ThinkingParrot (有脑鹦鹉) Backend

Django-based API Service for ThinkingParrot AI Learning Platform

Backend service powering the ThinkingParrot English learning platform, providing RESTful APIs for quiz management, speech recognition, AI chatbot integration, and user progress tracking.

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Related Repositories

• ThinkingParrot - Main project overview, learning methodology, and documentation
• ThinkingParrot-Miniprogram - WeChat Mini Program frontend
• Language-Chatbot-3.0 - CNO-LSTM chatbot engine for Level 6

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Tech Stack

• Python 3.7+ / Django 2.2.5 - Backend framework
• SQLite (dev) / MySQL (prod) - Database
• PyTorch - Deep learning framework
• CNO-LSTM - Chaotic Neural Oscillatory LSTM for chatbot
• NLTK / Word2Vec - NLP for answer judging
• Google Cloud - Speech-to-Text & Text-to-Speech APIs
• WeChat API - User authentication

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Project Structure

ThinkingParrot-Backend/
│
├── Chatbot/                    # CNO-LSTM chatbot module
│   ├── ChaoticLSTM.py
│   ├── Chatbot.py
│   └── LeeOscillator.py
│
├── miniproject/                # Django project
│   ├── manage.py
│   │
│   ├── miniproject/            # Project settings
│   │   ├── settings.py
│   │   ├── urls.py
│   │   ├── views.py            # Chatbot API (Level 6)
│   │   └── wsgi.py
│   │
│   ├── questionRecord/         # Main app
│   │   ├── models.py           # 9 database models
│   │   ├── views.py            # 26 API functions
│   │   ├── urls.py
│   │   ├── audioRecognize.py   # Speech recognition & NLP judging
│   │   ├── forms.py
│   │   └── migrations/         # Database migrations
│   │
│   ├── static/                 # Static files (CSS, images)
│   └── template/               # HTML templates & Excel files
│
├── diagrams/                   # Architecture diagrams
├── requirements.txt            # Python dependencies
├── .gitignore                  # Git ignore rules
└── README.md                   # This file

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Key Features

Audio Answer Check (Level 4)

• Speech-to-Text via Google Cloud
• NLP-based judging using Word2Vec
• 75% similarity threshold
• Real-time feedback

AI Chatbot (Level 6)

• Powered by Language-Chatbot-3.0
• CNO-LSTM for natural conversations
• Context-aware responses
• Realistic English practice

Content Management

• Upload lectures via Excel files
• Bulk database operations
• Dynamic question generation

Gamification

• Rankings & leaderboards
• Daily check-ins
• Progress tracking
• Wrong answer review

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API Endpoints

Chatbot

• POST /ChatbotGetMessage/ - AI conversation (Level 6)

User Management

• POST /questionRecord/userinfo - Login/registration
• POST /questionRecord/getUserInformation/ - User profile
• POST /questionRecord/getUserRank/ - User rank
• GET /questionRecord/getRank/ - Top 100 leaderboard
• POST /questionRecord/signAddScore/ - Daily check-in

Learning & Questions

• GET /questionRecord/GetLectures/ - Available lectures
• POST /questionRecord/getNewQuestion/ - Get questions by level
• POST /questionRecord/getOneQuesiton/ - Single question
• POST /questionRecord/getWrongQuestion/ - Wrong questions
• POST /questionRecord/judgeAnswer/ - Judge answer (text/audio)
• POST /questionRecord/recordAnswer/ - Record result
• POST /questionRecord/correctAnswer/ - Mark corrected

Progress & Collections

• POST /questionRecord/getHistoryNum/ - History count
• POST /questionRecord/getWrongNum/ - Wrong count
• POST /questionRecord/getNotesCollection/ - Get collections
• POST /questionRecord/toCollect/ - Add to collection
• POST /questionRecord/toCancelCollect/ - Remove from collection

Text-to-Speech

• GET /questionRecord/textToSpeechEN/ - Generate English audio (MP3)
• GET /questionRecord/textToSpeechEN_CN/ - Generate audio (SSML)

Admin

• POST /questionRecord/lectureUpdate - Upload/update lectures
• POST /questionRecord/refreshDatabase/ - Refresh from Excel
• POST /questionRecord/addNewQuestion/ - Add questions
• POST /questionRecord/recognize - Direct audio recognition

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Database Models

9 main models power the learning system:

Model	Purpose
Unit	Learning units/lectures
Concept	English concepts
Example	Example sentences
Level2, Level3, Level4	Question types
CommonUser	User profiles
Progress	Learning progress
History	Question history
Wrong	Wrong answers
NotesCollection	Collections
DailyRank	Rankings

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Installation

Prerequisites

• Python 3.7+
• Google Cloud account (Speech & TTS APIs)
• WeChat Mini Program account
• MySQL (production) or SQLite (development)

Setup

# 1. Clone repository
git clone https://github.com/liafonx/ThinkingParrot-Backend.git
cd ThinkingParrot-Backend

# 2. Install dependencies
pip install -r requirements.txt

# 3. Create key directory and add your credentials
cd miniproject
mkdir key
# Add these files to key/:
#   - django.key (Django secret key)
#   - miniprogram.key (WeChat app secret)
#   - database.json (Database config)
#   - radiant-snow-api.json (Google Cloud service account)

# 4. Run migrations
python manage.py migrate

# 5. Start server
python manage.py runserver

Server runs at http://localhost:8000/

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For project overview and learning methodology, visit ThinkingParrot