1227 lines
54 KiB
Python
1227 lines
54 KiB
Python
import threading
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from functools import reduce
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import firebase_admin
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from firebase_admin import credentials
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from flask import Flask, request
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from flask_jwt_extended import JWTManager, jwt_required
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from helper.api_messages import *
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from helper.exam_variant import ExamVariant
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from helper.exercises import *
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from helper.file_helper import delete_files_older_than_one_day
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from helper.firebase_helper import *
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from helper.heygen_api import create_video, create_videos_and_save_to_db
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from helper.openai_interface import *
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from helper.question_templates import *
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from helper.speech_to_text_helper import *
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from heygen.AvatarEnum import AvatarEnum
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load_dotenv()
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app = Flask(__name__)
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app.config['JWT_SECRET_KEY'] = os.getenv("JWT_SECRET_KEY")
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jwt = JWTManager(app)
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# Initialize Firebase Admin SDK
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cred = credentials.Certificate(os.getenv("GOOGLE_APPLICATION_CREDENTIALS"))
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FIREBASE_BUCKET = os.getenv('FIREBASE_BUCKET')
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firebase_admin.initialize_app(cred)
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thread_event = threading.Event()
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# Configure logging
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logging.basicConfig(level=logging.DEBUG, # Set the logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
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format='%(asctime)s - %(levelname)s - %(message)s')
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@app.route('/healthcheck', methods=['GET'])
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def healthcheck():
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return {"healthy": True}
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@app.route('/listening_section_1', methods=['GET'])
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@jwt_required()
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def get_listening_section_1_question():
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try:
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delete_files_older_than_one_day(AUDIO_FILES_PATH)
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# Extract parameters from the URL query string
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topic = request.args.get('topic', default=random.choice(two_people_scenarios))
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req_exercises = request.args.getlist('exercises')
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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if (len(req_exercises) == 0):
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req_exercises = random.sample(LISTENING_EXERCISE_TYPES, 1)
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number_of_exercises_q = divide_number_into_parts(TOTAL_LISTENING_SECTION_1_EXERCISES, len(req_exercises))
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processed_conversation = generate_listening_1_conversation(topic)
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app.logger.info("Generated conversation: " + str(processed_conversation))
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start_id = 1
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exercises = generate_listening_conversation_exercises(parse_conversation(processed_conversation), req_exercises,
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number_of_exercises_q,
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start_id, difficulty)
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return {
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"exercises": exercises,
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"text": processed_conversation,
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"difficulty": difficulty
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}
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except Exception as e:
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return str(e)
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@app.route('/listening_section_2', methods=['GET'])
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@jwt_required()
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def get_listening_section_2_question():
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try:
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delete_files_older_than_one_day(AUDIO_FILES_PATH)
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# Extract parameters from the URL query string
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topic = request.args.get('topic', default=random.choice(social_monologue_contexts))
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req_exercises = request.args.getlist('exercises')
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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if (len(req_exercises) == 0):
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req_exercises = random.sample(LISTENING_EXERCISE_TYPES, 2)
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number_of_exercises_q = divide_number_into_parts(TOTAL_LISTENING_SECTION_2_EXERCISES, len(req_exercises))
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monologue = generate_listening_2_monologue(topic)
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app.logger.info("Generated monologue: " + str(monologue))
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start_id = 11
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exercises = generate_listening_monologue_exercises(str(monologue), req_exercises, number_of_exercises_q,
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start_id, difficulty)
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return {
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"exercises": exercises,
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"text": monologue,
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"difficulty": difficulty
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}
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except Exception as e:
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return str(e)
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@app.route('/listening_section_3', methods=['GET'])
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@jwt_required()
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def get_listening_section_3_question():
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try:
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delete_files_older_than_one_day(AUDIO_FILES_PATH)
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# Extract parameters from the URL query string
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topic = request.args.get('topic', default=random.choice(four_people_scenarios))
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req_exercises = request.args.getlist('exercises')
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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if (len(req_exercises) == 0):
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req_exercises = random.sample(LISTENING_EXERCISE_TYPES, 1)
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number_of_exercises_q = divide_number_into_parts(TOTAL_LISTENING_SECTION_3_EXERCISES, len(req_exercises))
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processed_conversation = generate_listening_3_conversation(topic)
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app.logger.info("Generated conversation: " + str(processed_conversation))
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start_id = 21
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exercises = generate_listening_conversation_exercises(parse_conversation(processed_conversation), req_exercises,
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number_of_exercises_q,
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start_id, difficulty)
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return {
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"exercises": exercises,
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"text": processed_conversation,
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"difficulty": difficulty
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}
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except Exception as e:
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return str(e)
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@app.route('/listening_section_4', methods=['GET'])
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@jwt_required()
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def get_listening_section_4_question():
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try:
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delete_files_older_than_one_day(AUDIO_FILES_PATH)
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# Extract parameters from the URL query string
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topic = request.args.get('topic', default=random.choice(academic_subjects))
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req_exercises = request.args.getlist('exercises')
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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if (len(req_exercises) == 0):
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req_exercises = random.sample(LISTENING_EXERCISE_TYPES, 2)
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number_of_exercises_q = divide_number_into_parts(TOTAL_LISTENING_SECTION_4_EXERCISES, len(req_exercises))
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monologue = generate_listening_4_monologue(topic)
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app.logger.info("Generated monologue: " + str(monologue))
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start_id = 31
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exercises = generate_listening_monologue_exercises(str(monologue), req_exercises, number_of_exercises_q,
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start_id, difficulty)
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return {
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"exercises": exercises,
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"text": monologue,
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"difficulty": difficulty
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}
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except Exception as e:
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return str(e)
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@app.route('/listening', methods=['POST'])
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@jwt_required()
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def save_listening():
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try:
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data = request.get_json()
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parts = data.get('parts')
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minTimer = data.get('minTimer', LISTENING_MIN_TIMER_DEFAULT)
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difficulty = data.get('difficulty', random.choice(difficulties))
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template = getListeningTemplate()
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template['difficulty'] = difficulty
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id = str(uuid.uuid4())
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for i, part in enumerate(parts, start=0):
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part_template = getListeningPartTemplate()
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file_name = str(uuid.uuid4()) + ".mp3"
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sound_file_path = AUDIO_FILES_PATH + file_name
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firebase_file_path = FIREBASE_LISTENING_AUDIO_FILES_PATH + file_name
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if "conversation" in part["text"]:
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conversation_text_to_speech(part["text"]["conversation"], sound_file_path)
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else:
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text_to_speech(part["text"], sound_file_path)
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file_url = upload_file_firebase_get_url(FIREBASE_BUCKET, firebase_file_path, sound_file_path)
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part_template["audio"]["source"] = file_url
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part_template["exercises"] = part["exercises"]
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template['parts'].append(part_template)
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if minTimer != LISTENING_MIN_TIMER_DEFAULT:
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template["minTimer"] = minTimer
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template["variant"] = ExamVariant.PARTIAL.value
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else:
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template["variant"] = ExamVariant.FULL.value
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(result, id) = save_to_db_with_id("listening", template, id)
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if result:
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return {**template, "id": id}
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else:
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raise Exception("Failed to save question: " + parts)
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except Exception as e:
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return str(e)
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@app.route('/writing_task1', methods=['POST'])
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@jwt_required()
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def grade_writing_task_1():
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try:
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data = request.get_json()
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question = data.get('question')
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answer = data.get('answer')
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if not has_words(answer):
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return {
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'comment': "The answer does not contain enough english words.",
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'overall': 0,
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'task_response': {
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'Coherence and Cohesion': 0,
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'Grammatical Range and Accuracy': 0,
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'Lexical Resource': 0,
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'Task Achievement': 0
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}
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}
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elif not has_x_words(answer, 100):
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return {
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'comment': "The answer is insufficient and too small to be graded.",
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'overall': 0,
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'task_response': {
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'Coherence and Cohesion': 0,
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'Grammatical Range and Accuracy': 0,
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'Lexical Resource': 0,
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'Task Achievement': 0
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}
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}
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else:
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messages = [
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{
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"role": "system",
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"content": ('You are a helpful assistant designed to output JSON on this format: '
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'{"perfect_answer": "example perfect answer", "comment": '
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'"comment about answer quality", "overall": 0.0, "task_response": '
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'{"Task Achievement": 0.0, "Coherence and Cohesion": 0.0, '
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'"Lexical Resource": 0.0, "Grammatical Range and Accuracy": 0.0 }')
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},
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{
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"role": "user",
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"content": ('Evaluate the given Writing Task 1 response based on the IELTS grading system, '
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'ensuring a strict assessment that penalizes errors. Deduct points for deviations '
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'from the task, and assign a score of 0 if the response fails to address the question. '
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'Additionally, provide an exemplary answer with a minimum of 150 words, along with a '
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'detailed commentary highlighting both strengths and weaknesses in the response. '
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'\n Question: "' + question + '" \n Answer: "' + answer + '"')
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},
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{
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"role": "user",
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"content": 'The perfect answer must have at least 150 words.'
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}
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]
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token_count = count_total_tokens(messages)
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response = make_openai_call(GPT_3_5_TURBO, messages, token_count,
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["comment"],
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GRADING_TEMPERATURE)
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response["overall"] = fix_writing_overall(response["overall"], response["task_response"])
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response['fixed_text'] = get_fixed_text(answer)
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return response
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except Exception as e:
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return str(e)
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@app.route('/writing_task1_general', methods=['GET'])
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@jwt_required()
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def get_writing_task_1_general_question():
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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topic = request.args.get("topic", default=random.choice(mti_topics))
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try:
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messages = [
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{
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"role": "system",
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"content": ('You are a helpful assistant designed to output JSON on this format: '
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'{"prompt": "prompt content"}')
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},
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{
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"role": "user",
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"content": ('Craft a prompt for an IELTS Writing Task 1 General Training exercise that instructs the '
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'student to compose a letter. The prompt should present a specific scenario or situation, '
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'based on the topic of "' + topic + '", requiring the student to provide information, '
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'advice, or instructions within the letter. '
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'Make sure that the generated prompt is '
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'of ' + difficulty + 'difficulty and does not contain '
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'forbidden subjects in muslim '
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'countries.')
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}
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]
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token_count = count_total_tokens(messages)
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response = make_openai_call(GPT_3_5_TURBO, messages, token_count, "prompt",
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GEN_QUESTION_TEMPERATURE)
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return {
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"question": response["prompt"].strip(),
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"difficulty": difficulty,
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"topic": topic
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}
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except Exception as e:
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return str(e)
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@app.route('/writing_task2', methods=['POST'])
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@jwt_required()
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def grade_writing_task_2():
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try:
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data = request.get_json()
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question = data.get('question')
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answer = data.get('answer')
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if not has_words(answer):
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return {
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'comment': "The answer does not contain enough english words.",
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'overall': 0,
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'task_response': {
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'Coherence and Cohesion': 0,
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'Grammatical Range and Accuracy': 0,
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'Lexical Resource': 0,
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'Task Achievement': 0
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}
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}
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elif not has_x_words(answer, 180):
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return {
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'comment': "The answer is insufficient and too small to be graded.",
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'overall': 0,
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'task_response': {
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'Coherence and Cohesion': 0,
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'Grammatical Range and Accuracy': 0,
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'Lexical Resource': 0,
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'Task Achievement': 0
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}
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}
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else:
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messages = [
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{
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"role": "system",
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"content": ('You are a helpful assistant designed to output JSON on this format: '
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'{"perfect_answer": "example perfect answer", "comment": '
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'"comment about answer quality", "overall": 0.0, "task_response": '
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'{"Task Achievement": 0.0, "Coherence and Cohesion": 0.0, '
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'"Lexical Resource": 0.0, "Grammatical Range and Accuracy": 0.0 }')
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},
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{
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"role": "user",
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"content": (
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'Evaluate the given Writing Task 2 response based on the IELTS grading system, ensuring a '
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'strict assessment that penalizes errors. Deduct points for deviations from the task, and '
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'assign a score of 0 if the response fails to address the question. Additionally, provide an '
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'exemplary answer with a minimum of 250 words, along with a detailed commentary highlighting '
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'both strengths and weaknesses in the response.'
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'\n Question: "' + question + '" \n Answer: "' + answer + '"')
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},
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{
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"role": "user",
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"content": 'The perfect answer must have at least 250 words.'
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}
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]
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token_count = count_total_tokens(messages)
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response = make_openai_call(GPT_4_O, messages, token_count, ["comment"],
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GEN_QUESTION_TEMPERATURE)
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response["overall"] = fix_writing_overall(response["overall"], response["task_response"])
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response['fixed_text'] = get_fixed_text(answer)
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return response
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except Exception as e:
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return str(e)
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def fix_writing_overall(overall: float, task_response: dict):
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if overall > max(task_response.values()) or overall < min(task_response.values()):
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total_sum = sum(task_response.values())
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average = total_sum / len(task_response.values())
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rounded_average = round(average, 0)
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return rounded_average
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return overall
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|
|
|
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@app.route('/writing_task2_general', methods=['GET'])
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@jwt_required()
|
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def get_writing_task_2_general_question():
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
|
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topic = request.args.get("topic", default=random.choice(mti_topics))
|
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try:
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messages = [
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|
{
|
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"role": "system",
|
|
"content": ('You are a helpful assistant designed to output JSON on this format: '
|
|
'{"prompt": "prompt content"}')
|
|
},
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{
|
|
"role": "user",
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"content": (
|
|
'Craft a comprehensive question of ' + difficulty + 'difficulty like the ones for IELTS Writing Task 2 General Training that directs the candidate '
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|
'to delve into an in-depth analysis of contrasting perspectives on the topic of "' + topic + '". '
|
|
'The candidate should be asked to discuss the strengths and weaknesses of both viewpoints, provide evidence or '
|
|
'examples, and present a well-rounded argument before concluding with their personal opinion on the subject.')
|
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}
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|
]
|
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token_count = count_total_tokens(messages)
|
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response = make_openai_call(GPT_4_O, messages, token_count, "prompt", GEN_QUESTION_TEMPERATURE)
|
|
return {
|
|
"question": response["prompt"].strip(),
|
|
"difficulty": difficulty,
|
|
"topic": topic
|
|
}
|
|
except Exception as e:
|
|
return str(e)
|
|
|
|
|
|
@app.route('/speaking_task_1', methods=['POST'])
|
|
@jwt_required()
|
|
def grade_speaking_task_1():
|
|
request_id = uuid.uuid4()
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|
delete_files_older_than_one_day(AUDIO_FILES_PATH)
|
|
sound_file_name = AUDIO_FILES_PATH + str(uuid.uuid4())
|
|
logging.info("POST - speaking_task_1 - Received request to grade speaking task 1. "
|
|
"Use this id to track the logs: " + str(request_id) + " - Request data: " + str(request.get_json()))
|
|
try:
|
|
data = request.get_json()
|
|
question = data.get('question')
|
|
answer_firebase_path = data.get('answer')
|
|
|
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logging.info("POST - speaking_task_1 - " + str(request_id) + " - Downloading file " + answer_firebase_path)
|
|
download_firebase_file(FIREBASE_BUCKET, answer_firebase_path, sound_file_name)
|
|
logging.info("POST - speaking_task_1 - " + str(
|
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request_id) + " - Downloaded file " + answer_firebase_path + " to " + sound_file_name)
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|
|
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answer = speech_to_text(sound_file_name)
|
|
logging.info("POST - speaking_task_1 - " + str(request_id) + " - Transcripted answer: " + answer)
|
|
|
|
json_format = {
|
|
"comment": "extensive comment about answer quality",
|
|
"overall": 0.0,
|
|
"task_response": {
|
|
"Fluency and Coherence": {
|
|
"grade": 0.0,
|
|
"comment": "extensive comment about fluency and coherence"
|
|
},
|
|
"Lexical Resource": {
|
|
"grade": 0.0,
|
|
"comment": "extensive comment about lexical resource"
|
|
},
|
|
"Grammatical Range and Accuracy": {
|
|
"grade": 0.0,
|
|
"comment": "extensive comment about grammatical range and accuracy"
|
|
},
|
|
"Pronunciation": {
|
|
"grade": 0.0,
|
|
"comment": "extensive comment about pronunciation on the transcribed answer"
|
|
}
|
|
}
|
|
}
|
|
|
|
if has_x_words(answer, 20):
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": (
|
|
'You are a helpful assistant designed to output JSON on this format: ' + str(json_format))
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": (
|
|
'Evaluate the given Speaking Part 1 response based on the IELTS grading system, ensuring a '
|
|
'strict assessment that penalizes errors. Deduct points for deviations from the task, and '
|
|
'assign a score of 0 if the response fails to address the question. Additionally, provide '
|
|
'detailed commentary highlighting both strengths and weaknesses in the response.'
|
|
'\n Question: "' + question + '" \n Answer: "' + answer + '"')
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": 'Address the student as "you"'
|
|
}
|
|
]
|
|
token_count = count_total_tokens(messages)
|
|
|
|
logging.info("POST - speaking_task_1 - " + str(request_id) + " - Requesting grading of the answer.")
|
|
response = make_openai_call(GPT_3_5_TURBO, messages, token_count, ["comment"],
|
|
GRADING_TEMPERATURE)
|
|
logging.info("POST - speaking_task_1 - " + str(request_id) + " - Answer graded: " + str(response))
|
|
|
|
perfect_answer_messages = [
|
|
{
|
|
"role": "system",
|
|
"content": ('You are a helpful assistant designed to output JSON on this format: '
|
|
'{"answer": "perfect answer"}')
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": (
|
|
'Provide a perfect answer according to ielts grading system to the following '
|
|
'Speaking Part 1 question: "' + question + '"')
|
|
}
|
|
]
|
|
token_count = count_total_tokens(perfect_answer_messages)
|
|
|
|
logging.info("POST - speaking_task_1 - " + str(request_id) + " - Requesting perfect answer.")
|
|
response['perfect_answer'] = make_openai_call(GPT_3_5_TURBO,
|
|
perfect_answer_messages,
|
|
token_count,
|
|
["answer"],
|
|
GEN_QUESTION_TEMPERATURE)["answer"]
|
|
logging.info("POST - speaking_task_1 - " + str(
|
|
request_id) + " - Perfect answer: " + response['perfect_answer'])
|
|
|
|
response['transcript'] = answer
|
|
|
|
logging.info("POST - speaking_task_1 - " + str(request_id) + " - Requesting fixed text.")
|
|
response['fixed_text'] = get_speaking_corrections(answer)
|
|
logging.info("POST - speaking_task_1 - " + str(request_id) + " - Fixed text: " + response['fixed_text'])
|
|
|
|
if response["overall"] == "0.0" or response["overall"] == 0.0:
|
|
response["overall"] = round((response["task_response"]["Fluency and Coherence"] +
|
|
response["task_response"]["Lexical Resource"] + response["task_response"][
|
|
"Grammatical Range and Accuracy"] + response["task_response"][
|
|
"Pronunciation"]) / 4, 1)
|
|
|
|
logging.info("POST - speaking_task_1 - " + str(request_id) + " - Final response: " + str(response))
|
|
return response
|
|
else:
|
|
logging.info("POST - speaking_task_1 - " + str(
|
|
request_id) + " - The answer had less words than threshold 20 to be graded. Answer: " + answer)
|
|
return {
|
|
"comment": "The audio recorded does not contain enough english words to be graded.",
|
|
"overall": 0,
|
|
"task_response": {
|
|
"Fluency and Coherence": 0,
|
|
"Lexical Resource": 0,
|
|
"Grammatical Range and Accuracy": 0,
|
|
"Pronunciation": 0
|
|
}
|
|
}
|
|
except Exception as e:
|
|
os.remove(sound_file_name)
|
|
return str(e), 400
|
|
|
|
|
|
@app.route('/speaking_task_1', methods=['GET'])
|
|
@jwt_required()
|
|
def get_speaking_task_1_question():
|
|
difficulty = request.args.get("difficulty", default=random.choice(difficulties))
|
|
topic = request.args.get("topic", default=random.choice(mti_topics))
|
|
try:
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": (
|
|
'You are a helpful assistant designed to output JSON on this format: '
|
|
'{"topic": "topic", "question": "question"}')
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": (
|
|
'Craft a thought-provoking question of ' + difficulty + ' difficulty for IELTS Speaking Part 1 '
|
|
'that encourages candidates to delve deeply into '
|
|
'personal experiences, preferences, or insights on the topic '
|
|
'of "' + topic + '". Instruct the candidate '
|
|
'to offer not only detailed '
|
|
'descriptions but also provide '
|
|
'nuanced explanations, examples, '
|
|
'or anecdotes to enrich their response. '
|
|
'Make sure that the generated question '
|
|
'does not contain forbidden subjects in '
|
|
'muslim countries.')
|
|
}
|
|
]
|
|
token_count = count_total_tokens(messages)
|
|
response = make_openai_call(GPT_4_O, messages, token_count, ["topic"],
|
|
GEN_QUESTION_TEMPERATURE)
|
|
response["type"] = 1
|
|
response["difficulty"] = difficulty
|
|
response["topic"] = topic
|
|
return response
|
|
except Exception as e:
|
|
return str(e)
|
|
|
|
|
|
@app.route('/speaking_task_2', methods=['POST'])
|
|
@jwt_required()
|
|
def grade_speaking_task_2():
|
|
request_id = uuid.uuid4()
|
|
delete_files_older_than_one_day(AUDIO_FILES_PATH)
|
|
sound_file_name = AUDIO_FILES_PATH + str(uuid.uuid4())
|
|
logging.info("POST - speaking_task_2 - Received request to grade speaking task 2. "
|
|
"Use this id to track the logs: " + str(request_id) + " - Request data: " + str(request.get_json()))
|
|
try:
|
|
data = request.get_json()
|
|
question = data.get('question')
|
|
answer_firebase_path = data.get('answer')
|
|
|
|
logging.info("POST - speaking_task_2 - " + str(request_id) + " - Downloading file " + answer_firebase_path)
|
|
download_firebase_file(FIREBASE_BUCKET, answer_firebase_path, sound_file_name)
|
|
logging.info("POST - speaking_task_2 - " + str(
|
|
request_id) + " - Downloaded file " + answer_firebase_path + " to " + sound_file_name)
|
|
|
|
answer = speech_to_text(sound_file_name)
|
|
logging.info("POST - speaking_task_2 - " + str(request_id) + " - Transcripted answer: " + answer)
|
|
|
|
json_format = {
|
|
"comment": "extensive comment about answer quality",
|
|
"overall": 0.0,
|
|
"task_response": {
|
|
"Fluency and Coherence": {
|
|
"grade": 0.0,
|
|
"comment": "extensive comment about fluency and coherence"
|
|
},
|
|
"Lexical Resource": {
|
|
"grade": 0.0,
|
|
"comment": "extensive comment about lexical resource"
|
|
},
|
|
"Grammatical Range and Accuracy": {
|
|
"grade": 0.0,
|
|
"comment": "extensive comment about grammatical range and accuracy"
|
|
},
|
|
"Pronunciation": {
|
|
"grade": 0.0,
|
|
"comment": "extensive comment about pronunciation on the transcribed answer"
|
|
}
|
|
}
|
|
}
|
|
|
|
if has_x_words(answer, 20):
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": (
|
|
'You are a helpful assistant designed to output JSON on this format: ' + str(json_format))
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": (
|
|
'Evaluate the given Speaking Part 2 response based on the IELTS grading system, ensuring a '
|
|
'strict assessment that penalizes errors. Deduct points for deviations from the task, and '
|
|
'assign a score of 0 if the response fails to address the question. Additionally, provide '
|
|
'detailed commentary highlighting both strengths and weaknesses in the response.'
|
|
'\n Question: "' + question + '" \n Answer: "' + answer + '"')
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": 'Address the student as "you"'
|
|
}
|
|
]
|
|
token_count = count_total_tokens(messages)
|
|
|
|
logging.info("POST - speaking_task_2 - " + str(request_id) + " - Requesting grading of the answer.")
|
|
response = make_openai_call(GPT_3_5_TURBO, messages, token_count, ["comment"],
|
|
GRADING_TEMPERATURE)
|
|
logging.info("POST - speaking_task_2 - " + str(request_id) + " - Answer graded: " + str(response))
|
|
|
|
perfect_answer_messages = [
|
|
{
|
|
"role": "system",
|
|
"content": ('You are a helpful assistant designed to output JSON on this format: '
|
|
'{"answer": "perfect answer"}')
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": (
|
|
'Provide a perfect answer according to ielts grading system to the following '
|
|
'Speaking Part 2 question: "' + question + '"')
|
|
}
|
|
]
|
|
token_count = count_total_tokens(perfect_answer_messages)
|
|
|
|
logging.info("POST - speaking_task_2 - " + str(request_id) + " - Requesting perfect answer.")
|
|
response['perfect_answer'] = make_openai_call(GPT_3_5_TURBO,
|
|
perfect_answer_messages,
|
|
token_count,
|
|
["answer"],
|
|
GEN_QUESTION_TEMPERATURE)["answer"]
|
|
logging.info("POST - speaking_task_2 - " + str(
|
|
request_id) + " - Perfect answer: " + response['perfect_answer'])
|
|
|
|
response['transcript'] = answer
|
|
|
|
logging.info("POST - speaking_task_2 - " + str(request_id) + " - Requesting fixed text.")
|
|
response['fixed_text'] = get_speaking_corrections(answer)
|
|
logging.info("POST - speaking_task_2 - " + str(request_id) + " - Fixed text: " + response['fixed_text'])
|
|
|
|
if response["overall"] == "0.0" or response["overall"] == 0.0:
|
|
response["overall"] = round((response["task_response"]["Fluency and Coherence"] +
|
|
response["task_response"]["Lexical Resource"] + response["task_response"][
|
|
"Grammatical Range and Accuracy"] + response["task_response"][
|
|
"Pronunciation"]) / 4, 1)
|
|
|
|
logging.info("POST - speaking_task_2 - " + str(request_id) + " - Final response: " + str(response))
|
|
return response
|
|
else:
|
|
logging.info("POST - speaking_task_2 - " + str(
|
|
request_id) + " - The answer had less words than threshold 20 to be graded. Answer: " + answer)
|
|
return {
|
|
"comment": "The audio recorded does not contain enough english words to be graded.",
|
|
"overall": 0,
|
|
"task_response": {
|
|
"Fluency and Coherence": 0,
|
|
"Lexical Resource": 0,
|
|
"Grammatical Range and Accuracy": 0,
|
|
"Pronunciation": 0
|
|
}
|
|
}
|
|
except Exception as e:
|
|
os.remove(sound_file_name)
|
|
return str(e), 400
|
|
|
|
|
|
@app.route('/speaking_task_2', methods=['GET'])
|
|
@jwt_required()
|
|
def get_speaking_task_2_question():
|
|
difficulty = request.args.get("difficulty", default=random.choice(difficulties))
|
|
topic = request.args.get("topic", default=random.choice(mti_topics))
|
|
try:
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": (
|
|
'You are a helpful assistant designed to output JSON on this format: '
|
|
'{"topic": "topic", "question": "question", "prompts": ["prompt_1", "prompt_2", "prompt_3"]}')
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": (
|
|
'Create a question of ' + difficulty + ' difficulty for IELTS Speaking Part 2 '
|
|
'that encourages candidates to narrate a '
|
|
'personal experience or story related to the topic '
|
|
'of "' + topic + '". Include 3 prompts that '
|
|
'guide the candidate to describe '
|
|
'specific aspects of the experience, '
|
|
'such as details about the situation, '
|
|
'their actions, and the reasons it left a '
|
|
'lasting impression. Make sure that the '
|
|
'generated question does not contain '
|
|
'forbidden subjects in muslim countries.')
|
|
}
|
|
]
|
|
token_count = count_total_tokens(messages)
|
|
response = make_openai_call(GPT_4_O, messages, token_count, GEN_FIELDS, GEN_QUESTION_TEMPERATURE)
|
|
response["type"] = 2
|
|
response["difficulty"] = difficulty
|
|
response["topic"] = topic
|
|
return response
|
|
except Exception as e:
|
|
return str(e)
|
|
|
|
|
|
@app.route('/speaking_task_3', methods=['GET'])
|
|
@jwt_required()
|
|
def get_speaking_task_3_question():
|
|
difficulty = request.args.get("difficulty", default=random.choice(difficulties))
|
|
topic = request.args.get("topic", default=random.choice(mti_topics))
|
|
try:
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": (
|
|
'You are a helpful assistant designed to output JSON on this format: '
|
|
'{"topic": "topic", "questions": ["question", "question", "question"]}')
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": (
|
|
'Formulate a set of 3 questions of ' + difficulty + ' difficulty for IELTS Speaking Part 3 that encourage candidates to engage in a '
|
|
'meaningful discussion on the topic of "' + topic + '". Provide inquiries, ensuring '
|
|
'they explore various aspects, perspectives, and implications related to the topic.'
|
|
'Make sure that the generated question does not contain forbidden subjects in muslim countries.')
|
|
|
|
}
|
|
]
|
|
token_count = count_total_tokens(messages)
|
|
response = make_openai_call(GPT_4_O, messages, token_count, GEN_FIELDS, GEN_QUESTION_TEMPERATURE)
|
|
# Remove the numbers from the questions only if the string starts with a number
|
|
response["questions"] = [re.sub(r"^\d+\.\s*", "", question) if re.match(r"^\d+\.", question) else question for
|
|
question in response["questions"]]
|
|
response["type"] = 3
|
|
response["difficulty"] = difficulty
|
|
response["topic"] = topic
|
|
return response
|
|
except Exception as e:
|
|
return str(e)
|
|
|
|
|
|
@app.route('/speaking_task_3', methods=['POST'])
|
|
@jwt_required()
|
|
def grade_speaking_task_3():
|
|
request_id = uuid.uuid4()
|
|
delete_files_older_than_one_day(AUDIO_FILES_PATH)
|
|
logging.info("POST - speaking_task_3 - Received request to grade speaking task 3. "
|
|
"Use this id to track the logs: " + str(request_id) + " - Request data: " + str(request.get_json()))
|
|
try:
|
|
data = request.get_json()
|
|
answers = data.get('answers')
|
|
text_answers = []
|
|
perfect_answers = []
|
|
logging.info("POST - speaking_task_3 - " + str(
|
|
request_id) + " - Received " + str(len(answers)) + " total answers.")
|
|
for item in answers:
|
|
sound_file_name = AUDIO_FILES_PATH + str(uuid.uuid4())
|
|
|
|
logging.info("POST - speaking_task_3 - " + str(request_id) + " - Downloading file " + item["answer"])
|
|
download_firebase_file(FIREBASE_BUCKET, item["answer"], sound_file_name)
|
|
logging.info("POST - speaking_task_3 - " + str(
|
|
request_id) + " - Downloaded file " + item["answer"] + " to " + sound_file_name)
|
|
|
|
answer_text = speech_to_text(sound_file_name)
|
|
logging.info("POST - speaking_task_3 - " + str(request_id) + " - Transcripted answer: " + answer_text)
|
|
|
|
text_answers.append(answer_text)
|
|
item["answer"] = answer_text
|
|
os.remove(sound_file_name)
|
|
if not has_x_words(answer_text, 20):
|
|
logging.info("POST - speaking_task_3 - " + str(
|
|
request_id) + " - The answer had less words than threshold 20 to be graded. Answer: " + answer_text)
|
|
return {
|
|
"comment": "The audio recorded does not contain enough english words to be graded.",
|
|
"overall": 0,
|
|
"task_response": {
|
|
"Fluency and Coherence": 0,
|
|
"Lexical Resource": 0,
|
|
"Grammatical Range and Accuracy": 0,
|
|
"Pronunciation": 0
|
|
}
|
|
}
|
|
|
|
perfect_answer_messages = [
|
|
{
|
|
"role": "system",
|
|
"content": ('You are a helpful assistant designed to output JSON on this format: '
|
|
'{"answer": "perfect answer"}')
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": (
|
|
'Provide a perfect answer according to ielts grading system to the following '
|
|
'Speaking Part 3 question: "' + item["question"] + '"')
|
|
}
|
|
]
|
|
token_count = count_total_tokens(perfect_answer_messages)
|
|
logging.info("POST - speaking_task_3 - " + str(
|
|
request_id) + " - Requesting perfect answer for question: " + item["question"])
|
|
perfect_answers.append(make_openai_call(GPT_3_5_TURBO,
|
|
perfect_answer_messages,
|
|
token_count,
|
|
["answer"],
|
|
GEN_QUESTION_TEMPERATURE))
|
|
json_format = {
|
|
"comment": "extensive comment about answer quality",
|
|
"overall": 0.0,
|
|
"task_response": {
|
|
"Fluency and Coherence": {
|
|
"grade": 0.0,
|
|
"comment": "extensive comment about fluency and coherence"
|
|
},
|
|
"Lexical Resource": {
|
|
"grade": 0.0,
|
|
"comment": "extensive comment about lexical resource"
|
|
},
|
|
"Grammatical Range and Accuracy": {
|
|
"grade": 0.0,
|
|
"comment": "extensive comment about grammatical range and accuracy"
|
|
},
|
|
"Pronunciation": {
|
|
"grade": 0.0,
|
|
"comment": "extensive comment about pronunciation on the transcribed answer"
|
|
}
|
|
}
|
|
}
|
|
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": (
|
|
'You are a helpful assistant designed to output JSON on this format: ' + str(json_format))
|
|
}
|
|
]
|
|
message = (
|
|
"Evaluate the given Speaking Part 3 response based on the IELTS grading system, ensuring a "
|
|
"strict assessment that penalizes errors. Deduct points for deviations from the task, and "
|
|
"assign a score of 0 if the response fails to address the question. Additionally, provide detailed "
|
|
"commentary highlighting both strengths and weaknesses in the response."
|
|
"\n\n The questions and answers are: \n\n'")
|
|
|
|
logging.info("POST - speaking_task_3 - " + str(request_id) + " - Formatting answers and questions for prompt.")
|
|
formatted_text = ""
|
|
for i, entry in enumerate(answers, start=1):
|
|
formatted_text += f"**Question {i}:**\n{entry['question']}\n\n"
|
|
formatted_text += f"**Answer {i}:**\n{entry['answer']}\n\n"
|
|
logging.info("POST - speaking_task_3 - " + str(
|
|
request_id) + " - Formatted answers and questions for prompt: " + formatted_text)
|
|
|
|
message += formatted_text
|
|
|
|
messages.append({
|
|
"role": "user",
|
|
"content": message
|
|
})
|
|
|
|
messages.append({
|
|
"role": "user",
|
|
"content": 'Address the student as "you"'
|
|
})
|
|
|
|
token_count = count_total_tokens(messages)
|
|
|
|
logging.info("POST - speaking_task_3 - " + str(request_id) + " - Requesting grading of the answers.")
|
|
response = make_openai_call(GPT_3_5_TURBO, messages, token_count, ["comment"], GRADING_TEMPERATURE)
|
|
logging.info("POST - speaking_task_3 - " + str(request_id) + " - Answers graded: " + str(response))
|
|
|
|
logging.info("POST - speaking_task_3 - " + str(request_id) + " - Adding perfect answers to response.")
|
|
for i, answer in enumerate(perfect_answers, start=1):
|
|
response['perfect_answer_' + str(i)] = answer
|
|
|
|
logging.info("POST - speaking_task_3 - " + str(
|
|
request_id) + " - Adding transcript and fixed texts to response.")
|
|
for i, answer in enumerate(text_answers, start=1):
|
|
response['transcript_' + str(i)] = answer
|
|
response['fixed_text_' + str(i)] = get_speaking_corrections(answer)
|
|
if response["overall"] == "0.0" or response["overall"] == 0.0:
|
|
response["overall"] = round((response["task_response"]["Fluency and Coherence"] + response["task_response"][
|
|
"Lexical Resource"] + response["task_response"]["Grammatical Range and Accuracy"] +
|
|
response["task_response"]["Pronunciation"]) / 4, 1)
|
|
logging.info("POST - speaking_task_3 - " + str(request_id) + " - Final response: " + str(response))
|
|
return response
|
|
except Exception as e:
|
|
return str(e), 400
|
|
|
|
|
|
@app.route('/speaking', methods=['POST'])
|
|
@jwt_required()
|
|
def save_speaking():
|
|
try:
|
|
data = request.get_json()
|
|
exercises = data.get('exercises')
|
|
minTimer = data.get('minTimer', SPEAKING_MIN_TIMER_DEFAULT)
|
|
template = getSpeakingTemplate()
|
|
template["minTimer"] = minTimer
|
|
|
|
if minTimer < SPEAKING_MIN_TIMER_DEFAULT:
|
|
template["variant"] = ExamVariant.PARTIAL.value
|
|
else:
|
|
template["variant"] = ExamVariant.FULL.value
|
|
|
|
id = str(uuid.uuid4())
|
|
app.logger.info('Received request to save speaking with id: ' + id)
|
|
thread_event.set()
|
|
thread = threading.Thread(
|
|
target=create_videos_and_save_to_db,
|
|
args=(exercises, template, id),
|
|
name=("thread-save-speaking-" + id)
|
|
)
|
|
thread.start()
|
|
app.logger.info('Started thread to save speaking. Thread: ' + thread.getName())
|
|
|
|
# Return response without waiting for create_videos_and_save_to_db to finish
|
|
return {**template, "id": id}
|
|
except Exception as e:
|
|
return str(e)
|
|
|
|
|
|
@app.route("/speaking/generate_speaking_video", methods=['POST'])
|
|
@jwt_required()
|
|
def generate_speaking_video():
|
|
try:
|
|
data = request.get_json()
|
|
avatar = data.get("avatar", random.choice(list(AvatarEnum)).value)
|
|
prompts = data.get("prompts", [])
|
|
question = data.get("question")
|
|
if len(prompts) > 0:
|
|
question = question + " In your answer you should consider: " + " ".join(prompts)
|
|
sp1_result = create_video(question, avatar)
|
|
if sp1_result is not None:
|
|
sound_file_path = VIDEO_FILES_PATH + sp1_result
|
|
firebase_file_path = FIREBASE_SPEAKING_VIDEO_FILES_PATH + sp1_result
|
|
url = upload_file_firebase_get_url(FIREBASE_BUCKET, firebase_file_path, sound_file_path)
|
|
sp1_video_path = firebase_file_path
|
|
sp1_video_url = url
|
|
|
|
return {
|
|
"text": data["question"],
|
|
"prompts": prompts,
|
|
"title": data["topic"],
|
|
"video_url": sp1_video_url,
|
|
"video_path": sp1_video_path,
|
|
"type": "speaking",
|
|
"id": uuid.uuid4()
|
|
}
|
|
else:
|
|
app.logger.error("Failed to create video for part 1 question: " + data["question"])
|
|
return str("Failed to create video for part 1 question: " + data["question"])
|
|
|
|
except Exception as e:
|
|
return str(e)
|
|
|
|
|
|
@app.route("/speaking/generate_interactive_video", methods=['POST'])
|
|
@jwt_required()
|
|
def generate_interactive_video():
|
|
try:
|
|
data = request.get_json()
|
|
sp3_questions = []
|
|
avatar = data.get("avatar", random.choice(list(AvatarEnum)).value)
|
|
|
|
app.logger.info('Creating videos for speaking part 3')
|
|
for question in data["questions"]:
|
|
result = create_video(question, avatar)
|
|
if result is not None:
|
|
sound_file_path = VIDEO_FILES_PATH + result
|
|
firebase_file_path = FIREBASE_SPEAKING_VIDEO_FILES_PATH + result
|
|
url = upload_file_firebase_get_url(FIREBASE_BUCKET, firebase_file_path, sound_file_path)
|
|
video = {
|
|
"text": question,
|
|
"video_path": firebase_file_path,
|
|
"video_url": url
|
|
}
|
|
sp3_questions.append(video)
|
|
else:
|
|
app.app.logger.error("Failed to create video for part 3 question: " + question)
|
|
|
|
return {
|
|
"prompts": sp3_questions,
|
|
"title": data["topic"],
|
|
"type": "interactiveSpeaking",
|
|
"id": uuid.uuid4()
|
|
}
|
|
except Exception as e:
|
|
return str(e)
|
|
|
|
|
|
@app.route('/reading_passage_1', methods=['GET'])
|
|
@jwt_required()
|
|
def get_reading_passage_1_question():
|
|
try:
|
|
# Extract parameters from the URL query string
|
|
topic = request.args.get('topic', default=random.choice(topics))
|
|
req_exercises = request.args.getlist('exercises')
|
|
difficulty = request.args.get("difficulty", default=random.choice(difficulties))
|
|
return gen_reading_passage_1(topic, req_exercises, difficulty)
|
|
except Exception as e:
|
|
return str(e)
|
|
|
|
|
|
@app.route('/reading_passage_2', methods=['GET'])
|
|
@jwt_required()
|
|
def get_reading_passage_2_question():
|
|
try:
|
|
# Extract parameters from the URL query string
|
|
topic = request.args.get('topic', default=random.choice(topics))
|
|
req_exercises = request.args.getlist('exercises')
|
|
difficulty = request.args.get("difficulty", default=random.choice(difficulties))
|
|
return gen_reading_passage_2(topic, req_exercises, difficulty)
|
|
except Exception as e:
|
|
return str(e)
|
|
|
|
|
|
@app.route('/reading_passage_3', methods=['GET'])
|
|
@jwt_required()
|
|
def get_reading_passage_3_question():
|
|
try:
|
|
# Extract parameters from the URL query string
|
|
topic = request.args.get('topic', default=random.choice(topics))
|
|
req_exercises = request.args.getlist('exercises')
|
|
difficulty = request.args.get("difficulty", default=random.choice(difficulties))
|
|
return gen_reading_passage_3(topic, req_exercises, difficulty)
|
|
except Exception as e:
|
|
return str(e)
|
|
|
|
|
|
@app.route('/level', methods=['GET'])
|
|
@jwt_required()
|
|
def get_level_exam():
|
|
try:
|
|
number_of_exercises = 25
|
|
exercises = gen_multiple_choice_level(number_of_exercises)
|
|
return {
|
|
"exercises": [exercises],
|
|
"isDiagnostic": False,
|
|
"minTimer": 25,
|
|
"module": "level"
|
|
}
|
|
except Exception as e:
|
|
return str(e)
|
|
|
|
@app.route('/level_utas', methods=['GET'])
|
|
@jwt_required()
|
|
def get_level_utas():
|
|
try:
|
|
# Formats
|
|
mc = {
|
|
"id": str(uuid.uuid4()),
|
|
"prompt": "Choose the correct word or group of words that completes the sentences.",
|
|
"questions": None,
|
|
"type": "multipleChoice",
|
|
"part": 1
|
|
}
|
|
|
|
umc = {
|
|
"id": str(uuid.uuid4()),
|
|
"prompt": "Choose the underlined word or group of words that is not correct.",
|
|
"questions": None,
|
|
"type": "multipleChoice",
|
|
"part": 2
|
|
}
|
|
|
|
bs_1 = {
|
|
"id": str(uuid.uuid4()),
|
|
"prompt": "Read the text and write the correct word for each space.",
|
|
"questions": None,
|
|
"type": "blankSpaceText",
|
|
"part": 3
|
|
}
|
|
|
|
bs_2 = {
|
|
"id": str(uuid.uuid4()),
|
|
"prompt": "Read the text and write the correct word for each space.",
|
|
"questions": None,
|
|
"type": "blankSpaceText",
|
|
"part": 4
|
|
}
|
|
|
|
reading = {
|
|
"id": str(uuid.uuid4()),
|
|
"prompt": "Read the text and answer the questions below.",
|
|
"questions": None,
|
|
"type": "readingExercises",
|
|
"part": 5
|
|
}
|
|
|
|
all_mc_questions = []
|
|
|
|
# PART 1
|
|
mc_exercises1 = gen_multiple_choice_blank_space_utas(15, 1, all_mc_questions)
|
|
print(json.dumps(mc_exercises1, indent=4))
|
|
all_mc_questions.append(mc_exercises1)
|
|
|
|
# PART 2
|
|
mc_exercises2 = gen_multiple_choice_blank_space_utas(15, 16, all_mc_questions)
|
|
print(json.dumps(mc_exercises2, indent=4))
|
|
all_mc_questions.append(mc_exercises2)
|
|
|
|
# PART 3
|
|
mc_exercises3 = gen_multiple_choice_blank_space_utas(15, 31, all_mc_questions)
|
|
print(json.dumps(mc_exercises3, indent=4))
|
|
all_mc_questions.append(mc_exercises3)
|
|
|
|
mc_exercises = mc_exercises1['questions'] + mc_exercises2['questions'] + mc_exercises3['questions']
|
|
print(json.dumps(mc_exercises, indent=4))
|
|
mc["questions"] = mc_exercises
|
|
|
|
# Underlined mc
|
|
underlined_mc = gen_multiple_choice_underlined_utas(15, 46)
|
|
print(json.dumps(underlined_mc, indent=4))
|
|
umc["questions"] = underlined_mc
|
|
|
|
# Blank Space text 1
|
|
blank_space_text_1 = gen_blank_space_text_utas(12, 61, 250)
|
|
print(json.dumps(blank_space_text_1, indent=4))
|
|
bs_1["questions"] = blank_space_text_1
|
|
|
|
# Blank Space text 2
|
|
blank_space_text_2 = gen_blank_space_text_utas(14, 73, 350)
|
|
print(json.dumps(blank_space_text_2, indent=4))
|
|
bs_2["questions"] = blank_space_text_2
|
|
|
|
# Reading text
|
|
reading_text = gen_reading_passage_utas(87, 10, 4)
|
|
print(json.dumps(reading_text, indent=4))
|
|
reading["questions"] = reading_text
|
|
|
|
return {
|
|
"exercises": {
|
|
"blankSpaceMultipleChoice": mc,
|
|
"underlinedMultipleChoice": umc,
|
|
"blankSpaceText1": bs_1,
|
|
"blankSpaceText2": bs_2,
|
|
"readingExercises": reading,
|
|
},
|
|
"isDiagnostic": False,
|
|
"minTimer": 25,
|
|
"module": "level"
|
|
}
|
|
except Exception as e:
|
|
return str(e)
|
|
|
|
|
|
@app.route('/fetch_tips', methods=['POST'])
|
|
@jwt_required()
|
|
def fetch_answer_tips():
|
|
try:
|
|
data = request.get_json()
|
|
context = data.get('context')
|
|
question = data.get('question')
|
|
answer = data.get('answer')
|
|
correct_answer = data.get('correct_answer')
|
|
messages = get_question_tips(question, answer, correct_answer, context)
|
|
token_count = reduce(lambda count, item: count + count_tokens(item)['n_tokens'],
|
|
map(lambda x: x["content"], filter(lambda x: "content" in x, messages)), 0)
|
|
response = make_openai_call(GPT_3_5_TURBO, messages, token_count, None, TIPS_TEMPERATURE)
|
|
|
|
if isinstance(response, str):
|
|
response = re.sub(r"^[a-zA-Z0-9_]+\:\s*", "", response)
|
|
|
|
return response
|
|
except Exception as e:
|
|
return str(e)
|
|
|
|
|
|
@app.route('/grading_summary', methods=['POST'])
|
|
@jwt_required()
|
|
def grading_summary():
|
|
# Body Format
|
|
# {'sections': Array of {'code': key, 'name': name, 'grade': grade}}
|
|
# Output Format
|
|
# {'sections': Array of {'code': key, 'name': name, 'grade': grade, 'evaluation': evaluation, 'suggestions': suggestions}}
|
|
try:
|
|
return calculate_grading_summary(request.get_json())
|
|
except Exception as e:
|
|
return str(e)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
app.run()
|