import openai
import json
import requests
import base64
import os
import tempfile
import asyncio
import edge_tts
import time
import hashlib
import shutil
from typing import List, Dict, Any, Optional

class VirtualInterviewer:
    def __init__(self, api_key: str):
        """Initialize the virtual interviewer with the OpenAI API key."""
        self.api_key = api_key
        self.questions_asked = []
        self.user_answers = []
        self.conversation_history = []
        self.ideal_answers = {}
        self.question_audio_paths = {}
        
        # Create audio directory
        self.audio_dir = self._create_audio_directory()
        
        # Clean up any existing audio files
        self._cleanup_audio_files()
        
        # Initialize OpenAI client
        try:
            self.client = openai.OpenAI(api_key=api_key)
        except Exception as e:
            raise Exception(f"Failed to initialize OpenAI client: {str(e)}")
    
    def _create_audio_directory(self) -> str:
        """Create a directory to store audio files."""
        audio_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "audio_files")
        os.makedirs(audio_dir, exist_ok=True)
        return audio_dir
    
    def _cleanup_audio_files(self):
        """Delete all temporary audio files from previous sessions."""
        try:
            if os.path.exists(self.audio_dir):
                # Delete all files in the directory
                for filename in os.listdir(self.audio_dir):
                    file_path = os.path.join(self.audio_dir, filename)
                    if os.path.isfile(file_path):
                        os.remove(file_path)
                print(f"Cleaned up audio files in {self.audio_dir}")
        except Exception as e:
            print(f"Error cleaning up audio files: {str(e)}")
    
    def generate_interview_questions(
        self,
        job_description: str,
        interview_type: str,
        difficulty_level: str,
        key_topics: str,
        num_questions: int,
        generate_ideal_answers: bool = True
    ) -> List[str]:
        """Generate interview questions based on the job description and other parameters."""
        try:
            # Construct the system prompt based on whether we want ideal answers or not
            if generate_ideal_answers:
                system_prompt = f"""You are an expert interviewer for {interview_type} interviews.
                Generate {num_questions} {difficulty_level.lower()} difficulty interview questions for a {interview_type.lower()} interview based on the following job description:
                
                Job Description:
                {job_description}
                
                Key Topics to Focus on:
                {key_topics if key_topics else "No specific topics provided."}
                
                Please provide the questions and ideal answers in the following JSON format:
                {{
                    "questions": [
                        {{
                            "question": "Question 1",
                            "ideal_answer": "Ideal answer for question 1"
                        }},
                        ...
                    ]
                }}
                
                Make sure the questions are challenging but appropriate for the {difficulty_level.lower()} difficulty level.
                The ideal answers should be comprehensive and demonstrate expertise in the subject matter.
                """
            else:
                system_prompt = f"""You are an expert interviewer for {interview_type} interviews.
                Generate {num_questions} {difficulty_level.lower()} difficulty interview questions for a {interview_type.lower()} interview based on the following job description:
                
                Job Description:
                {job_description}
                
                Key Topics to Focus on:
                {key_topics if key_topics else "No specific topics provided."}
                
                Please provide the questions in a numbered list format.
                Make sure the questions are challenging but appropriate for the {difficulty_level.lower()} difficulty level.
                """
            
            # Make the API call to generate questions
            response = self.client.chat.completions.create(
                model="gpt-4o",
                messages=[
                    {"role": "system", "content": system_prompt},
                    {"role": "user", "content": f"Generate {num_questions} {interview_type.lower()} interview questions for a {difficulty_level.lower()} difficulty level."}
                ],
                temperature=0.7,
                max_tokens=2000
            )
            
            # Extract the response content
            response_content = response.choices[0].message.content
            
            # Process the response based on whether we're expecting JSON or a simple list
            if generate_ideal_answers:
                try:
                    # Try to parse as JSON
                    json_response = self._extract_json(response_content)
                    
                    # Extract questions and ideal answers
                    questions = []
                    for item in json_response.get("questions", []):
                        question = item.get("question", "")
                        ideal_answer = item.get("ideal_answer", "")
                        
                        if question:
                            questions.append(question)
                            if ideal_answer:
                                self.ideal_answers[question] = ideal_answer
                    
                    # If we couldn't extract questions from JSON, fall back to parsing as text
                    if not questions:
                        questions = self._parse_questions(response_content, num_questions)
                        # Generate ideal answers separately
                        self._generate_ideal_answers(questions, job_description, interview_type, difficulty_level)
                except Exception as e:
                    # If JSON parsing fails, fall back to text parsing
                    questions = self._parse_questions(response_content, num_questions)
                    # Generate ideal answers separately
                    self._generate_ideal_answers(questions, job_description, interview_type, difficulty_level)
            else:
                # Parse as simple text
                questions = self._parse_questions(response_content, num_questions)
            
            # Store the generated questions
            self.questions_asked = questions
            
            return questions
        except Exception as e:
            raise Exception(f"Failed to generate interview questions: {str(e)}")
    
    def generate_question_audio(self, question: str, voice_type: str) -> str:
        """Generate audio for a question using edge-tts."""
        try:
            # Check if we already have audio for this question
            if question in self.question_audio_paths and os.path.exists(self.question_audio_paths[question]):
                print(f"Using existing audio for question: {question[:30]}...")
                return self.question_audio_paths[question]
            
            # Create a unique filename based on the question content and timestamp
            question_hash = hashlib.md5(question.encode()).hexdigest()
            timestamp = int(time.time())
            filename = f"question_{question_hash}_{timestamp}.mp3"
            output_path = os.path.join(self.audio_dir, filename)
            
            # Map voice type to edge-tts voice
            voice_mapping = {
                "male_casual": "en-US-GuyNeural",
                "male_formal": "en-US-ChristopherNeural",
                "male_british": "en-GB-RyanNeural",
                "female_casual": "en-US-JennyNeural",
                "female_formal": "en-US-AriaNeural",
                "female_british": "en-GB-SoniaNeural"
            }
            
            # Get the voice name from the mapping, default to female casual
            voice = voice_mapping.get(voice_type, "en-US-JennyNeural")
            
            # Generate audio using edge-tts
            async def generate_audio():
                communicate = edge_tts.Communicate(question, voice)
                await communicate.save(output_path)
            
            # Run the async function
            asyncio.run(generate_audio())
            
            print(f"Generated audio for question: {question[:30]}... at {output_path}")
            
            # Store the audio path for this question
            self.question_audio_paths[question] = output_path
            
            return output_path
        except Exception as e:
            print(f"Error generating audio: {str(e)}")
            return ""
    
    def get_question_audio_path(self, question: str) -> str:
        """Get the audio path for a question."""
        # Check if we have an audio path for this question
        if question in self.question_audio_paths:
            # Verify the file exists
            if os.path.exists(self.question_audio_paths[question]):
                return self.question_audio_paths[question]
            else:
                # File doesn't exist, remove from dictionary
                del self.question_audio_paths[question]
                return ""
        return ""
    
    def _extract_json(self, text: str) -> Dict[str, Any]:
        """Extract JSON from text."""
        try:
            # Try to parse the entire text as JSON
            return json.loads(text)
        except json.JSONDecodeError:
            # If that fails, try to extract JSON from the text
            import re
            json_match = re.search(r'```json\n(.*?)\n```', text, re.DOTALL)
            if json_match:
                try:
                    return json.loads(json_match.group(1))
                except json.JSONDecodeError:
                    pass
            
            # Try to find JSON between curly braces
            json_match = re.search(r'({.*})', text, re.DOTALL)
            if json_match:
                try:
                    return json.loads(json_match.group(1))
                except json.JSONDecodeError:
                    pass
            
            # If all else fails, return an empty dict
            return {}
    
    def _generate_ideal_answers(self, questions: List[str], job_description: str, interview_type: str, difficulty_level: str):
        """Generate ideal answers for the questions."""
        try:
            # Prepare the prompt for generating ideal answers
            prompt = f"""You are an expert in {interview_type} interviews.
            For each of the following interview questions, provide an ideal answer that would impress the interviewer.
            The answers should be comprehensive, demonstrate expertise, and be appropriate for a {difficulty_level.lower()} difficulty level interview.
            
            Job Description:
            {job_description}
            
            Questions:
            {json.dumps(questions)}
            
            Please provide the answers in the following JSON format:
            {{
                "answers": [
                    {{
                        "question": "Question 1",
                        "ideal_answer": "Ideal answer for question 1"
                    }},
                    ...
                ]
            }}
            """
            
            # Make the API call to generate ideal answers
            response = self.client.chat.completions.create(
                model="gpt-4o",
                messages=[
                    {"role": "system", "content": "You are an expert interviewer providing ideal answers to interview questions."},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.7,
                max_tokens=2000
            )
            
            # Extract the response content
            response_content = response.choices[0].message.content
            
            try:
                # Try to parse as JSON
                json_response = self._extract_json(response_content)
                
                # Extract ideal answers
                for item in json_response.get("answers", []):
                    question = item.get("question", "")
                    ideal_answer = item.get("ideal_answer", "")
                    
                    if question and ideal_answer:
                        # Find the matching question in our list
                        for q in questions:
                            if question.lower() in q.lower() or q.lower() in question.lower():
                                self.ideal_answers[q] = ideal_answer
                                break
            except Exception as e:
                # If batch processing fails, fall back to individual processing
                for question in questions:
                    if question not in self.ideal_answers:
                        self.ideal_answers[question] = f"Unable to generate ideal answer: {str(e)}"
        except Exception as e:
            # Handle any errors in the overall ideal answer generation process
            print(f"Error generating ideal answers: {str(e)}")
            # Ensure all questions have a fallback ideal answer
            for question in questions:
                if question not in self.ideal_answers:
                    self.ideal_answers[question] = "Unable to generate ideal answer due to an error."
    
    def _parse_questions(self, questions_text: str, expected_count: int) -> List[str]:
        """Parse the questions from the text response."""
        lines = questions_text.strip().split('\n')
        questions = []
        
        for line in lines:
            line = line.strip()
            if line and (line[0].isdigit() or line.startswith('- ')):
                # Remove numbering or bullet points
                cleaned_line = line.lstrip('0123456789.- ').strip()
                if cleaned_line:
                    questions.append(cleaned_line)
        
        # If we couldn't parse the expected number of questions, try a simpler approach
        if len(questions) != expected_count:
            questions = [line.strip() for line in lines if line.strip()][:expected_count]
            
        return questions[:expected_count]  # Ensure we return exactly the expected number
    
    def get_next_question(self, question_index: int) -> str:
        """Get the next question from the list of generated questions."""
        if 0 <= question_index < len(self.questions_asked):
            return self.questions_asked[question_index]
        return "No more questions available."
    
    def store_user_answer(self, question: str, answer: str):
        """Store the user's answer to a question."""
        self.user_answers.append({"question": question, "answer": answer})
        self.conversation_history.append({"role": "assistant", "content": question})
        self.conversation_history.append({"role": "user", "content": answer})
    
    def get_ideal_answer(self, question: str) -> str:
        """Get the ideal answer for a question."""
        return self.ideal_answers.get(question, "No ideal answer available for this question.")
    
    def score_interview(self, job_description: str, interview_type: str, difficulty_level: str) -> Dict[str, Any]:
        """Score the interview based on the user's answers."""
        try:
            # Prepare the data for scoring
            questions_and_answers = []
            for qa in self.user_answers:
                question = qa["question"]
                answer = qa["answer"]
                ideal_answer = self.get_ideal_answer(question)
                
                questions_and_answers.append({
                    "question": question,
                    "answer": answer,
                    "ideal_answer": ideal_answer
                })
            
            # Prepare the prompt for scoring
            prompt = f"""You are an expert interviewer for {interview_type} interviews.
            Score the following interview answers based on the job description and difficulty level.
            
            Job Description:
            {job_description}
            
            Difficulty Level: {difficulty_level}
            
            For each question and answer, provide:
            1. A score from 0 to 5 (where 5 is excellent)
            2. Feedback on the answer
            3. Include the ideal answer for comparison. The ideal answer should be a comprehensive and detailed answer that would impress the interviewer with bullet points.
            
            Questions and Answers:
            {json.dumps(questions_and_answers)}
            
            Please provide the scores in the following JSON format:
            {{
                "overall_score": 4.5,
                "overall_feedback": "Overall feedback on the interview performance",
                "individual_scores": [
                    {{
                        "question": "Question 1",
                        "answer": "User's answer to question 1",
                        "ideal_answer": "Ideal answer to question 1",
                        "score": 4,
                        "feedback": "Feedback on the answer to question 1"
                    }},
                    ...
                ]
            }}
            """
            
            # Make the API call to score the interview
            response = self.client.chat.completions.create(
                model="gpt-4o",
                messages=[
                    {"role": "system", "content": "You are an expert interviewer scoring interview answers."},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.3,
                max_tokens=2000
            )
            
            # Extract the response content
            response_content = response.choices[0].message.content
            
            try:
                # Try to parse as JSON
                json_response = self._extract_json(response_content)
                return json_response
            except Exception as e:
                # If JSON parsing fails, return an error
                return {
                    "overall_score": 0,
                    "overall_feedback": f"Failed to score the interview: {str(e)}",
                    "individual_scores": []
                }
        except Exception as e:
            # If scoring fails, return an error
            return {
                "overall_score": 0,
                "overall_feedback": f"Failed to score the interview: {str(e)}",
                "individual_scores": []
            }