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"""
Adaptive Feedback System for FitScore Feedback Agent
"""
import uuid
from datetime import datetime
from typing import Dict, Any, Optional
from sqlalchemy.orm import Session
from .database import get_db, GlobalPrompt, LocalPrompt, Feedback, FeedbackPattern
class AdaptiveFeedbackSystem:
"""Adaptive feedback system for processing and learning from feedback"""
def __init__(self):
self.global_prompt_template = self._get_global_prompt_template()
def _get_global_prompt_template(self) -> str:
"""Get the base global prompt template"""
return """
🎯 GOAL: Generate Smart Hiring Criteria to evaluate candidates for any role.
## Base Scoring Categories:
- Education (Weight: 20%)
- Career Trajectory (Weight: 25%)
- Company Relevance (Weight: 20%)
- Tenure Stability (Weight: 15%)
- Skills Match (Weight: 20%)
## Scoring Rules:
1. **Education Score (0-10)**:
- PhD: 10 points
- Masters: 9 points
- Bachelors: 7 points
- Associate: 5 points
- High School: 3 points
2. **Career Trajectory (0-10)**:
- Progressive roles: +2 points
- Industry relevance: +2 points
- Leadership experience: +2 points
- Technical depth: +2 points
- Innovation/achievements: +2 points
3. **Company Relevance (0-10)**:
- Fortune 500: 8-10 points
- Mid-size (100-1000): 6-8 points
- Startup: 4-6 points
- Industry alignment: +2 points
4. **Tenure Stability (0-10)**:
- 5+ years average: 10 points
- 3-5 years average: 8 points
- 2-3 years average: 6 points
- 1-2 years average: 4 points
- <1 year average: 2 points
5. **Skills Match (0-10)**:
- 90%+ match: 10 points
- 70-89% match: 8 points
- 50-69% match: 6 points
- 30-49% match: 4 points
- <30% match: 2 points
## Final Score Calculation:
Total Score = (Education × 0.20) + (Career Trajectory × 0.25) + (Company Relevance × 0.20) + (Tenure Stability × 0.15) + (Skills Match × 0.20)
## Acceptance Criteria:
- Score ≥ 7.0: Strong candidate
- Score 5.0-6.9: Consider with reservations
- Score < 5.0: Reject
"""
def create_initial_global_prompt(self):
"""Create initial global prompt in database"""
try:
db = next(get_db())
# Check if global prompt already exists
existing_prompt = db.query(GlobalPrompt).filter(GlobalPrompt.is_active == True).first()
if existing_prompt:
return existing_prompt
# Create new global prompt
global_prompt = GlobalPrompt(
version="v1.0",
prompt_content=self.global_prompt_template,
category_weights={
"education": 0.20,
"career_trajectory": 0.25,
"company_relevance": 0.20,
"tenure_stability": 0.15,
"skills_match": 0.20
},
scoring_rules={
"education": {"PhD": 10, "Masters": 9, "Bachelors": 7, "Associate": 5, "High School": 3},
"career_trajectory": {"progressive": 2, "industry_relevance": 2, "leadership": 2, "technical_depth": 2, "innovation": 2},
"company_relevance": {"fortune_500": 10, "mid_size": 8, "startup": 6, "industry_alignment": 2},
"tenure_stability": {"5_plus": 10, "3_5": 8, "2_3": 6, "1_2": 4, "less_1": 2},
"skills_match": {"90_plus": 10, "70_89": 8, "50_69": 6, "30_49": 4, "less_30": 2}
},
is_active=True
)
db.add(global_prompt)
db.commit()
db.refresh(global_prompt)
print("✅ Initial global prompt created successfully!")
return global_prompt
except Exception as e:
print(f"❌ Error creating initial global prompt: {e}")
return None
def add_feedback(self, job_id: str, company_id: str, analysis_id: str,
feedback_type: str, feedback_text: str, feedback_category: str,
confidence_score: float, email: Optional[str] = None,
linkedin_url: Optional[str] = None):
"""Add feedback to the system"""
try:
db = next(get_db())
feedback = Feedback(
feedback_id=str(uuid.uuid4()),
job_id=job_id,
company_id=company_id,
analysis_id=analysis_id,
feedback_type=feedback_type,
feedback_text=feedback_text,
feedback_category=feedback_category,
confidence_score=confidence_score,
email=email,
linkedin_url=linkedin_url
)
db.add(feedback)
db.commit()
db.refresh(feedback)
# Process feedback for patterns
self._process_feedback_patterns(feedback, db)
return feedback
except Exception as e:
print(f"❌ Error adding feedback: {e}")
raise
def _process_feedback_patterns(self, feedback: Feedback, db: Session):
"""Process feedback to identify patterns"""
try:
# Simple pattern detection based on feedback text
feedback_lower = feedback.feedback_text.lower()
# Check for common patterns
patterns = {
"technical_skills": ["technical", "skills", "programming", "coding", "development"],
"experience": ["experience", "years", "background", "history"],
"communication": ["communication", "soft skills", "interpersonal", "teamwork"],
"culture_fit": ["culture", "fit", "values", "personality", "attitude"]
}
for pattern_name, keywords in patterns.items():
if any(keyword in feedback_lower for keyword in keywords):
# Check if pattern already exists
existing_pattern = db.query(FeedbackPattern).filter(
FeedbackPattern.pattern_text == pattern_name,
FeedbackPattern.category == feedback.feedback_category
).first()
if existing_pattern:
existing_pattern.frequency += 1
existing_pattern.last_seen = datetime.utcnow()
else:
new_pattern = FeedbackPattern(
pattern_text=pattern_name,
category=feedback.feedback_category,
frequency=1,
affected_jobs=[feedback.job_id],
affected_companies=[feedback.company_id],
is_global=False
)
db.add(new_pattern)
db.commit()
except Exception as e:
print(f"❌ Error processing feedback patterns: {e}")
def get_feedback_analytics(self) -> Dict[str, Any]:
"""Get feedback analytics"""
try:
db = next(get_db())
total_feedback = db.query(Feedback).count()
# Feedback by type
feedback_by_type = {}
for feedback_type in ['hired', 'accepted', 'rejected', 'interviewed']:
count = db.query(Feedback).filter(Feedback.feedback_type == feedback_type).count()
feedback_by_type[feedback_type] = count
# Feedback by category
feedback_by_category = {}
for category in ['skills', 'experience', 'location', 'education', 'other']:
count = db.query(Feedback).filter(Feedback.feedback_category == category).count()
feedback_by_category[category] = count
return {
"total_feedback": total_feedback,
"feedback_by_type": feedback_by_type,
"feedback_by_category": feedback_by_category,
"patterns_count": db.query(FeedbackPattern).count()
}
except Exception as e:
print(f"❌ Error getting feedback analytics: {e}")
return {
"total_feedback": 0,
"feedback_by_type": {},
"feedback_by_category": {},
"patterns_count": 0
} |