from langchain.llms import HuggingFaceHub
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
import os

class InterviewAgent:
    def __init__(self, job_role, cv_summary):
        self.job_role = job_role
        self.cv_summary = cv_summary
        self.llm = HuggingFaceHub(
            repo_id="meta-llama/Meta-Llama-3-8B-Instruct",
            model_kwargs={"temperature": 0.5, "max_length": 2048}
        )
        self.questions = self._generate_questions()
    
    def _generate_questions(self):
        # Load job-specific questions
        base_questions = self._load_base_questions()
        
        # Generate CV-specific questions
        cv_questions = self._generate_cv_questions()
        
        return base_questions + cv_questions
    
    def _load_base_questions(self):
        # In a real app, these would be more sophisticated and loaded from a database
        role_questions = {
            "Software Engineer": [
                {"text": "Explain the SOLID principles in object-oriented design.", "type": "technical", "weight": 0.3},
                {"text": "How would you optimize a slow database query?", "type": "technical", "weight": 0.25},
                {"text": "Describe your experience with Agile methodologies.", "type": "behavioral", "weight": 0.2},
                {"text": "How do you handle conflicts in a team setting?", "type": "behavioral", "weight": 0.15},
                {"text": "Where do you see yourself in 5 years?", "type": "general", "weight": 0.1}
            ],
            "Data Scientist": [
                {"text": "Explain the bias-variance tradeoff.", "type": "technical", "weight": 0.3},
                {"text": "How would you handle missing data in a dataset?", "type": "technical", "weight": 0.25},
                {"text": "Describe a time when you had to explain complex technical concepts to non-technical stakeholders.", "type": "behavioral", "weight": 0.2},
                {"text": "How do you stay updated with the latest developments in data science?", "type": "behavioral", "weight": 0.15},
                {"text": "What motivates you to work in data science?", "type": "general", "weight": 0.1}
            ]
        }
        
        return role_questions.get(self.job_role, role_questions["Software Engineer"])
    
    def _generate_cv_questions(self):
        # Generate questions based on CV content
        prompt = f"""
        Based on the following CV summary for a {self.job_role} position, generate 3 specific interview questions.
        Focus on areas that need clarification or seem particularly relevant to the role.
        
        CV Summary:
        {self.cv_summary['text']}
        
        Generate exactly 3 questions in this format:
        1. [question text]|technical
        2. [question text]|behavioral
        3. [question text]|technical
        
        Make the questions specific to the candidate's experience and the job role.
        """
        
        response = self.llm(prompt)
        questions = []
        
        for line in response.split('\n'):
            if line.strip() and '|' in line:
                text = line.split('|')[0].strip()
                q_type = line.split('|')[1].strip().lower()
                questions.append({
                    "text": text,
                    "type": q_type,
                    "weight": 0.15 if q_type == "technical" else 0.1,
                    "cv_based": True
                })
        
        return questions[:3]  # Ensure we only take 3 questions
    
    def get_questions(self):
        return self.questions
    
    def evaluate_answer(self, question, answer):
        prompt = f"""
        Evaluate the following interview answer for a {self.job_role} position.
        Provide specific feedback and a score from 1-10 based on:
        - Technical accuracy (if technical question)
        - Relevance to the question
        - Clarity of communication
        - Demonstration of skills/experience
        
        Question: {question['text']}
        Answer: {answer}
        
        Respond in this exact format:
        Score: [x]/10
        Feedback: [your feedback here]
        """
        
        response = self.llm(prompt)
        
        # Parse the response
        score = 5  # default if parsing fails
        feedback = "Evaluation not available"
        
        if "Score:" in response and "Feedback:" in response:
            try:
                score_part = response.split("Score:")[1].split("/10")[0].strip()
                score = float(score_part)
                feedback = response.split("Feedback:")[1].strip()
            except:
                pass
        
        return {
            "score": score,
            "feedback": feedback,
            "max_score": 10
        }
    
    def final_evaluation(self, answers):
        total_score = 0
        max_possible = 0
        
        # Calculate weighted score
        for answer in answers:
            weight = answer['question'].get('weight', 0.1)
            total_score += answer['evaluation']['score'] * weight
            max_possible += 10 * weight
        
        overall_score = (total_score / max_possible) * 10
        
        # Determine band
        if overall_score >= 9:
            band = "Expert (Band 5)"
        elif overall_score >= 7:
            band = "Proficient (Band 4)"
        elif overall_score >= 5:
            band = "Competent (Band 3)"
        elif overall_score >= 3:
            band = "Limited (Band 2)"
        else:
            band = "Beginner (Band 1)"
        
        return {
            "score": round(overall_score, 1),
            "band": band,
            "total_questions": len(answers)
        }