import os
import uuid
import json
import logging
from flask import Blueprint, request, jsonify, send_file, url_for, current_app
from flask_login import login_required, current_user
from backend.models.database import db, Job, Application
from backend.services.interview_engine import (
    generate_first_question,
    edge_tts_to_file_sync,
    whisper_stt,
    evaluate_answer
)

interview_api = Blueprint("interview_api", __name__)

@interview_api.route("/start_interview", methods=["POST"])
@login_required
def start_interview():
    """
    Start a new interview. Generates the first question based on the user's
    resume/profile and the selected job. Always returns a JSON payload
    containing the question text and, if available, a URL to an audio
    rendition of the question.
    """
    try:
        data = request.get_json() or {}
        job_id = data.get("job_id")

        # Validate the job and the user's application
        job = Job.query.get_or_404(job_id)
        application = Application.query.filter_by(
            user_id=current_user.id,
            job_id=job_id
        ).first()
        
        if not application or not application.extracted_features:
            return jsonify({"error": "No application/profile data found."}), 400

        # Parse the candidate's profile
        try:
            profile = json.loads(application.extracted_features)
        except Exception as e:
            logging.error(f"Invalid profile JSON: {e}")
            return jsonify({"error": "Invalid profile JSON"}), 500

        # Generate the first question using the LLM
        question = generate_first_question(profile, job)
        
        if not question:
            question = "Tell me about yourself and why you're interested in this position."

        # Attempt to generate a TTS audio file for the question
        audio_url = None
        try:
            audio_dir = "/tmp/audio"
            os.makedirs(audio_dir, exist_ok=True)
            filename = f"q_{uuid.uuid4().hex}.wav"
            audio_path = os.path.join(audio_dir, filename)
            
            audio_result = edge_tts_to_file_sync(question, audio_path)
            if audio_result and os.path.exists(audio_path) and os.path.getsize(audio_path) > 1000:
                audio_url = url_for("interview_api.get_audio", filename=filename)
                logging.info(f"Audio generated successfully: {audio_url}")
            else:
                logging.warning("Audio generation failed or file too small")
        except Exception as e:
            logging.error(f"Error generating TTS audio: {e}")
            audio_url = None

        return jsonify({
            "question": question,
            "audio_url": audio_url
        })
        
    except Exception as e:
        logging.error(f"Error in start_interview: {e}")
        return jsonify({"error": "Internal server error"}), 500

import subprocess
@interview_api.route("/transcribe_audio", methods=["POST"])
@login_required
def transcribe_audio():
    """Transcribe uploaded .webm audio using ffmpeg conversion and Faster-Whisper"""
    audio_file = request.files.get("audio")
    if not audio_file:
        return jsonify({"error": "No audio file received."}), 400

    temp_dir = "/tmp/interview_temp"
    os.makedirs(temp_dir, exist_ok=True)

    original_path = os.path.join(temp_dir, f"user_audio_{uuid.uuid4().hex}.webm")
    wav_path = original_path.replace(".webm", ".wav")

    audio_file.save(original_path)

    # Convert to WAV using ffmpeg
    try:
        subprocess.run(
            ["ffmpeg", "-y", "-i", original_path, wav_path],
            stdout=subprocess.DEVNULL,
            stderr=subprocess.DEVNULL
        )
    except Exception as e:
        logging.error(f"FFmpeg conversion failed: {e}")
        return jsonify({"error": "Failed to convert audio"}), 500

    # Transcribe
    transcript = whisper_stt(wav_path)

    # Cleanup
    try:
        os.remove(original_path)
        os.remove(wav_path)
    except:
        pass

    if not transcript or not transcript.strip():
        return jsonify({"error": "No speech detected in audio. Please try again."}), 400

    return jsonify({"transcript": transcript})

@interview_api.route("/process_answer", methods=["POST"])
@login_required
def process_answer():
    """
    Process a user's answer and return a follow‑up question along with an
    evaluation. Always responds with JSON.
    """
    try:
        data = request.get_json() or {}
        answer = data.get("answer", "").strip()
        question_idx = data.get("questionIndex", 0)
        
        if not answer:
            return jsonify({"error": "No answer provided."}), 400

        # Get the current question for evaluation context
        current_question = data.get("current_question", "Tell me about yourself")

        # Evaluate the answer
        evaluation_result = evaluate_answer(current_question, answer)

        # Determine completion (3 questions in total, zero‑based index)
        is_complete = question_idx >= 2
        
        next_question_text = None
        audio_url = None
        
        if not is_complete:
            # Generate the next question based on the current question index.
            #
            # Question indices are zero‑based: 0 for the first follow‑up question,
            # 1 for the second, and so on.  We want the final (third) question
            # delivered by this route to always probe the candidate's salary
            # expectations and preferred working arrangement.  After the user
            # answers this question (i.e. when ``question_idx`` becomes 2), the
            # interview is considered complete and no further questions are
            # generated.
            if question_idx == 0:
                next_question_text = "Can you describe a challenging project you've worked on and how you overcame the difficulties?"
            elif question_idx == 1:
                # Salary expectations question for the final interview round
                next_question_text = (
                    "What are your salary expectations? Are you looking for a full-time or part-time role, "
                    "and do you prefer remote or on-site work?"
                )
            else:
                # Fallback for unexpected indices; ask if the candidate has any questions.
                next_question_text = "Do you have any questions about the role or our company?"

            # Try to generate audio for the next question
            try:
                audio_dir = "/tmp/audio"
                os.makedirs(audio_dir, exist_ok=True)
                filename = f"q_{uuid.uuid4().hex}.wav"
                audio_path = os.path.join(audio_dir, filename)
                
                audio_result = edge_tts_to_file_sync(next_question_text, audio_path)
                if audio_result and os.path.exists(audio_path) and os.path.getsize(audio_path) > 1000:
                    audio_url = url_for("interview_api.get_audio", filename=filename)
                    logging.info(f"Next question audio generated: {audio_url}")
            except Exception as e:
                logging.error(f"Error generating next question audio: {e}")
                audio_url = None

        return jsonify({
            "success": True,
            "next_question": next_question_text,
            "audio_url": audio_url,
            "evaluation": evaluation_result,
            "is_complete": is_complete
        })
        
    except Exception as e:
        logging.error(f"Error in process_answer: {e}")
        return jsonify({"error": "Error processing answer. Please try again."}), 500

@interview_api.route("/audio/<string:filename>", methods=["GET"])
@login_required
def get_audio(filename: str):
    """Serve previously generated TTS audio from the /tmp/audio directory."""
    try:
        # Sanitize filename to prevent directory traversal
        safe_name = os.path.basename(filename)
        if not safe_name.endswith('.wav'):
            return jsonify({"error": "Invalid audio file format."}), 400
            
        audio_path = os.path.join("/tmp/audio", safe_name)
        
        if not os.path.exists(audio_path):
            logging.warning(f"Audio file not found: {audio_path}")
            return jsonify({"error": "Audio file not found."}), 404
            
        if os.path.getsize(audio_path) == 0:
            logging.warning(f"Audio file is empty: {audio_path}")
            return jsonify({"error": "Audio file is empty."}), 404
            
        return send_file(
            audio_path, 
            mimetype="audio/wav", 
            as_attachment=False,
            conditional=True  # Enable range requests for better audio streaming
        )
        
    except Exception as e:
        logging.error(f"Error serving audio file {filename}: {e}")
        return jsonify({"error": "Error serving audio file."}), 500