backend/services/interview_engine.py

import os
import json
import asyncio
import edge_tts
from faster_whisper import WhisperModel
from langchain_groq import ChatGroq
import logging
import tempfile
import shutil

# Initialize models
chat_groq_api = os.getenv("GROQ_API_KEY")
if not chat_groq_api:
    raise ValueError("GROQ_API_KEY is not set in environment variables.")
groq_llm = ChatGroq(
    temperature=0.7,
    model_name="llama-3.3-70b-versatile",
    api_key=chat_groq_api
)

# Initialize Whisper model
whisper_model = None

def load_whisper_model():
    global whisper_model
    if whisper_model is None:
        try:
            device = "cuda" if os.system("nvidia-smi") == 0 else "cpu"
            compute_type = "float16" if device == "cuda" else "int8"
            whisper_model = WhisperModel("base", device=device, compute_type=compute_type)
            logging.info(f"Whisper model loaded on {device} with {compute_type}")
        except Exception as e:
            logging.error(f"Error loading Whisper model: {e}")
            # Fallback to CPU
            whisper_model = WhisperModel("base", device="cpu", compute_type="int8")
    return whisper_model

def generate_first_question(profile, job):
    """Generate the first interview question based on profile and job"""
    try:
        prompt = f"""
        You are conducting an interview for a {job.role} position at {job.company}.
        The candidate's profile shows:
        - Skills: {profile.get('skills', [])}
        - Experience: {profile.get('experience', [])}
        - Education: {profile.get('education', [])}
        
        Generate an appropriate opening interview question that is professional and relevant.
        Keep it concise and clear. Respond with ONLY the question text, no additional formatting.
        """
        
        response = groq_llm.invoke(prompt)
        
        # Fix: Handle AIMessage object properly
        if hasattr(response, 'content'):
            question = response.content.strip()
        elif isinstance(response, str):
            question = response.strip()
        else:
            question = str(response).strip()
            
        # Ensure we have a valid question
        if not question or len(question) < 10:
            question = "Tell me about yourself and why you're interested in this position."
            
        logging.info(f"Generated question: {question}")
        return question
        
    except Exception as e:
        logging.error(f"Error generating first question: {e}")
        return "Tell me about yourself and why you're interested in this position."

def edge_tts_to_file_sync(text, output_path, voice="en-US-AriaNeural"):
    """Synchronous wrapper for edge-tts with better error handling"""
    try:
        # Ensure text is not empty
        if not text or not text.strip():
            logging.error("Empty text provided for TTS")
            return None
            
        # Ensure the directory exists and is writable
        directory = os.path.dirname(output_path)
        if not directory:
            directory = "/tmp/audio"
            output_path = os.path.join(directory, os.path.basename(output_path))
        
        os.makedirs(directory, exist_ok=True)
        
        # Test write permissions with a temporary file
        test_file = os.path.join(directory, f"test_{os.getpid()}.tmp")
        try:
            with open(test_file, 'w') as f:
                f.write("test")
            os.remove(test_file)
            logging.info(f"Directory {directory} is writable")
        except (PermissionError, OSError) as e:
            logging.error(f"Directory {directory} is not writable: {e}")
            # Fallback to /tmp
            directory = "/tmp/audio"
            output_path = os.path.join(directory, os.path.basename(output_path))
            os.makedirs(directory, exist_ok=True)
        
        async def generate_audio():
            try:
                communicate = edge_tts.Communicate(text, voice)
                await communicate.save(output_path)
                logging.info(f"TTS audio saved to: {output_path}")
            except Exception as e:
                logging.error(f"Error in async TTS generation: {e}")
                raise
        
        # Run async function in sync context
        try:
            loop = asyncio.get_event_loop()
            if loop.is_running():
                # If loop is already running, create a new one in a thread
                import threading
                import concurrent.futures
                
                def run_in_thread():
                    new_loop = asyncio.new_event_loop()
                    asyncio.set_event_loop(new_loop)
                    try:
                        new_loop.run_until_complete(generate_audio())
                    finally:
                        new_loop.close()
                
                with concurrent.futures.ThreadPoolExecutor() as executor:
                    future = executor.submit(run_in_thread)
                    future.result(timeout=30)  # 30 second timeout
            else:
                loop.run_until_complete(generate_audio())
        except RuntimeError:
            # No event loop exists
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)
            try:
                loop.run_until_complete(generate_audio())
            finally:
                loop.close()
        
        # Verify file was created and has content
        if os.path.exists(output_path):
            file_size = os.path.getsize(output_path)
            if file_size > 1000:  # At least 1KB for a valid audio file
                logging.info(f"TTS file created successfully: {output_path} ({file_size} bytes)")
                return output_path
            else:
                logging.error(f"TTS file is too small: {output_path} ({file_size} bytes)")
                return None
        else:
            logging.error(f"TTS file was not created: {output_path}")
            return None
            
    except Exception as e:
        logging.error(f"Error in TTS generation: {e}")
        return None

def convert_webm_to_wav(webm_path, wav_path):
    """Convert WebM audio to WAV using ffmpeg if available"""
    try:
        import subprocess
        result = subprocess.run([
            'ffmpeg', '-i', webm_path, '-ar', '16000', '-ac', '1', '-y', wav_path
        ], capture_output=True, text=True, timeout=30)
        
        if result.returncode == 0 and os.path.exists(wav_path) and os.path.getsize(wav_path) > 0:
            logging.info(f"Successfully converted {webm_path} to {wav_path}")
            return wav_path
        else:
            logging.error(f"FFmpeg conversion failed: {result.stderr}")
            return None
    except (subprocess.TimeoutExpired, FileNotFoundError, Exception) as e:
        logging.error(f"Error converting audio: {e}")
        return None

def whisper_stt(audio_path):
    """Speech-to-text using Faster-Whisper with better error handling"""
    try:
        if not audio_path or not os.path.exists(audio_path):
            logging.error(f"Audio file does not exist: {audio_path}")
            return ""
        
        # Check if file has content
        file_size = os.path.getsize(audio_path)
        if file_size == 0:
            logging.error(f"Audio file is empty: {audio_path}")
            return ""
        
        logging.info(f"Processing audio file: {audio_path} ({file_size} bytes)")
        
        # If the file is WebM, try to convert it to WAV
        if audio_path.endswith('.webm'):
            wav_path = audio_path.replace('.webm', '.wav')
            converted_path = convert_webm_to_wav(audio_path, wav_path)
            if converted_path:
                audio_path = converted_path
            else:
                logging.warning("Could not convert WebM to WAV, trying with original file")
        
        model = load_whisper_model()
        
        # Add timeout and better error handling
        try:
            segments, info = model.transcribe(
                audio_path,
                language="en",  # Specify language for better performance
                task="transcribe",
                vad_filter=True,  # Voice activity detection
                vad_parameters=dict(min_silence_duration_ms=500)
            )
            
            transcript_parts = []
            for segment in segments:
                if hasattr(segment, 'text') and segment.text.strip():
                    transcript_parts.append(segment.text.strip())
            
            transcript = " ".join(transcript_parts)
            
            if transcript:
                logging.info(f"Transcription successful: '{transcript[:100]}...'")
            else:
                logging.warning("No speech detected in audio file")
                
            return transcript.strip()
            
        except Exception as e:
            logging.error(f"Error during transcription: {e}")
            return ""
        
    except Exception as e:
        logging.error(f"Error in STT: {e}")
        return ""

def evaluate_answer(question, answer, job_role="Software Developer", seniority="Mid-level"):
    """Evaluate candidate's answer with better error handling"""
    try:
        if not answer or not answer.strip():
            return {
                "score": "Poor",
                "feedback": "No answer provided."
            }
            
        prompt = f"""
        You are evaluating a candidate's answer for a {seniority} {job_role} position.
        
        Question: {question}
        Candidate Answer: {answer}
        
        Evaluate based on technical correctness, clarity, and relevance.
        Provide a brief evaluation in 1-2 sentences.
        
        Rate the answer as one of: Poor, Medium, Good, Excellent
        
        Respond in this exact format:
        Score: [Poor/Medium/Good/Excellent]
        Feedback: [Your brief feedback here]
        """
        
        response = groq_llm.invoke(prompt)
        
        # Handle AIMessage object properly
        if hasattr(response, 'content'):
            response_text = response.content.strip()
        elif isinstance(response, str):
            response_text = response.strip()
        else:
            response_text = str(response).strip()
        
        # Parse the response
        lines = response_text.split('\n')
        score = "Medium"  # default
        feedback = "Good answer, but could be more detailed."  # default
        
        for line in lines:
            line = line.strip()
            if line.startswith('Score:'):
                score = line.replace('Score:', '').strip()
            elif line.startswith('Feedback:'):
                feedback = line.replace('Feedback:', '').strip()
        
        # Ensure score is valid
        valid_scores = ["Poor", "Medium", "Good", "Excellent"]
        if score not in valid_scores:
            score = "Medium"
        
        return {
            "score": score,
            "feedback": feedback
        }
        
    except Exception as e:
        logging.error(f"Error evaluating answer: {e}")
        return {
            "score": "Medium",
            "feedback": "Unable to evaluate answer at this time."
        }