chunks( - content, chunk_size=args.chunk_size, chunk_overlap=args.chunk_overlap - ) + chunks = split_text_into_chunks(content, chunk_size=chunk_size, chunk_overlap=chunk_overlap) if not chunks: - print( - json.dumps( - { - "status": "error", - "reason": "no_chunks", - "url": input_url, - "resolved_url": resolved_url, - } - ) - ) - return - - if len(chunks) > args.max_chunks: - head = chunks[: args.max_chunks - 1] - tail = " ".join(chunks[args.max_chunks - 1 :]) + click.secho("Error: No text chunks produced.", fg="red", err=True) + click.echo(json.dumps({"status": "error", "reason": "no_chunks", "url": url, "resolved_url": resolved_url})) + raise SystemExit(1) + + # Merge overflow chunks into the last allowed slot + if len(chunks) > max_chunks: + head = chunks[: max_chunks - 1] + tail = " ".join(chunks[max_chunks - 1 :]) chunks = head + [tail] - results: List[Dict[str, Any]] = [] - for i, chunk in enumerate(chunks, 1): - print(f"Analyzing chunk {i}/{len(chunks)}...") - j = analyze_chunk_json(chunk, model=args.model) - if isinstance(j, dict) and "scores" in j: - j["index"] = i - results.append(j) + # --- Phase 3: LLM analysis --- + click.secho(f"[3/3] Analyzing {len(chunks)} chunk(s) in parallel...", fg="cyan") + start_analysis = time.time() + results: list[dict[str, Any]] = [] + + with ThreadPoolExecutor(max_workers=5) as executor: + futures = { + executor.submit(analyze_chunk_json, chunk, model): i + for i, chunk in enumerate(chunks, 1) + } + for future in futures: + idx = futures[future] + res = future.result() + if res: + res["index"] = idx + results.append(res) + + analysis_time = time.time() - start_analysis + total_time = time.time() - start_total if not results: - print( - json.dumps( - { - "status": "error", - "reason": "no_valid_scores", - "url": input_url, - "resolved_url": resolved_url, - } - ) - ) - return + click.secho("Error: No valid scores returned by model.", fg="red", err=True) + click.echo(json.dumps({"status": "error", "reason": "no_valid_scores", "url": url, "resolved_url": resolved_url})) + raise SystemExit(1) + # --- Build output --- agg = aggregate_chunk_results(results) - base = { + base_info = { "status": "ok", - "url": input_url, + "url": url, "resolved_url": resolved_url, - "model": args.model, + "model": model, "chunks": len(chunks), "valid_chunks": len(results), } - if args.report == "summary": + if report == "summary": out = { - **base, + **base_info, "overall_score": agg["overall_score"], "confidence": agg["confidence"], "top_strengths": agg["top_strengths"], "top_risks": agg["top_risks"], "red_flags_count": len(agg["red_flags"]), } - elif args.report == "detailed": - out = {**base, **agg} + elif report == "detailed": + out = {**base_info, **agg} else: - out = {**base, **agg, "chunks": results} - - print(json.dumps(out, ensure_ascii=False, indent=2)) + out = {**base_info, **agg, "chunks": results} + + click.echo(json.dumps(out, ensure_ascii=False, indent=2)) + + # --- Performance summary (stderr, won't pollute JSON stdout) --- + click.secho("\n" + "=" * 40, fg="yellow", err=True) + click.secho("Performance Summary", bold=True, err=True) + click.secho("-" * 40, fg="yellow", err=True) + click.echo(f" Discovery: {discovery_time:>6.2f}s", err=True) + click.echo(f" Fetching: {fetch_time:>6.2f}s", err=True) + click.echo(f" LLM Analysis: {analysis_time:>6.2f}s", err=True) + click.echo(f" Total: {total_time:>6.2f}s", err=True) + if chunks: + click.echo(f" Per chunk: {(analysis_time / len(chunks)):>6.2f}s", err=True) + click.secho("=" * 40 + "\n", fg="yellow", err=True) if __name__ == "__main__": diff --git a/uv.lock b/uv.lock index 0a9dbbc..4920a65 100644 --- a/uv.lock +++ b/uv.lock @@ -1,14 +1,13 @@ version = 1 -revision = 3 requires-python = ">=3.10.11" [[package]] name = "annotated-types" version = "0.7.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = 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