Smart Recruitment: Never Miss Great Talent

class SmartRecruiter:
    """Smart recruitment system"""
    
    def __init__(self, llm_api):
        self.llm = llm_api
        self.skill_taxonomy = self.load_skill_taxonomy()
        
    def parse_resume(self, resume_text):
        """Parse resume"""
        prompt = f"""Parse the following resume and extract structured information:

{resume_text}

Return JSON in the format:
{
    "basic_info": {
        "name": "",
        "email": "",
        "phone": "",
        "location": ""
    },
    "education": [...],
    "experience": [
        {
            "company": "",
            "position": "",
            "duration": "",
            "responsibilities": [],
            "achievements": []
        }
    ],
    "skills": {
        "technical": [],
        "soft": [],
        "languages": []
    },
    "summary": "Candidate core strengths summary"
}"""
        
        result = self.llm.generate(prompt, temperature=0.1)
        return json.loads(result)
    
    def match_candidate(self, candidate_profile, job_description):
        """Candidate-job matching"""
        prompt = f"""Evaluate the candidate's fit for the role:

Job description:
{job_description}

Candidate profile:
{json.dumps(candidate_profile, ensure_ascii=False)}

Evaluate on these dimensions (0-100 each):
1. Skill match
2. Experience relevance
3. Growth potential
4. Overall recommendation index

Also provide:
- Key strengths (3-5)
- Potential weaknesses (if any)
- Interview focus areas
- Overall recommendation"""
        
        analysis = self.llm.generate(prompt)
        return self.parse_match_result(analysis)
    
    def generate_interview_questions(self, candidate, position):
        """Generate interview questions"""
        prompt = f"""Based on the candidate background and job requirements, generate personalized interview questions:

Position: {position['title']}
Key requirements: {position['requirements']}

Candidate background:
- Skills: {', '.join(candidate['skills']['technical'])}
- Experience: {self.summarize_experience(candidate['experience'])}

Please generate:
1. Technical questions (3)
2. Project deep-dive questions (2)
3. Situational questions (2)
4. Culture fit question (1)

For each question include:
- The question
- What to assess
- Key points of an excellent answer"""
        
        questions = self.llm.generate(prompt)
        return self.format_questions(questions)
    
    def evaluate_interview(self, transcript, expected_skills):
        """Evaluate interview performance"""
        prompt = f"""Evaluate the candidate's interview performance:

Transcript:
{transcript}

Skills to assess:
{', '.join(expected_skills)}

Provide:
1. Technical proficiency
2. Communication
3. Problem-solving approach
4. Learning potential
5. Teamwork

Give scores (0-10) and specific comments."""
        
        evaluation = self.llm.generate(prompt)
        return evaluation
    
    def generate_talent_report(self, candidate_data):
        """Generate talent report"""
        # Comprehensive analysis of the candidate
        report = {
            'overview': self.analyze_candidate_overview(candidate_data),
            'strengths': self.identify_strengths(candidate_data),
            'development_areas': self.identify_gaps(candidate_data),
            'career_trajectory': self.predict_career_path(candidate_data),
            'recommendation': self.make_recommendation(candidate_data)
        }
        
        return report