Add enhanced analysis with selective field analysis and category proposer
New Features: - Selective field analysis: Choose which fields to analyze (title, meta_description, categories, site) - In-place CSV updates: Update input CSV with new columns (automatic backup created) - Category proposer: Dedicated command for AI-powered category suggestions New Commands: - seo analyze -f title categories: Analyze specific fields only - seo analyze -u: Update input CSV with recommendations - seo category_propose: Propose categories based on content New Scripts: - enhanced_analyzer.py: Enhanced AI analyzer with selective analysis - category_proposer.py: Dedicated category proposal tool CLI Options: - --fields, -f: Specify fields to analyze - --update, -u: Update input CSV (creates backup) - --output, -o: Custom output file path Output Columns: - proposed_title, title_reason (for title analysis) - proposed_meta_description, meta_reason (for meta analysis) - proposed_category, category_reason (for category analysis) - proposed_site, site_reason (for site analysis) - ai_confidence, ai_priority (common to all) Documentation: - ENHANCED_ANALYSIS_GUIDE.md: Complete guide with examples Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
This commit is contained in:
Kevin Bataille
239
scripts/category_proposer.py
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239
scripts/category_proposer.py
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@@ -0,0 +1,239 @@
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#!/usr/bin/env python3
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"""
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Category Proposer - AI-powered category suggestions
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Analyzes posts and proposes optimal categories based on content.
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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
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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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logger = logging.getLogger(__name__)
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class CategoryProposer:
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"""Propose categories for posts using AI."""
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def __init__(self, csv_file: str):
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"""Initialize proposer 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.ai_model = Config.AI_MODEL
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self.posts = []
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self.proposed_categories = []
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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."""
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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")
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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 get_category_proposals(self, batch: List[Dict]) -> Optional[str]:
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"""Get AI category proposals 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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# Format posts for AI
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formatted = []
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for i, post in enumerate(batch, 1):
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text = f"{i}. ID: {post['post_id']}\n"
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text += f" Title: {post.get('title', '')}\n"
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text += f" Current Categories: {post.get('categories', '')}\n"
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if 'content_preview' in post:
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text += f" Content: {post['content_preview'][:300]}...\n"
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formatted.append(text)
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posts_text = "\n".join(formatted)
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prompt = f"""Analyze these blog posts and propose optimal categories.
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{posts_text}
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For EACH post, provide:
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{{
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"post_id": <id>,
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"current_categories": "<current>",
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"proposed_category": "<best category>",
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"alternative_categories": ["<alt1>", "<alt2>"],
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"reason": "<brief explanation>",
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"confidence": "<High|Medium|Low>"
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}}
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Return ONLY a JSON array with one object per post."""
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try:
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logger.info(f" Getting category proposals...")
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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": self.ai_model,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.3,
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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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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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logger.info(f" ✓ Got proposals (tokens: {input_tokens}+{output_tokens})")
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return result['choices'][0]['message']['content'].strip()
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except Exception as e:
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logger.error(f"Error getting proposals: {e}")
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return None
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def parse_proposals(self, proposals_json: str) -> List[Dict]:
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"""Parse JSON proposals."""
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try:
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start_idx = proposals_json.find('[')
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end_idx = proposals_json.rfind(']') + 1
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if start_idx == -1 or end_idx == 0:
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return []
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return json.loads(proposals_json[start_idx:end_idx])
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except json.JSONDecodeError:
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return []
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def propose_categories(self, batch_size: int = 10) -> bool:
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"""Propose categories for all posts."""
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logger.info("\n" + "="*70)
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logger.info("PROPOSING CATEGORIES WITH AI")
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logger.info("="*70 + "\n")
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batches = [self.posts[i:i + batch_size] for i in range(0, len(self.posts), batch_size)]
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logger.info(f"Processing {len(self.posts)} posts in {len(batches)} batches...\n")
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all_proposals = {}
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for batch_num, batch in enumerate(batches, 1):
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logger.info(f"Batch {batch_num}/{len(batches)}...")
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proposals_json = self.get_category_proposals(batch)
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if not proposals_json:
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continue
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proposals = self.parse_proposals(proposals_json)
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for prop in proposals:
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all_proposals[str(prop.get('post_id', ''))] = prop
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logger.info(f" ✓ Got {len(proposals)} proposals")
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logger.info(f"\n✓ Proposals complete!")
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logger.info(f" Total: {len(all_proposals)}")
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logger.info(f" API calls: {self.api_calls}")
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logger.info(f" Cost: ${self.ai_cost:.4f}")
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# Map proposals 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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proposal = all_proposals.get(post_id, {})
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self.proposed_categories.append({
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**post,
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'proposed_category': proposal.get('proposed_category', post.get('categories', '')),
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'alternative_categories': ', '.join(proposal.get('alternative_categories', [])),
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'category_reason': proposal.get('reason', ''),
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'category_confidence': proposal.get('confidence', 'Medium'),
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'current_categories': post.get('categories', '')
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})
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return True
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def export_proposals(self, output_file: Optional[str] = None) -> str:
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"""Export category proposals to CSV."""
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if not output_file:
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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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output_file = output_dir / f'category_proposals_{timestamp}.csv'
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output_file = Path(output_file)
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output_file.parent.mkdir(parents=True, exist_ok=True)
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fieldnames = [
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'post_id', 'title', 'site', 'current_categories',
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'proposed_category', 'alternative_categories',
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'category_reason', 'category_confidence'
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]
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logger.info(f"\nExporting to: {output_file}")
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with open(output_file, 'w', newline='', encoding='utf-8') as f:
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writer = csv.DictWriter(f, fieldnames=fieldnames, extrasaction='ignore')
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writer.writeheader()
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writer.writerows(self.proposed_categories)
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logger.info(f"✓ Exported {len(self.proposed_categories)} proposals")
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return str(output_file)
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def run(self, output_file: Optional[str] = None, batch_size: int = 10) -> str:
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"""Run complete category proposal process."""
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if not self.load_csv():
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sys.exit(1)
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if not self.propose_categories(batch_size=batch_size):
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logger.error("Failed to propose categories")
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sys.exit(1)
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return self.export_proposals(output_file)
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def main():
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"""Main entry point."""
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import argparse
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parser = argparse.ArgumentParser(
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description='AI-powered category proposer for blog posts'
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)
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parser.add_argument('csv_file', help='Input CSV file with posts')
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parser.add_argument('--output', '-o', help='Output CSV file')
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parser.add_argument('--batch-size', type=int, default=10, help='Batch size')
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args = parser.parse_args()
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proposer = CategoryProposer(args.csv_file)
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output_file = proposer.run(batch_size=args.batch_size)
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logger.info(f"\n✓ Category proposals saved to: {output_file}")
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if __name__ == '__main__':
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main()
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375
scripts/enhanced_analyzer.py
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375
scripts/enhanced_analyzer.py
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#!/usr/bin/env python3
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"""
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Enhanced AI Analyzer - Selective analysis with in-place updates
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Analyzes posts and updates CSV with AI recommendations for:
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- Title optimization
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- Meta description optimization
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- Category suggestions
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- Site placement recommendations
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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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logger = logging.getLogger(__name__)
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class EnhancedPostAnalyzer:
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"""Enhanced analyzer with selective column analysis and in-place updates."""
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def __init__(self, csv_file: str, analyze_fields: Optional[List[str]] = None):
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"""
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Initialize analyzer.
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Args:
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csv_file: Path to input CSV
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analyze_fields: List of fields to analyze ['title', 'meta_description', 'categories', 'site']
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If None, analyzes all fields
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"""
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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.ai_model = Config.AI_MODEL
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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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# Default: analyze all fields
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if analyze_fields is None:
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self.analyze_fields = ['title', 'meta_description', 'categories', 'site']
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else:
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self.analyze_fields = analyze_fields
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logger.info(f"Fields to analyze: {', '.join(self.analyze_fields)}")
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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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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 get_ai_recommendations(self, batch: List[Dict], fields: List[str]) -> Optional[str]:
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"""Get AI recommendations for specific fields."""
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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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# Format posts for AI
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formatted_posts = []
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for i, post in enumerate(batch, 1):
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post_text = f"{i}. POST ID: {post['post_id']}\n"
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post_text += f" Site: {post.get('site', '')}\n"
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if 'title' in fields:
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post_text += f" Title: {post.get('title', '')}\n"
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if 'meta_description' in fields:
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post_text += f" Meta Description: {post.get('meta_description', '')}\n"
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if 'categories' in fields:
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post_text += f" Categories: {post.get('categories', '')}\n"
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if 'content_preview' in post:
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post_text += f" Content Preview: {post.get('content_preview', '')[:300]}...\n"
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formatted_posts.append(post_text)
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posts_text = "\n".join(formatted_posts)
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# Build prompt based on requested fields
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prompt_parts = ["Analyze these blog posts and provide recommendations.\n\n"]
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if 'site' in fields:
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prompt_parts.append("""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
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- hellogeek.net: Low-traffic, experimental, off-brand content
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""")
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prompt_parts.append(posts_text)
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prompt_parts.append("\nFor EACH post, provide a JSON object with:\n{\n")
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if 'title' in fields:
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prompt_parts.append(' "proposed_title": "<Improved SEO title>",\n')
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prompt_parts.append(' "title_reason": "<Reason for title change>",\n')
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if 'meta_description' in fields:
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prompt_parts.append(' "proposed_meta_description": "<Improved meta description (120-160 chars)>",\n')
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prompt_parts.append(' "meta_reason": "<Reason for meta description change>",\n')
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if 'categories' in fields:
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prompt_parts.append(' "proposed_category": "<Best category>",\n')
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prompt_parts.append(' "category_reason": "<Reason for category change>",\n')
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if 'site' in fields:
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prompt_parts.append(' "proposed_site": "<Best site for this post>",\n')
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prompt_parts.append(' "site_reason": "<Reason for site recommendation>",\n')
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prompt_parts.append(' "confidence": "<High|Medium|Low>",\n')
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prompt_parts.append(' "priority": "<High|Medium|Low>"\n}')
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prompt_parts.append("\nReturn ONLY a JSON array of objects, one per post.")
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prompt = "".join(prompt_parts)
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try:
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logger.info(f" Sending batch to AI 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": self.ai_model,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.3,
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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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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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return []
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def analyze_posts(self, batch_size: int = 10) -> 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.posts[i:i + batch_size] for i in range(0, len(self.posts), batch_size)]
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logger.info(f"Processing {len(self.posts)} posts in {len(batches)} batches...\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, self.analyze_fields)
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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)}")
|
||||
logger.info(f" API calls: {self.api_calls}")
|
||||
logger.info(f" Estimated cost: ${self.ai_cost:.4f}")
|
||||
|
||||
# Map recommendations to posts
|
||||
for post in self.posts:
|
||||
post_id = str(post['post_id'])
|
||||
if post_id in all_recommendations:
|
||||
rec = all_recommendations[post_id]
|
||||
|
||||
# Add only requested fields
|
||||
if 'title' in self.analyze_fields:
|
||||
post['proposed_title'] = rec.get('proposed_title', post.get('title', ''))
|
||||
post['title_reason'] = rec.get('title_reason', '')
|
||||
|
||||
if 'meta_description' in self.analyze_fields:
|
||||
post['proposed_meta_description'] = rec.get('proposed_meta_description', post.get('meta_description', ''))
|
||||
post['meta_reason'] = rec.get('meta_reason', '')
|
||||
|
||||
if 'categories' in self.analyze_fields:
|
||||
post['proposed_category'] = rec.get('proposed_category', post.get('categories', ''))
|
||||
post['category_reason'] = rec.get('category_reason', '')
|
||||
|
||||
if 'site' in self.analyze_fields:
|
||||
post['proposed_site'] = rec.get('proposed_site', post.get('site', ''))
|
||||
post['site_reason'] = rec.get('site_reason', '')
|
||||
|
||||
# Common fields
|
||||
post['ai_confidence'] = rec.get('confidence', 'Medium')
|
||||
post['ai_priority'] = rec.get('priority', 'Medium')
|
||||
else:
|
||||
# Add empty fields for consistency
|
||||
if 'title' in self.analyze_fields:
|
||||
post['proposed_title'] = post.get('title', '')
|
||||
post['title_reason'] = 'No AI recommendation'
|
||||
|
||||
if 'meta_description' in self.analyze_fields:
|
||||
post['proposed_meta_description'] = post.get('meta_description', '')
|
||||
post['meta_reason'] = 'No AI recommendation'
|
||||
|
||||
if 'categories' in self.analyze_fields:
|
||||
post['proposed_category'] = post.get('categories', '')
|
||||
post['category_reason'] = 'No AI recommendation'
|
||||
|
||||
if 'site' in self.analyze_fields:
|
||||
post['proposed_site'] = post.get('site', '')
|
||||
post['site_reason'] = 'No AI recommendation'
|
||||
|
||||
post['ai_confidence'] = 'Unknown'
|
||||
post['ai_priority'] = 'Medium'
|
||||
|
||||
self.analyzed_posts.append(post)
|
||||
|
||||
return len(self.analyzed_posts) > 0
|
||||
|
||||
def export_results(self, output_file: Optional[str] = None, update_input: bool = False) -> str:
|
||||
"""
|
||||
Export results to CSV.
|
||||
|
||||
Args:
|
||||
output_file: Custom output path
|
||||
update_input: If True, update the input CSV file (creates backup)
|
||||
|
||||
Returns:
|
||||
Path to exported file
|
||||
"""
|
||||
if update_input:
|
||||
# Create backup of original file
|
||||
backup_file = self.csv_file.parent / f"{self.csv_file.stem}_backup_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
|
||||
import shutil
|
||||
shutil.copy2(self.csv_file, backup_file)
|
||||
logger.info(f"✓ Created backup: {backup_file}")
|
||||
|
||||
output_file = self.csv_file
|
||||
elif not output_file:
|
||||
output_dir = Path(__file__).parent.parent / 'output'
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
output_file = output_dir / f'analyzed_posts_{timestamp}.csv'
|
||||
|
||||
output_file = Path(output_file)
|
||||
output_file.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
if not self.analyzed_posts:
|
||||
logger.error("No analyzed posts to export")
|
||||
return ""
|
||||
|
||||
# Build fieldnames - original fields + new fields
|
||||
original_fields = list(self.analyzed_posts[0].keys())
|
||||
|
||||
# Determine which new fields were added
|
||||
new_fields = []
|
||||
if 'title' in self.analyze_fields:
|
||||
new_fields.extend(['proposed_title', 'title_reason'])
|
||||
if 'meta_description' in self.analyze_fields:
|
||||
new_fields.extend(['proposed_meta_description', 'meta_reason'])
|
||||
if 'categories' in self.analyze_fields:
|
||||
new_fields.extend(['proposed_category', 'category_reason'])
|
||||
if 'site' in self.analyze_fields:
|
||||
new_fields.extend(['proposed_site', 'site_reason'])
|
||||
|
||||
new_fields.extend(['ai_confidence', 'ai_priority'])
|
||||
|
||||
fieldnames = original_fields + new_fields
|
||||
|
||||
logger.info(f"\nExporting results to: {output_file}")
|
||||
|
||||
with open(output_file, 'w', newline='', encoding='utf-8') as f:
|
||||
writer = csv.DictWriter(f, fieldnames=fieldnames)
|
||||
writer.writeheader()
|
||||
writer.writerows(self.analyzed_posts)
|
||||
|
||||
logger.info(f"✓ Exported {len(self.analyzed_posts)} posts")
|
||||
return str(output_file)
|
||||
|
||||
def run(self, output_file: Optional[str] = None, update_input: bool = False, batch_size: int = 10) -> str:
|
||||
"""Run complete analysis."""
|
||||
if not self.load_csv():
|
||||
sys.exit(1)
|
||||
|
||||
if not self.analyze_posts(batch_size=batch_size):
|
||||
logger.error("Failed to analyze posts")
|
||||
sys.exit(1)
|
||||
|
||||
return self.export_results(output_file=output_file, update_input=update_input)
|
||||
|
||||
|
||||
def main():
|
||||
"""Main entry point with argument parsing."""
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description='Enhanced AI analyzer with selective field analysis'
|
||||
)
|
||||
parser.add_argument('csv_file', help='Input CSV file')
|
||||
parser.add_argument('--output', '-o', help='Output CSV file (default: creates new file in output/)')
|
||||
parser.add_argument('--update', '-u', action='store_true', help='Update input CSV file (creates backup)')
|
||||
parser.add_argument('--fields', '-f', nargs='+',
|
||||
choices=['title', 'meta_description', 'categories', 'site'],
|
||||
help='Fields to analyze (default: all fields)')
|
||||
parser.add_argument('--batch-size', type=int, default=10, help='Batch size for AI analysis')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
analyzer = EnhancedPostAnalyzer(args.csv_file, analyze_fields=args.fields)
|
||||
output_file = analyzer.run(
|
||||
output_file=args.output,
|
||||
update_input=args.update,
|
||||
batch_size=args.batch_size
|
||||
)
|
||||
|
||||
logger.info(f"\n✓ Analysis complete! Results saved to: {output_file}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user