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persistence.py
--------------
Persistence layer for candidate results, feedback, and analytics.
Features:
- SQLite for local storage (no external DB needed)
- Store results, feedback, hiring outcomes
- Query historical matches
- Analytics on hiring success
Author: SmartHire AI
"""
import json
import logging
import sqlite3
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Optional, Tuple
logger = logging.getLogger(__name__)
DB_PATH = Path("smarthire_data.db")
class CandidateDatabase:
"""
SQLite-backed candidate and result persistence.
"""
def __init__(self, db_path: Path = DB_PATH):
self.db_path = db_path
self._init_db()
def _init_db(self) -> None:
"""Initialize database schema if not exists."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
# Candidates table
cursor.execute("""
CREATE TABLE IF NOT EXISTS candidates (
id INTEGER PRIMARY KEY,
name TEXT UNIQUE,
email TEXT,
phone TEXT,
resume_text TEXT,
skills TEXT,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
""")
# Job descriptions table
cursor.execute("""
CREATE TABLE IF NOT EXISTS job_descriptions (
id INTEGER PRIMARY KEY,
title TEXT,
jd_text TEXT,
skills_required TEXT,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
""")
# Match results table
cursor.execute("""
CREATE TABLE IF NOT EXISTS match_results (
id INTEGER PRIMARY KEY,
candidate_id INTEGER,
jd_id INTEGER,
match_score REAL,
semantic_score REAL,
skill_coverage REAL,
recommendation TEXT,
result_json TEXT,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY(candidate_id) REFERENCES candidates(id),
FOREIGN KEY(jd_id) REFERENCES job_descriptions(id)
)
""")
# Feedback table
cursor.execute("""
CREATE TABLE IF NOT EXISTS feedback (
id INTEGER PRIMARY KEY,
match_id INTEGER,
outcome TEXT,
notes TEXT,
rating INTEGER,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY(match_id) REFERENCES match_results(id)
)
""")
conn.commit()
conn.close()
logger.info(f"Database initialized: {self.db_path}")
def add_candidate(self, name: str, email: str, phone: str, resume_text: str) -> int:
"""Add a candidate. Returns candidate_id."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
try:
cursor.execute(
"INSERT INTO candidates (name, email, phone, resume_text) VALUES (?, ?, ?, ?)",
(name, email, phone, resume_text),
)
conn.commit()
candidate_id = cursor.lastrowid
logger.info(f"Candidate added: {name} (ID: {candidate_id})")
return candidate_id
except sqlite3.IntegrityError:
logger.warning(f"Candidate already exists: {name}")
return self.get_candidate_id(name)
finally:
conn.close()
def get_candidate_id(self, name: str) -> Optional[int]:
"""Get candidate ID by name."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute("SELECT id FROM candidates WHERE name = ?", (name,))
result = cursor.fetchone()
conn.close()
return result[0] if result else None
def add_job_description(self, title: str, jd_text: str) -> int:
"""Add a job description. Returns jd_id."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute(
"INSERT INTO job_descriptions (title, jd_text) VALUES (?, ?)",
(title, jd_text),
)
conn.commit()
jd_id = cursor.lastrowid
conn.close()
return jd_id
def save_match_result(
self,
candidate_name: str,
jd_id: int,
match_score: float,
semantic_score: float,
skill_coverage: float,
recommendation: str,
result_dict: Dict,
) -> int:
"""Save a match result."""
candidate_id = self.get_candidate_id(candidate_name)
if not candidate_id:
logger.warning(f"Candidate not found: {candidate_name}")
return None
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute(
"""
INSERT INTO match_results (
candidate_id, jd_id, match_score, semantic_score,
skill_coverage, recommendation, result_json
) VALUES (?, ?, ?, ?, ?, ?, ?)
""",
(
candidate_id,
jd_id,
match_score,
semantic_score,
skill_coverage,
recommendation,
json.dumps(result_dict),
),
)
conn.commit()
result_id = cursor.lastrowid
conn.close()
logger.info(f"Match result saved (ID: {result_id})")
return result_id
def add_feedback(self, match_id: int, outcome: str, rating: int, notes: str = "") -> int:
"""Add feedback for a match. outcome: 'hired'|'rejected'|'in_progress'"""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute(
"INSERT INTO feedback (match_id, outcome, rating, notes) VALUES (?, ?, ?, ?)",
(match_id, outcome, rating, notes),
)
conn.commit()
feedback_id = cursor.lastrowid
conn.close()
logger.info(f"Feedback added for match {match_id}")
return feedback_id
def get_analytics(self) -> Dict:
"""Get overall hiring analytics."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
# Total matches
cursor.execute("SELECT COUNT(*) FROM match_results")
total_matches = cursor.fetchone()[0]
# Hiring outcomes
cursor.execute(
"SELECT outcome, COUNT(*) FROM feedback GROUP BY outcome"
)
outcomes = {row[0]: row[1] for row in cursor.fetchall()}
# Average scores by outcome
cursor.execute(
"""
SELECT f.outcome, AVG(m.match_score)
FROM feedback f
JOIN match_results m ON f.match_id = m.id
GROUP BY f.outcome
"""
)
avg_scores = {row[0]: round(row[1], 2) for row in cursor.fetchall()}
# Success rate (hired / total)
hired = outcomes.get("hired", 0)
total_with_feedback = sum(outcomes.values())
success_rate = (
(hired / total_with_feedback * 100)
if total_with_feedback > 0
else 0.0
)
conn.close()
return {
"total_matches": total_matches,
"outcomes": outcomes,
"average_scores_by_outcome": avg_scores,
"hiring_success_rate": round(success_rate, 2),
"total_with_feedback": total_with_feedback,
}
def get_match_history(self, candidate_name: str) -> List[Dict]:
"""Get all matches for a candidate."""
candidate_id = self.get_candidate_id(candidate_name)
if not candidate_id:
return []
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute(
"""
SELECT id, match_score, semantic_score, skill_coverage,
recommendation, created_at
FROM match_results
WHERE candidate_id = ?
ORDER BY created_at DESC
""",
(candidate_id,),
)
rows = cursor.fetchall()
conn.close()
return [
{
"id": row[0],
"match_score": row[1],
"semantic_score": row[2],
"skill_coverage": row[3],
"recommendation": row[4],
"created_at": row[5],
}
for row in rows
]
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