#!/usr/bin/env python3
"""
Google Cloud Natural Language API - Entity, sentiment, and content analysis.

Adds NLP entity coverage, sentiment analysis, and content classification
as internal content-analysis enrichment only. It is unrelated to Google
Search ranking or official E-E-A-T scoring.

Usage:
    python nlp_analyze.py --text "Your content here" --json
    python nlp_analyze.py --url https://example.com --json
    python nlp_analyze.py --text "Your content" --features entities,sentiment,classify
"""

import argparse
import json
import sys
from typing import Optional

try:
    import requests
except ImportError:
    print("Error: requests library required. Install with: pip install requests", file=sys.stderr)
    sys.exit(1)

try:
    from google_auth import (
        get_api_key,
        google_api_key_headers,
        redact_google_api_key,
        validate_url,
    )
    from url_safety import URLSafetyError, safe_requests_get
except ImportError:
    import os
    sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
    from google_auth import (
        get_api_key,
        google_api_key_headers,
        redact_google_api_key,
        validate_url,
    )
    from url_safety import URLSafetyError, safe_requests_get

NLP_ENDPOINT = "https://language.googleapis.com/v2/documents:annotateText"
NLP_V1_ENTITIES_ENDPOINT = "https://language.googleapis.com/v1/documents:analyzeEntities"

# Free tier: 5,000 units/month per feature
# Paid: $0.001 per 1,000-character unit for entity/sentiment
FEATURES = {
    "entities": "extractEntities",
    "sentiment": "extractDocumentSentiment",
    "classify": "classifyText",
    "categories": "classifyText",
    "moderate": "moderateText",
}


def analyze_text(
    text: str,
    features: Optional[list] = None,
    api_key: Optional[str] = None,
    language: str = "en",
) -> dict:
    """
    Analyze text using Google Cloud Natural Language API.

    Args:
        text: Text content to analyze (max 1M characters).
        features: List of features: entities, sentiment, classify, moderate.
        api_key: Google API key.
        language: Language code (default: en).

    Returns:
        Dictionary with entities, sentiment, categories, and moderation results.
    """
    result = {
        "text_length": len(text),
        "language": language,
        "entities": [],
        "sentiment": None,
        "categories": [],
        "moderation": [],
        "error": None,
    }

    key = api_key or get_api_key()
    if not key:
        result["error"] = "No API key. Set GOOGLE_API_KEY or add 'api_key' to config."
        return result

    if features is None:
        features = ["entities", "sentiment", "classify"]

    document = {
        "type": "PLAIN_TEXT",
        "content": text[:100000],  # API limit
        "languageCode": language,
    }

    # Entities still use v1 because it returns Knowledge Graph metadata
    # and salience consistently. Other features stay on v2 annotateText.
    wants_entities = "entities" in features
    if wants_entities:
        body = {
            "document": document,
            "encodingType": "UTF8",
        }
        try:
            resp = requests.post(
                NLP_V1_ENTITIES_ENDPOINT,
                headers=google_api_key_headers(key),
                json=body,
                timeout=30,
            )

            if resp.status_code == 403:
                result["error"] = (
                    "Cloud Natural Language API access denied. Enable it in "
                    "GCP Console: APIs & Services > Library > Cloud Natural Language API. "
                    "Billing must be enabled on the project."
                )
                return result

            if resp.status_code == 429:
                result["error"] = "NLP API quota exceeded. Free tier: 5,000 units/month."
                return result

            resp.raise_for_status()
            entity_data = resp.json()
        except requests.exceptions.RequestException as e:
            result["error"] = f"NLP API request failed: {redact_google_api_key(e)}"
            return result

        for entity in entity_data.get("entities", []):
            mentions = entity.get("mentions", [])
            result["entities"].append({
                "name": entity.get("name", ""),
                "type": entity.get("type", "UNKNOWN"),
                "salience": round(entity.get("salience", 0), 4),
                "sentiment_score": entity.get("sentiment", {}).get("score"),
                "sentiment_magnitude": entity.get("sentiment", {}).get("magnitude"),
                "mention_count": len(mentions),
                "metadata": entity.get("metadata", {}),
            })

        result["entities"].sort(key=lambda e: e["salience"], reverse=True)

    feature_map = {}
    for f in features:
        api_feature = FEATURES.get(f)
        if api_feature and api_feature != "extractEntities":
            feature_map[api_feature] = True

    if not feature_map:
        return result

    body = {
        "document": document,
        "features": feature_map,
        "encodingType": "UTF8",
    }

    try:
        resp = requests.post(
            NLP_ENDPOINT,
            headers=google_api_key_headers(key),
            json=body,
            timeout=30,
        )

        if resp.status_code == 403:
            result["error"] = (
                "Cloud Natural Language API access denied. Enable it in "
                "GCP Console: APIs & Services > Library > Cloud Natural Language API. "
                "Billing must be enabled on the project."
            )
            return result

        if resp.status_code == 429:
            result["error"] = "NLP API quota exceeded. Free tier: 5,000 units/month."
            return result

        resp.raise_for_status()
        data = resp.json()
    except requests.exceptions.RequestException as e:
        result["error"] = f"NLP API request failed: {redact_google_api_key(e)}"
        return result

    # Document sentiment
    doc_sentiment = data.get("documentSentiment", {})
    if doc_sentiment:
        score = doc_sentiment.get("score", 0)
        magnitude = doc_sentiment.get("magnitude", 0)
        if score > 0.25:
            tone = "positive"
        elif score < -0.25:
            tone = "negative"
        else:
            tone = "neutral"

        result["sentiment"] = {
            "score": round(score, 3),
            "magnitude": round(magnitude, 3),
            "tone": tone,
            "interpretation": (
                f"{'Positive' if score > 0 else 'Negative' if score < 0 else 'Neutral'} "
                f"(score: {score:.2f}) with "
                f"{'high' if magnitude > 2 else 'moderate' if magnitude > 0.5 else 'low'} "
                f"emotional content (magnitude: {magnitude:.2f})"
            ),
        }

        # Sentence-level sentiment
        sentences = data.get("sentences", [])
        if sentences:
            result["sentiment"]["sentence_count"] = len(sentences)
            sent_scores = [s.get("sentiment", {}).get("score", 0) for s in sentences]
            result["sentiment"]["most_positive"] = max(sent_scores) if sent_scores else 0
            result["sentiment"]["most_negative"] = min(sent_scores) if sent_scores else 0

    # Categories (content classification)
    for cat in data.get("categories", []):
        result["categories"].append({
            "name": cat.get("name", ""),
            "confidence": round(cat.get("confidence", 0), 4),
        })

    # Moderation categories
    for mod in data.get("moderationCategories", []):
        if mod.get("confidence", 0) > 0.5:
            result["moderation"].append({
                "name": mod.get("name", ""),
                "confidence": round(mod.get("confidence", 0), 4),
            })

    return result


def analyze_url(
    url: str,
    features: Optional[list] = None,
    api_key: Optional[str] = None,
) -> dict:
    """
    Fetch a URL's text content and analyze it.

    Args:
        url: URL to fetch and analyze.
        features: NLP features to extract.
        api_key: API key override.

    Returns:
        Dictionary with NLP analysis results.
    """
    if not validate_url(url):
        return {"error": "Invalid URL. Only http/https URLs to public hosts are accepted."}

    # Fetch the page text
    try:
        resp = safe_requests_get(
            url,
            timeout=30,
            headers={"User-Agent": "Mozilla/5.0 (compatible; ClaudeSEO/1.7 NLP Analyzer)"},
        )
        resp.raise_for_status()
        html = resp.text
    except URLSafetyError as e:
        return {"error": f"URL blocked by SSRF protection: {e}"}
    except requests.exceptions.RequestException as e:
        return {"error": f"Could not fetch URL: {redact_google_api_key(e)}"}

    # Extract text from HTML (simple approach)
    try:
        from bs4 import BeautifulSoup
        soup = BeautifulSoup(html, "html.parser")
        # Remove script and style
        for tag in soup(["script", "style", "nav", "footer", "header"]):
            tag.decompose()
        text = soup.get_text(separator=" ", strip=True)
    except ImportError:
        # Fallback: regex-based text extraction
        import re
        text = re.sub(r"<script[^>]*>.*?</script>", "", html, flags=re.DOTALL | re.IGNORECASE)
        text = re.sub(r"<style[^>]*>.*?</style>", "", text, flags=re.DOTALL | re.IGNORECASE)
        text = re.sub(r"<[^>]+>", " ", text)
        text = re.sub(r"\s+", " ", text).strip()

    if not text or len(text) < 50:
        return {"error": "Extracted text too short for meaningful NLP analysis."}

    result = analyze_text(text, features=features, api_key=api_key)
    result["source_url"] = url
    result["extracted_text_length"] = len(text)
    return result


def main():
    parser = argparse.ArgumentParser(
        description="Google Cloud Natural Language API - Entity/sentiment/classification for SEO"
    )
    parser.add_argument("--text", "-t", help="Text to analyze")
    parser.add_argument("--url", "-u", help="URL to fetch and analyze")
    parser.add_argument(
        "--features", "-f",
        default="entities,sentiment,classify",
        help="Comma-separated features: entities, sentiment, classify, moderate (default: entities,sentiment,classify)",
    )
    parser.add_argument("--api-key", help="API key override")
    parser.add_argument("--json", "-j", action="store_true", help="Output as JSON")

    args = parser.parse_args()

    if not args.text and not args.url:
        print("Error: Provide --text or --url to analyze.", file=sys.stderr)
        sys.exit(1)

    features = [f.strip() for f in args.features.split(",")]

    if args.url:
        result = analyze_url(args.url, features=features, api_key=args.api_key)
    else:
        result = analyze_text(args.text, features=features, api_key=args.api_key)

    if result.get("error"):
        print(f"Error: {result['error']}", file=sys.stderr)
        if not args.json:
            sys.exit(1)

    if args.json:
        print(json.dumps(result, indent=2))
    else:
        if result.get("source_url"):
            print(f"=== NLP Analysis: {result['source_url']} ===")
            print(f"Text extracted: {result.get('extracted_text_length', 0):,} chars")
        else:
            print(f"=== NLP Analysis ({result.get('text_length', 0):,} chars) ===")

        sent = result.get("sentiment")
        if sent:
            print(f"\nSentiment: {sent['tone'].upper()} (score: {sent['score']}, magnitude: {sent['magnitude']})")
            print(f"  {sent['interpretation']}")

        entities = result.get("entities", [])
        if entities:
            print(f"\nTop Entities ({len(entities)} total):")
            for e in entities[:15]:
                print(f"  [{e['type']:12s}] {e['name']} (salience: {e['salience']:.3f})")

        categories = result.get("categories", [])
        if categories:
            print(f"\nContent Categories:")
            for c in categories:
                print(f"  {c['name']} ({c['confidence']:.1%})")

        moderation = result.get("moderation", [])
        if moderation:
            print(f"\nModeration Flags:")
            for m in moderation:
                print(f"  {m['name']} ({m['confidence']:.1%})")


if __name__ == "__main__":
    main()