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Enhanced Judgment Segmenter (FIXED)
Segments judgments into: Facts, Issues, Arguments, Analysis, Decision
"""
import re
import os
import logging
from typing import List, Dict, Tuple
from dataclasses import dataclass
try:
from transformers import pipeline
TRANSFORMERS_AVAILABLE = True
except ImportError:
TRANSFORMERS_AVAILABLE = False
logger = logging.getLogger(__name__)
@dataclass
class Section:
type: str # facts/issues/arguments/analysis/decision/unknown
text: str
start_para_idx: int
end_para_idx: int
confidence: float
class JudgmentSegmenter:
MARKERS = {
'facts': [
r'\bbrief\s+facts?\b',
r'\bfactual\s+(matrix|background)\b',
r'\bcircumstances\s+of\s+the\s+case\b',
r'\bbackground\b',
],
'issues': [
r'\bissues?\s+(for|of)\s+(consideration|determination)\b',
r'\bsubstantial\s+questions?\b',
r'\bpoints?\s+for\s+consideration\b',
r'\bquestions?\s+framed\b',
],
'arguments': [
r'\blearned\s+counsel\b',
r'\bsubmissions?\b',
r'\b(argued|submitted|contended)\b',
r'\bon\s+behalf\s+of\b',
],
'analysis': [
r'\bwe\s+have\s+(considered|examined|analysed)\b',
r'\bthe\s+court\s+(finds|observes|notes|holds)\b',
r'\bin\s+our\s+(view|opinion)\b',
r'\bit\s+is\s+clear\s+that\b',
],
'decision': [
r'\b(appeal|petition|writ)\s+is\s+(allowed|dismissed)\b',
r'\baccordingly\b',
r'\bwe\s+direct\b',
r'\bheld\s*:\b',
r'\border\b',
]
}
def __init__(self, model_path: str = "models/segmentation_model"):
"""Initialize segmenter, preferring ML model if available, else Regex fallback"""
self.use_ml = False
self.classifier = None
if TRANSFORMERS_AVAILABLE and os.path.exists(model_path):
try:
logger.info(f"Loading ML Segmentation model from {model_path}...")
self.classifier = pipeline("text-classification", model=model_path, device=-1)
self.use_ml = True
logger.info("✓ ML Segmenter loaded successfully.")
except Exception as e:
logger.warning(f"Failed to load ML model, falling back to Regex: {e}")
else:
logger.info("ML model not found or transformers not installed. Using Regex fallback.")
def detect_section(self, para: str, position_ratio: float) -> Tuple[str, float]:
"""
Detect section type for a paragraph
Returns: (section_type, confidence)
"""
para_lower = para.lower()
best_type = 'unknown'
best_conf = 0.0
for sec_type, patterns in self.MARKERS.items():
for pattern in patterns:
if re.search(pattern, para_lower):
conf = 0.6
# Position-based bias
if sec_type == 'facts' and position_ratio < 0.30:
conf += 0.2
elif sec_type == 'decision' and position_ratio > 0.70:
conf += 0.3
# Strong anchor near paragraph start
if re.search(pattern, para_lower[:120]):
conf += 0.2
conf = min(conf, 1.0)
if conf > best_conf:
best_type = sec_type
best_conf = conf
return best_type, best_conf
def detect_section_ml(self, para: str) -> Tuple[str, float]:
"""Detect using HuggingFace classifier"""
if not para.strip() or not self.classifier:
return "unknown", 0.0
# Truncate to max length to avoid tokenization errors
truncated = para[:512]
result = self.classifier(truncated)[0]
# Assume labels are like LABEL_FACTS, LABEL_ISSUES or directly facts, issues
label = result['label'].lower().replace('label_', '')
score = result['score']
# Enforce confidence threshold
if score < 0.5:
return "unknown", score
return label, score
def segment(self, paragraph_texts: List[str]) -> List[Section]:
"""
Segment judgment based on paragraph list (INDEX-ALIGNED)
"""
if not paragraph_texts:
return []
sections: List[Section] = []
current_type = 'unknown'
current_paras = []
current_conf = 0.0
start_idx = 0
total = len(paragraph_texts)
for i, para in enumerate(paragraph_texts):
position_ratio = i / max(total, 1)
if self.use_ml:
sec_type, conf = self.detect_section_ml(para)
else:
sec_type, conf = self.detect_section(para, position_ratio)
# Fallback: early unknown paragraphs are likely facts
if sec_type == 'unknown' and position_ratio < 0.30 and i > 0:
sec_type = 'facts'
conf = 0.4
# Section boundary
if conf > 0.4 and sec_type != current_type:
if current_paras:
sections.append(
Section(
type=current_type,
text="\n\n".join(current_paras),
start_para_idx=start_idx,
end_para_idx=i - 1,
confidence=round(current_conf, 2)
)
)
current_type = sec_type
current_paras = [para]
current_conf = conf
start_idx = i
else:
current_paras.append(para)
current_conf = max(current_conf, conf)
# Final section
if current_paras:
sections.append(
Section(
type=current_type,
text="\n\n".join(current_paras),
start_para_idx=start_idx,
end_para_idx=total - 1,
confidence=round(current_conf, 2)
)
)
return sections
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