Kunthawat Greethong
b26c8199a5
Changes: - Add FAL_KEY and GEMINI_API_KEY to .env.example - Update picture-it to use ~/.config/opencode/.env (unified creds) - Remove shodh-memory skill (no longer used) - Remove alphaear-* skills (deprecated) - Remove thai-frontend-dev skill (replaced by website-creator) - Remove theme-factory skill - Add mql-developer skill (MQL5 trading) - Add ecommerce-astro skill (Astro e-commerce) - Add website-creator skill (Next.js + Payload CMS) - Update install script for new skills
335 lines
12 KiB
Python
335 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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Thai Readability Analyzer
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Analyze Thai text readability with PyThaiNLP integration.
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Detects formality level, grade level, and sentence structure.
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"""
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import argparse
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import json
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import re
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from typing import Dict, List
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try:
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from pythainlp import word_tokenize, sent_tokenize
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THAI_SUPPORT = True
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except ImportError:
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THAI_SUPPORT = False
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print("Warning: PyThaiNLP not installed. Install with: pip install pythainlp")
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class ThaiReadabilityAnalyzer:
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"""Analyze Thai text readability"""
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def __init__(self):
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self.formal_particles = [
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'ครับ', 'ค่ะ', 'ข้าพเจ้า', 'กระผม', 'ดิฉัน', 'ท่าน', 'ซึ่ง', 'อัน',
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'ย่อม', 'ย่อมเป็น', 'ประการ', 'ดังกล่าว', 'ดังกล่าวแล้ว', 'ดังนี้'
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]
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self.informal_particles = [
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'นะ', 'จ้ะ', 'อ่ะ', 'มั้ย', 'เปล่าว่ะ', 'gue', 'mang', 'เว้ย',
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'วะ', 'เหอะ', 'ซิ', 'นู่น', 'นี่', 'นั่น', 'โครต', 'มาก'
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]
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def count_sentences(self, text: str) -> int:
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"""Count Thai sentences"""
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if not THAI_SUPPORT:
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# Fallback: count Thai sentence endings
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thai_endings = ['.', '!', '?', '।', '๏']
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count = sum(text.count(e) for e in thai_endings)
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return max(count, 1)
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sentences = sent_tokenize(text, engine="whitespace")
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return len([s for s in sentences if s.strip()])
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def count_words(self, text: str) -> int:
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"""Count Thai words"""
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if not THAI_SUPPORT:
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return len(text.split())
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tokens = word_tokenize(text, engine="newmm")
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return len([t for t in tokens if t.strip()])
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def calculate_avg_sentence_length(self, text: str) -> float:
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"""Calculate average sentence length"""
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if not THAI_SUPPORT:
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sentences = re.split(r'[.!?]', text)
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sentences = [s for s in sentences if s.strip()]
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if not sentences:
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return 0
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words = text.split()
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return len(words) / len(sentences)
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sentences = sent_tokenize(text, engine="whitespace")
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sentences = [s for s in sentences if s.strip()]
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if not sentences:
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return 0
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total_words = sum(
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len(word_tokenize(s, engine="newmm"))
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for s in sentences
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)
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return total_words / len(sentences)
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def detect_formality(self, text: str) -> Dict:
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"""Detect Thai formality level"""
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formal_count = sum(text.count(p) for p in self.formal_particles)
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informal_count = sum(text.count(p) for p in self.informal_particles)
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total = formal_count + informal_count
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if total == 0:
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ratio = 0.5 # Neutral
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else:
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ratio = formal_count / total
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if ratio > 0.6:
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level = "เป็นทางการ (Formal)"
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score = 80
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elif ratio < 0.4:
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level = "กันเอง (Casual)"
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score = 20
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else:
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level = "ปกติ (Normal)"
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score = 50
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return {
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'level': level,
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'score': score,
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'formal_particle_count': formal_count,
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'informal_particle_count': informal_count,
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'ratio': round(ratio, 2)
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}
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def estimate_grade_level(self, avg_sentence_length: float, formality_score: int) -> Dict:
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"""Estimate Thai grade level"""
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# Thai grade level estimation based on sentence complexity
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if avg_sentence_length < 15:
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grade_th = "ง่าย (ม.6-ม.9)"
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grade_num = 6-9
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elif avg_sentence_length < 25:
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grade_th = "ปานกลาง (ม.10-ม.12)"
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grade_num = 10-12
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else:
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grade_th = "ยาก (ม.13+)"
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grade_num = 13
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# Adjust for formality
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if formality_score > 70:
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grade_th += " (ทางการ)"
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elif formality_score < 30:
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grade_th += " (กันเอง)"
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return {
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'thai': grade_th,
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'numeric_range': grade_num,
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'us_equivalent': self._thai_to_us_grade(grade_num)
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}
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def _thai_to_us_grade(self, thai_grade_range) -> str:
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"""Convert Thai grade to US equivalent"""
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if isinstance(thai_grade_range, range):
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avg = sum(thai_grade_range) / len(thai_grade_range)
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elif isinstance(thai_grade_range, int):
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avg = thai_grade_range
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else:
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avg = 10
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# Very rough conversion
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if avg <= 9:
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return "6th-8th grade"
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elif avg <= 12:
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return "9th-12th grade"
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else:
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return "College+"
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def analyze_paragraph_structure(self, text: str) -> Dict:
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"""Analyze paragraph structure"""
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paragraphs = [p for p in text.split('\n\n') if p.strip()]
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if not paragraphs:
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return {
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'paragraph_count': 0,
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'avg_length_words': 0,
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'avg_length_sentences': 0
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}
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paragraph_lengths = [
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self.count_words(p)
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for p in paragraphs
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]
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paragraph_sentences = [
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self.count_sentences(p)
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for p in paragraphs
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]
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return {
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'paragraph_count': len(paragraphs),
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'avg_length_words': round(sum(paragraph_lengths) / len(paragraphs), 1),
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'avg_length_sentences': round(sum(paragraph_sentences) / len(paragraphs), 1),
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'shortest_paragraph': min(paragraph_lengths),
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'longest_paragraph': max(paragraph_lengths)
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}
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def calculate_readability_score(self, avg_sentence_length: float, formality_score: int,
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paragraph_score: float) -> float:
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"""
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Calculate overall readability score (0-100)
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Factors:
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- Sentence length (optimal: 15-25 words)
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- Formality (optimal: 40-60 for general content)
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- Paragraph structure (optimal: varied lengths)
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"""
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# Sentence length score (0-40)
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if 15 <= avg_sentence_length <= 25:
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sentence_score = 40
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elif 10 <= avg_sentence_length < 15 or 25 < avg_sentence_length <= 30:
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sentence_score = 30
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elif avg_sentence_length < 10:
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sentence_score = 20
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else:
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sentence_score = 15
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# Formality score (0-30)
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# Optimal: 40-60 (normal/formal mix)
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if 40 <= formality_score <= 60:
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formality_points = 30
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elif 30 <= formality_score < 40 or 60 < formality_score <= 70:
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formality_points = 25
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else:
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formality_points = 15
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# Paragraph score (0-30)
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paragraph_points = min(30, paragraph_score * 30)
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total = sentence_score + formality_points + paragraph_points
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return round(total, 1)
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def get_recommendations(self, analysis: Dict) -> List[str]:
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"""Generate recommendations"""
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recs = []
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avg_len = analysis['avg_sentence_length']
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if avg_len < 15:
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recs.append("ประโยคสั้นเกินไป พิจารณาเพิ่มรายละเอียดบ้าง")
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elif avg_len > 25:
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recs.append("ประโยคยาวเกินไป แบ่งออกเป็น 2-3 ประโยคจะอ่านง่ายขึ้น")
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formality = analysis['formality']['level']
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if "เป็นทางการ" in formality:
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recs.append("ภาษาเป็นทางการเกินไปสำหรับเนื้อหาทั่วไป พิจารณาใช้ภาษาที่เป็นกันเองมากขึ้น")
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elif "กันเอง" in formality:
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recs.append("ภาษาเป็นกันเองมาก ตรวจสอบว่าเหมาะกับกลุ่มเป้าหมายหรือไม่")
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para = analysis['paragraph_structure']
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if para['avg_length_words'] > 200:
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recs.append("บางย่อหน้ายาวเกินไป แบ่งย่อหน้าเพื่อให้อ่านง่ายขึ้น")
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if para['paragraph_count'] < 5:
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recs.append("เพิ่มจำนวนย่อหน้าเพื่อให้อ่านง่ายขึ้น")
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return recs
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def analyze(self, text: str) -> Dict:
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"""Full readability analysis"""
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avg_sentence_length = self.calculate_avg_sentence_length(text)
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formality = self.detect_formality(text)
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grade_level = self.estimate_grade_level(avg_sentence_length, formality['score'])
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paragraph_structure = self.analyze_paragraph_structure(text)
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# Calculate paragraph score (0-1)
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para_score = 0.5 # Default
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if paragraph_structure['paragraph_count'] > 0:
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# Score based on variety
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lengths = [paragraph_structure['avg_length_words']]
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if paragraph_structure['shortest_paragraph'] != paragraph_structure['longest_paragraph']:
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para_score = 0.8 # Good variety
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else:
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para_score = 0.6 # Same length
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readability_score = self.calculate_readability_score(
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avg_sentence_length,
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formality['score'],
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para_score
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)
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recommendations = self.get_recommendations({
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'avg_sentence_length': avg_sentence_length,
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'formality': formality,
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'paragraph_structure': paragraph_structure
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})
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return {
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'avg_sentence_length': round(avg_sentence_length, 1),
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'sentence_count': self.count_sentences(text),
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'word_count': self.count_words(text),
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'grade_level': grade_level,
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'formality': formality,
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'paragraph_structure': paragraph_structure,
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'readability_score': readability_score,
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'recommendations': recommendations
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}
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def main():
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"""Main entry point"""
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parser = argparse.ArgumentParser(
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description='Analyze Thai text readability'
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)
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parser.add_argument(
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'--text', '-t',
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required=True,
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help='Text content to analyze'
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)
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parser.add_argument(
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'--output', '-o',
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choices=['json', 'text'],
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default='text',
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help='Output format (default: text)'
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)
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args = parser.parse_args()
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# Analyze
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analyzer = ThaiReadabilityAnalyzer()
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result = analyzer.analyze(args.text)
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# Output
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if args.output == 'json':
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print(json.dumps(result, indent=2, ensure_ascii=False))
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else:
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print("\n📖 Thai Readability Analysis\n")
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print(f"Sentence Count: {result['sentence_count']}")
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print(f"Word Count: {result['word_count']}")
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print(f"Avg Sentence Length: {result['avg_sentence_length']} words")
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print(f"\nGrade Level: {result['grade_level']['thai']}")
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print(f"US Equivalent: {result['grade_level']['us_equivalent']}")
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print(f"\nFormality: {result['formality']['level']} (score: {result['formality']['score']})")
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print(f" - Formal particles: {result['formality']['formal_particle_count']}")
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print(f" - Informal particles: {result['formality']['informal_particle_count']}")
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print(f"\nParagraph Structure:")
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print(f" - Count: {result['paragraph_structure']['paragraph_count']}")
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print(f" - Avg length: {result['paragraph_structure']['avg_length_words']} words")
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print(f"\nReadability Score: {result['readability_score']}/100")
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if result['recommendations']:
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print(f"\n💡 Recommendations:")
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for rec in result['recommendations']:
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print(f" • {rec}")
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print()
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if __name__ == '__main__':
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main()
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