import os
import uuid
import json
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.

    Previously this endpoint returned a raw audio file when TTS generation
    succeeded. This prevented the client from displaying the actual question
    and forced it to fall back to a hard‑coded default. By always returning
    structured JSON we ensure the UI can show the generated question and
    optionally play the associated audio.
    """
    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:
        return jsonify({"error": "Invalid profile JSON"}), 500

    # Generate the first question using the LLM
    question = generate_first_question(profile, job)

    # Attempt to generate a TTS audio file for the question. If successful
    # we'll return a URL that the client can call to retrieve it; otherwise
    # audio_url remains None.
    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_out = edge_tts_to_file_sync(question, audio_path)
        if audio_out and os.path.exists(audio_path):
            audio_url = url_for("interview_api.get_audio", filename=filename)
    except Exception:
        audio_url = None

    return jsonify({
        "question": question,
        "audio_url": audio_url
    })


@interview_api.route("/transcribe_audio", methods=["POST"])
@login_required
def transcribe_audio():
    audio_file = request.files.get("audio")
    if not audio_file:
        return jsonify({"error": "No audio file received."}), 400
    
    # Use /tmp directory which is writable in Hugging Face Spaces
    temp_dir = "/tmp/interview_temp"
    os.makedirs(temp_dir, exist_ok=True)

    filename = f"user_audio_{uuid.uuid4().hex}.wav"
    path = os.path.join(temp_dir, filename)
    audio_file.save(path)
    
    transcript = whisper_stt(path)
    
    # Clean up
    try:
        os.remove(path)
    except:
        pass
    
    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 containing:

      - success: boolean indicating the operation succeeded
      - next_question: the text of the next question
      - audio_url: optional URL to the TTS audio for the next question
      - evaluation: a dict with a score and feedback
      - is_complete: boolean indicating if the interview is finished

    Returning JSON even when audio generation succeeds simplifies client
    handling and prevents errors when parsing the response.
    """
    data = request.get_json() or {}
    answer = data.get("answer", "")
    question_idx = data.get("questionIndex", 0)

    # Construct the next question. In a full implementation this would
    # depend on the user's answer and job description.
    next_question_text = f"Follow‑up question {question_idx + 2}: Can you elaborate on your experience with relevant technologies?"

    # Stubbed evaluation of the answer. Replace with a call to evaluate_answer()
    evaluation_result = {
        "score": "medium",
        "feedback": "Good answer, but be more specific."
    }

    # Determine completion (3 questions in total, zero‑based index)
    is_complete = question_idx >= 2

    # Try to generate audio for the next 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_out = edge_tts_to_file_sync(next_question_text, audio_path)
        if audio_out and os.path.exists(audio_path):
            audio_url = url_for("interview_api.get_audio", filename=filename)
    except Exception:
        audio_url = None

    return jsonify({
        "success": True,
        "next_question": next_question_text,
        "audio_url": audio_url,
        "evaluation": evaluation_result,
        "is_complete": is_complete
    })

@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."""
    safe_name = os.path.basename(filename)
    audio_path = os.path.join("/tmp/audio", safe_name)
    if not os.path.exists(audio_path):
        return jsonify({"error": "Audio file not found."}), 404
    return send_file(audio_path, mimetype="audio/wav", as_attachment=False)