Refactor SEO automation into unified CLI application
Major refactoring to create a clean, integrated CLI application: ### New Features: - Unified CLI executable (./seo) with simple command structure - All commands accept optional CSV file arguments - Auto-detection of latest files when no arguments provided - Simplified output directory structure (output/ instead of output/reports/) - Cleaner export filename format (all_posts_YYYY-MM-DD.csv) ### Commands: - export: Export all posts from WordPress sites - analyze [csv]: Analyze posts with AI (optional CSV input) - recategorize [csv]: Recategorize posts with AI - seo_check: Check SEO quality - categories: Manage categories across sites - approve [files]: Review and approve recommendations - full_pipeline: Run complete workflow - analytics, gaps, opportunities, report, status ### Changes: - Moved all scripts to scripts/ directory - Created config.yaml for configuration - Updated all scripts to use output/ directory - Deprecated old seo-cli.py in favor of new ./seo - Added AGENTS.md and CHANGELOG.md documentation - Consolidated README.md with updated usage ### Technical: - Added PyYAML dependency - Removed hardcoded configuration values - All scripts now properly integrated - Better error handling and user feedback Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
This commit is contained in:
Kevin Bataille
453
scripts/ai_analyze_posts_for_decisions.py
Executable file
453
scripts/ai_analyze_posts_for_decisions.py
Executable file
@@ -0,0 +1,453 @@
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#!/usr/bin/env python3
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"""
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AI-Powered Post Analysis and Recommendation Script
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Analyzes exported posts CSV using Claude via OpenRouter and provides
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clear, automation-friendly recommendations for:
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- Which site to move posts to
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- Categories to set
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- Posts to consolidate
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- Posts to delete
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- Posts to optimize
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"""
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import csv
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import json
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import logging
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import sys
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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import requests
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from datetime import datetime
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from config import Config
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# Setup logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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class PostAnalyzer:
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"""Analyze posts CSV using Claude AI via OpenRouter."""
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def __init__(self, csv_file: str):
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"""Initialize analyzer with CSV file."""
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self.csv_file = Path(csv_file)
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self.openrouter_api_key = Config.OPENROUTER_API_KEY
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self.posts = []
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self.analyzed_posts = []
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self.api_calls = 0
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self.ai_cost = 0.0
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def load_csv(self) -> bool:
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"""Load posts from CSV file."""
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logger.info(f"Loading CSV: {self.csv_file}")
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if not self.csv_file.exists():
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logger.error(f"CSV file not found: {self.csv_file}")
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return False
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try:
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with open(self.csv_file, 'r', encoding='utf-8') as f:
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reader = csv.DictReader(f)
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self.posts = list(reader)
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logger.info(f"✓ Loaded {len(self.posts)} posts from CSV")
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# Group by site for stats
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by_site = {}
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for post in self.posts:
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site = post.get('site', '')
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if site not in by_site:
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by_site[site] = 0
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by_site[site] += 1
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for site, count in by_site.items():
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logger.info(f" {site}: {count} posts")
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return True
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except Exception as e:
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logger.error(f"Error loading CSV: {e}")
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return False
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def batch_posts_for_analysis(self, batch_size: int = 10) -> List[List[Dict]]:
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"""Batch posts for AI analysis to manage token usage."""
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batches = []
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for i in range(0, len(self.posts), batch_size):
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batches.append(self.posts[i:i + batch_size])
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return batches
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def format_batch_for_ai(self, batch: List[Dict]) -> str:
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"""Format batch of posts for AI analysis."""
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formatted = "POSTS TO ANALYZE:\n\n"
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for i, post in enumerate(batch, 1):
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formatted += f"{i}. POST ID: {post['post_id']}\n"
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formatted += f" Site: {post['site']}\n"
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formatted += f" Title: {post['title']}\n"
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formatted += f" Status: {post['status']}\n"
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formatted += f" Word Count: {post['word_count']}\n"
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formatted += f" Content: {post['content_preview']}\n"
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formatted += f" Current Categories: {post['categories']}\n"
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formatted += f" Meta Description: {post['meta_description']}\n"
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formatted += "\n"
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return formatted
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def get_ai_recommendations(self, batch: List[Dict]) -> Optional[str]:
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"""Get AI recommendations for a batch of posts."""
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if not self.openrouter_api_key:
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logger.error("OPENROUTER_API_KEY not set")
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return None
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batch_text = self.format_batch_for_ai(batch)
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prompt = f"""Analyze these blog posts and provide clear, actionable recommendations.
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Website Strategy:
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- mistergeek.net: High-value topics (VPN, Software, Gaming, General Tech, SEO, Content Marketing)
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- webscroll.fr: Torrenting, File-Sharing, Tracker guides (niche audience)
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- hellogeek.net: Low-traffic, experimental, off-brand, or niche content
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{batch_text}
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For EACH post, provide a JSON object with:
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{{
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"post_id": <id>,
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"decision": "<ACTION>" where ACTION is ONE of:
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- "Keep on mistergeek.net" (high-value, high-traffic)
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- "Move to webscroll.fr" (torrenting/file-sharing content)
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- "Move to hellogeek.net" (low-traffic or off-brand)
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- "Delete" (spam, extremely low quality, zero traffic)
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- "Consolidate with post_id:<id>" (similar content, duplicate)
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"category": "<CATEGORY>" where category is ONE of:
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- "VPN"
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- "Software/Tools"
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- "Gaming"
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- "Streaming"
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- "Torrenting"
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- "File-Sharing"
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- "SEO"
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- "Content Marketing"
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- "Other"
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"reason": "<Brief reason for decision>",
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"priority": "<High|Medium|Low>",
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"notes": "<Any additional notes>"
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}}
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Return ONLY a JSON array. Example:
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[
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{{"post_id": 2845, "decision": "Keep on mistergeek.net", "category": "VPN", "reason": "High traffic, core topic", "priority": "High", "notes": "Already optimized"}},
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{{"post_id": 1234, "decision": "Move to webscroll.fr", "category": "Torrenting", "reason": "Torrent tracker content", "priority": "Medium", "notes": "Good SEO potential on target site"}}
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]
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Analyze all posts and provide recommendations for EVERY post in the batch."""
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try:
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logger.info(f" Sending batch to Claude for analysis...")
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response = requests.post(
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"https://openrouter.ai/api/v1/chat/completions",
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headers={
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"Authorization": f"Bearer {self.openrouter_api_key}",
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"Content-Type": "application/json",
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},
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json={
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"model": "anthropic/claude-3.5-sonnet",
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"messages": [
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{"role": "user", "content": prompt}
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],
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"temperature": 0.3, # Lower temp for more consistent recommendations
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},
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timeout=60
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)
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response.raise_for_status()
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result = response.json()
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self.api_calls += 1
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# Track cost
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usage = result.get('usage', {})
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input_tokens = usage.get('prompt_tokens', 0)
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output_tokens = usage.get('completion_tokens', 0)
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self.ai_cost += (input_tokens * 3 + output_tokens * 15) / 1_000_000
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recommendations_text = result['choices'][0]['message']['content'].strip()
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logger.info(f" ✓ Got recommendations (tokens: {input_tokens}+{output_tokens})")
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return recommendations_text
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except Exception as e:
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logger.error(f"Error getting AI recommendations: {e}")
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return None
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def parse_recommendations(self, recommendations_json: str) -> List[Dict]:
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"""Parse JSON recommendations from AI."""
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try:
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# Try to extract JSON from response
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start_idx = recommendations_json.find('[')
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end_idx = recommendations_json.rfind(']') + 1
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if start_idx == -1 or end_idx == 0:
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logger.error("Could not find JSON array in response")
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return []
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json_str = recommendations_json[start_idx:end_idx]
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recommendations = json.loads(json_str)
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return recommendations
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except json.JSONDecodeError as e:
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logger.error(f"Error parsing JSON recommendations: {e}")
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logger.debug(f"Response was: {recommendations_json[:500]}")
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return []
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def analyze_all_posts(self) -> bool:
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"""Analyze all posts in batches."""
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logger.info("\n" + "="*70)
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logger.info("ANALYZING POSTS WITH AI")
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logger.info("="*70 + "\n")
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batches = self.batch_posts_for_analysis(batch_size=10)
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logger.info(f"Processing {len(self.posts)} posts in {len(batches)} batches of 10...\n")
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all_recommendations = {}
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for batch_num, batch in enumerate(batches, 1):
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logger.info(f"Batch {batch_num}/{len(batches)}: Analyzing {len(batch)} posts...")
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recommendations_json = self.get_ai_recommendations(batch)
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if not recommendations_json:
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logger.error(f" Failed to get recommendations for batch {batch_num}")
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continue
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recommendations = self.parse_recommendations(recommendations_json)
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for rec in recommendations:
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all_recommendations[str(rec.get('post_id', ''))] = rec
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logger.info(f" ✓ Got {len(recommendations)} recommendations")
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logger.info(f"\n✓ Analysis complete!")
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logger.info(f" Total recommendations: {len(all_recommendations)}")
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logger.info(f" API calls: {self.api_calls}")
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logger.info(f" Estimated cost: ${self.ai_cost:.4f}")
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# Map recommendations to posts
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for post in self.posts:
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post_id = str(post['post_id'])
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if post_id in all_recommendations:
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rec = all_recommendations[post_id]
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post['decision'] = rec.get('decision', 'No decision')
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post['recommended_category'] = rec.get('category', 'Other')
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post['reason'] = rec.get('reason', '')
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post['priority'] = rec.get('priority', 'Medium')
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post['ai_notes'] = rec.get('notes', '')
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else:
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post['decision'] = 'Pending'
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post['recommended_category'] = 'Other'
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post['reason'] = 'No recommendation'
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post['priority'] = 'Medium'
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post['ai_notes'] = ''
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self.analyzed_posts.append(post)
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return len(self.analyzed_posts) > 0
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def export_with_recommendations(self) -> Tuple[str, str, str, str]:
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"""Export CSV with recommendations and create action-specific files."""
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output_dir = Path(__file__).parent.parent / 'output'
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output_dir.mkdir(parents=True, exist_ok=True)
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timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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# Main file with all recommendations
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main_file = output_dir / f'posts_with_ai_recommendations_{timestamp}.csv'
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# Action-specific files
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moves_file = output_dir / f'posts_to_move_{timestamp}.csv'
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consolidate_file = output_dir / f'posts_to_consolidate_{timestamp}.csv'
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delete_file = output_dir / f'posts_to_delete_{timestamp}.csv'
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# Export main file
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fieldnames = list(self.analyzed_posts[0].keys()) + [
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'decision',
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'recommended_category',
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'reason',
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'priority',
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'ai_notes'
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]
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logger.info(f"\nExporting recommendations to CSV...")
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with open(main_file, 'w', newline='', encoding='utf-8') as f:
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writer = csv.DictWriter(f, fieldnames=fieldnames)
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writer.writeheader()
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writer.writerows(self.analyzed_posts)
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logger.info(f"✓ Main file: {main_file}")
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# Export action-specific files
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posts_to_move = [p for p in self.analyzed_posts if 'Move to' in p.get('decision', '')]
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posts_to_consolidate = [p for p in self.analyzed_posts if 'Consolidate' in p.get('decision', '')]
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posts_to_delete = [p for p in self.analyzed_posts if p.get('decision') == 'Delete']
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# Moves file
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if posts_to_move:
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with open(moves_file, 'w', newline='', encoding='utf-8') as f:
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writer = csv.DictWriter(f, fieldnames=fieldnames)
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writer.writeheader()
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writer.writerows(posts_to_move)
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logger.info(f"✓ Moves file ({len(posts_to_move)} posts): {moves_file}")
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# Consolidate file
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if posts_to_consolidate:
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with open(consolidate_file, 'w', newline='', encoding='utf-8') as f:
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writer = csv.DictWriter(f, fieldnames=fieldnames)
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writer.writeheader()
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writer.writerows(posts_to_consolidate)
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logger.info(f"✓ Consolidate file ({len(posts_to_consolidate)} posts): {consolidate_file}")
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# Delete file
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if posts_to_delete:
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with open(delete_file, 'w', newline='', encoding='utf-8') as f:
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writer = csv.DictWriter(f, fieldnames=fieldnames)
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writer.writeheader()
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writer.writerows(posts_to_delete)
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logger.info(f"✓ Delete file ({len(posts_to_delete)} posts): {delete_file}")
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return (
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str(main_file),
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str(moves_file) if posts_to_move else None,
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str(consolidate_file) if posts_to_consolidate else None,
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str(delete_file) if posts_to_delete else None
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)
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def print_summary(self):
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"""Print analysis summary."""
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logger.info("\n" + "="*70)
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logger.info("ANALYSIS SUMMARY")
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logger.info("="*70 + "\n")
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# Count decisions
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decisions = {}
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for post in self.analyzed_posts:
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decision = post.get('decision', 'Unknown')
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decisions[decision] = decisions.get(decision, 0) + 1
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logger.info("DECISIONS:")
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for decision, count in sorted(decisions.items(), key=lambda x: x[1], reverse=True):
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logger.info(f" {decision}: {count} posts")
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# Count categories
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categories = {}
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for post in self.analyzed_posts:
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cat = post.get('recommended_category', 'Other')
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categories[cat] = categories.get(cat, 0) + 1
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logger.info("\nRECOMMENDED CATEGORIES:")
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for cat, count in sorted(categories.items(), key=lambda x: x[1], reverse=True):
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logger.info(f" {cat}: {count} posts")
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# Count priorities
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priorities = {}
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for post in self.analyzed_posts:
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priority = post.get('priority', 'Unknown')
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priorities[priority] = priorities.get(priority, 0) + 1
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logger.info("\nPRIORITY BREAKDOWN:")
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for priority in ['High', 'Medium', 'Low']:
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count = priorities.get(priority, 0)
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logger.info(f" {priority}: {count} posts")
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# By site
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logger.info("\nBY SITE:")
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by_site = {}
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for post in self.analyzed_posts:
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site = post.get('site', 'Unknown')
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if site not in by_site:
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by_site[site] = []
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by_site[site].append(post.get('decision', 'Unknown'))
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for site in sorted(by_site.keys()):
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logger.info(f"\n {site}:")
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decisions_for_site = {}
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for decision in by_site[site]:
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decisions_for_site[decision] = decisions_for_site.get(decision, 0) + 1
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||||
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for decision, count in sorted(decisions_for_site.items()):
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logger.info(f" {decision}: {count}")
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def run(self):
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"""Run complete analysis."""
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logger.info("="*70)
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logger.info("AI-POWERED POST ANALYSIS AND RECOMMENDATIONS")
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||||
logger.info("="*70)
|
||||
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||||
# Load CSV
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if not self.load_csv():
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sys.exit(1)
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||||
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||||
# Analyze posts
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||||
if not self.analyze_all_posts():
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||||
logger.error("Failed to analyze posts")
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||||
sys.exit(1)
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||||
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||||
# Print summary
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||||
self.print_summary()
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||||
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||||
# Export results
|
||||
logger.info("\n" + "="*70)
|
||||
logger.info("EXPORTING RESULTS")
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||||
logger.info("="*70)
|
||||
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||||
main_file, moves_file, consol_file, delete_file = self.export_with_recommendations()
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||||
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||||
logger.info("\n" + "="*70)
|
||||
logger.info("NEXT STEPS")
|
||||
logger.info("="*70)
|
||||
logger.info("\n1. Review main file with all recommendations:")
|
||||
logger.info(f" {main_file}")
|
||||
logger.info("\n2. Execute moves (automate with script):")
|
||||
if moves_file:
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||||
logger.info(f" {moves_file}")
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||||
else:
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||||
logger.info(" No posts to move")
|
||||
|
||||
logger.info("\n3. Consolidate duplicates:")
|
||||
if consol_file:
|
||||
logger.info(f" {consol_file}")
|
||||
else:
|
||||
logger.info(" No posts to consolidate")
|
||||
|
||||
logger.info("\n4. Delete low-quality posts:")
|
||||
if delete_file:
|
||||
logger.info(f" {delete_file}")
|
||||
else:
|
||||
logger.info(" No posts to delete")
|
||||
|
||||
logger.info("\n✓ Analysis complete!")
|
||||
|
||||
|
||||
def main():
|
||||
"""Main entry point."""
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description='Analyze exported posts CSV using Claude AI and provide recommendations'
|
||||
)
|
||||
parser.add_argument(
|
||||
'csv_file',
|
||||
help='Path to exported posts CSV file'
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
analyzer = PostAnalyzer(args.csv_file)
|
||||
analyzer.run()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user