#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Gemini 地下室方案 #1 替代版: 用 GitHub Models (免费) 替代 Google Cloud NLP 做实体显著性检测
GitHub Models 免费模型: gpt-4o-mini, Phi-4, Llama-3.3-70B 等
用法: python nlp_content_audit.py article.html --targets "Gold" "XAUUSD" "position sizing"
"""
import sys, io, os, re, json, urllib.request
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')

# GitHub Models free endpoint (via GitHub Marketplace)
# 优先读环境变量，再读项目 .env config
sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..'))
from config import GITHUB_TOKEN as _CFG_TOKEN
GITHUB_TOKEN = os.environ.get('GITHUB_TOKEN') or _CFG_TOKEN or ''
if not GITHUB_TOKEN:
    print("❌ 未找到 GITHUB_TOKEN。请在 .env 中设置或设置环境变量")
    sys.exit(1)

MODEL = 'gpt-4o-mini'  # Free tier on GitHub Models
ENDPOINT = 'https://models.inference.ai.azure.com/chat/completions'

def analyze_entities_with_llm(text, target_entities=None):
    """用 LLM 做实体显著性分析 — 替代 Google Cloud NLP"""

    prompt = f"""You are a search engine content quality auditor. Analyze this trading/finance article and:

1. Extract the TOP 10 entities (people, concepts, instruments, strategies) mentioned.
2. Rate each entity's SALIENCE (prominence) from 0.0 to 1.0 based on:
   - 1.0 = THE main topic, appears in title and first sentence, discussed throughout
   - 0.5 = Mentioned several times but not central
   - 0.1 = Passing mention only
3. If the first paragraph doesn't directly state the main topic within 15 words, flag it.
4. Score overall content focus: A (laser-focused), B (mostly focused), C (scattered), D (all over the place).

Text to analyze:
---
{text[:3000]}
---

Respond in JSON format:
{{"entities": [{{"name": "...", "salience": 0.X, "type": "..."}}], "focus_grade": "A/B/C/D", "first_paragraph_issue": true/false, "issues": ["..."]}}"""

    payload = json.dumps({
        "model": MODEL,
        "messages": [{"role": "user", "content": prompt}],
        "temperature": 0.1,
        "max_tokens": 1000
    })

    req = urllib.request.Request(ENDPOINT, data=payload.encode(),
        headers={
            'Content-Type': 'application/json',
            'Authorization': f'Bearer {GITHUB_TOKEN}',
            'User-Agent': 'GFIL-NLP-Audit/1.0'
        })

    try:
        with urllib.request.urlopen(req, timeout=30) as resp:
            result = json.loads(resp.read().decode())
            content = result['choices'][0]['message']['content']
            # Extract JSON from response
            json_match = re.search(r'\{[\s\S]*\}', content)
            if json_match:
                return json.loads(json_match.group())
            return {"error": "Could not parse LLM response", "raw": content[:500]}
    except Exception as e:
        return {"error": str(e)}

def audit_html_file(filepath, target_entities=None):
    """分析 HTML 文件文本"""
    with open(filepath, 'r', encoding='utf-8') as f:
        html = f.read()

    title = re.search(r'<title>(.*?)</title>', html)
    h1 = re.search(r'<h1>(.*?)</h1>', html)
    body = re.sub(r'<[^>]+>', ' ', html)
    body = re.sub(r'\s+', ' ', body).strip()

    title_text = title.group(1) if title else ''
    h1_text = h1.group(1) if h1 else ''

    print(f"文件: {os.path.basename(filepath)}")
    print(f"标题: {title_text[:100]}")
    print(f"H1: {h1_text[:100]}")
    print(f"分析中...")

    text = f"{title_text}\n{h1_text}\n{body[:2500]}"
    result = analyze_entities_with_llm(text, target_entities)

    if 'error' in result:
        print(f"\n❌ 分析失败: {result['error']}")
        return

    print(f"\n=== 内容质量审计 ===")
    print(f"集中度评分: {result.get('focus_grade', 'N/A')}")
    if result.get('first_paragraph_issue'):
        print(f"⚠️ 第一段问题: 主题不够突出（前15字未点题）")

    print(f"\n=== 实体显著性 Top 10 ===")
    print(f"{'实体':<30} {'显著性':>8} {'类型':<15}")
    print("-" * 60)
    for e in result.get('entities', [])[:10]:
        bar = '█' * int(e['salience'] * 50)
        print(f"{e['name']:<30} {e['salience']:.3f}  {e.get('type', ''):<15}")

    if target_entities:
        print(f"\n=== 目标实体检测 ===")
        entity_names = {e['name'].lower(): e for e in result.get('entities', [])}
        for t in target_entities:
            found = None
            for name, e in entity_names.items():
                if t.lower() in name:
                    found = e
                    break
            if found:
                ok = '✅ 达标' if found['salience'] >= 0.8 else '❌ 不够(需≥0.8)'
                print(f"  {t}: 显著度 {found['salience']:.3f} {ok}")
            else:
                print(f"  {t}: ❌ 未找到! 需在标题或第一段加入")

    if result.get('issues'):
        print(f"\n=== 发现的问题 ===")
        for issue in result['issues']:
            print(f"  - {issue}")

if __name__ == '__main__':
    import argparse
    p = argparse.ArgumentParser(description='GitHub Models 免费实体审计 (替代Google NLP)')
    p.add_argument('file', help='HTML 文件路径')
    p.add_argument('--targets', nargs='+', help='目标实体: --targets Gold XAUUSD "position sizing"')
    args = p.parse_args()

    if '1234567890' in GITHUB_TOKEN:
        print("❌ 请设置 GITHUB_TOKEN 环境变量 (你的 GitHub Personal Access Token)")
        print("   $env:GITHUB_TOKEN='github_pat_xxxx'  # PowerShell")
        sys.exit(1)

    audit_html_file(args.file, args.targets)