backend/routes/interview_api.py

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():
    data = request.get_json()
    job_id = data.get("job_id")
    
    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
    
    try:
        profile = json.loads(application.extracted_features)
    except:
        return jsonify({"error": "Invalid profile JSON"}), 500
    
    question = generate_first_question(profile, job)
    
    # Use /tmp directory which is writable in Hugging Face Spaces
    audio_dir = "/tmp/audio"
    os.makedirs(audio_dir, exist_ok=True)

    audio_filename = f"q_{uuid.uuid4().hex}.wav"
    audio_path = os.path.join(audio_dir, audio_filename)

    # Generate audio synchronously. The function returns None on error.
    audio_out = edge_tts_to_file_sync(question, audio_path)

    if audio_out and os.path.exists(audio_path):
        return send_file(audio_path, mimetype="audio/wav", as_attachment=False)
    else:
        # Fallback to JSON response if audio generation fails
        return jsonify({"question": question})


@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():
    data = request.get_json()
    answer = data.get("answer", "")
    question_idx = data.get("questionIndex", 0)
    
    # Generate next question (simplified for now). In a full implementation this
    # would call a model such as groq_llm to generate a follow‑up question based
    # on the candidate's answer.
    next_question = f"Follow‑up question {question_idx + 2}: Can you elaborate on your experience with relevant technologies?"

    # Use /tmp directory for audio files
    audio_dir = "/tmp/audio"
    os.makedirs(audio_dir, exist_ok=True)

    audio_filename = f"q_{uuid.uuid4().hex}.wav"
    audio_path = os.path.join(audio_dir, audio_filename)

    # Attempt to generate speech for the next question. If audio generation
    # fails, ``audio_out`` will be None and we return JSON response instead.
    audio_out = edge_tts_to_file_sync(next_question, audio_path)

    if audio_out and os.path.exists(audio_path):
        return send_file(audio_path, mimetype="audio/wav", as_attachment=False)
    else:
        # Fallback to JSON response
        response = {
            "success": True,
            "nextQuestion": next_question,
            "evaluation": {
                "score": "medium",
                "feedback": "Good answer, but be more specific."
            },
            "isComplete": question_idx >= 2,
            "summary": []
        }
        return jsonify(response)