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import json
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import pandas as pd
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from collections import defaultdict
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from typing import List, Dict
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from datetime import datetime
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class LogAnalyzer:
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def __init__(self, log_path: str = "chat_history/chat_logs.json"):
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self.log_path = log_path
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self.logs = self._load_logs()
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def _load_logs(self) -> List[Dict]:
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"""Load and parse log entries from JSON file"""
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try:
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with open(self.log_path, "r", encoding="utf-8") as f:
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return [json.loads(line) for line in f]
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except (FileNotFoundError, json.JSONDecodeError):
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return []
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def get_basic_stats(self) -> Dict:
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"""Calculate basic conversation statistics"""
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if not self.logs:
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return {}
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return {
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"total_interactions": len(self.logs),
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"unique_users": len({log.get('session_id') for log in self.logs}),
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"avg_response_length": pd.Series([len(log['bot_response']) for log in self.logs]).mean(),
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"most_common_questions": self._get_common_questions(),
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"knowledge_base_usage": self._calculate_kb_usage()
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}
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def _get_common_questions(self, top_n: int = 5) -> List[Dict]:
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"""Identify most frequent user questions"""
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question_counts = defaultdict(int)
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for log in self.logs:
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question_counts[log['user_input']] += 1
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return sorted(
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[{"question": k, "count": v} for k, v in question_counts.items()],
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key=lambda x: x["count"],
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reverse=True
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)[:top_n]
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def _calculate_kb_usage(self) -> Dict:
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"""Analyze knowledge base effectiveness"""
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context_usage = defaultdict(int)
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for log in self.logs:
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if log.get('context'):
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context_usage['with_context'] += 1
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else:
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context_usage['without_context'] += 1
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return context_usage
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def temporal_analysis(self) -> Dict:
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"""Analyze usage patterns over time"""
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df = pd.DataFrame(self.logs)
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df['timestamp'] = pd.to_datetime(df['timestamp'])
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return {
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"daily_activity": df.resample('D', on='timestamp').size().to_dict(),
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"hourly_pattern": df.groupby(df['timestamp'].dt.hour).size().to_dict()
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}
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def generate_report(self) -> str:
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"""Generate comprehensive analysis report"""
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if not self.logs:
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return "No logs available for analysis"
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stats = self.get_basic_stats()
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temporal = self.temporal_analysis()
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top_questions = "\n".join(
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f"- {q['question']}: {q['count']}"
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for q in stats['most_common_questions']
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)
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header = "Legal Assistant Usage Report\n----------------------------"
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period = (f"Period: {self.logs[0]['timestamp']} - {self.logs[-1]['timestamp']}")
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basic_stats = (
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f"Total Interactions: {stats['total_interactions']}\n"
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f"Unique Users: {stats['unique_users']}\n"
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f"Average Response Length: {stats['avg_response_length']:.1f} chars"
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)
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kb_usage = (
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"Knowledge Base Usage:\n"
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f"- With context: {stats['knowledge_base_usage'].get('with_context', 0)}\n"
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f"- Without context: {stats['knowledge_base_usage'].get('without_context', 0)}"
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)
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patterns = (
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"Usage Patterns:\n"
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f"- Daily Activity: {temporal['daily_activity']}\n"
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f"- Hourly Distribution: {temporal['hourly_pattern']}"
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)
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report_sections = [
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header,
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period,
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basic_stats,
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"Top Questions:",
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top_questions,
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kb_usage,
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patterns
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]
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return "\n\n".join(report_sections)
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