# Implementation Plan for a Full-Stack AI Customer Service Chatbot Based on Dual-Model Architecture

> This article introduces a complete student project that demonstrates how to build a dual-model AI customer service system combining Naive Bayes and artificial neural networks, covering full-process technical details from NLP preprocessing to production deployment.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-31T10:39:43.000Z
- 最近活动: 2026-05-31T10:51:05.767Z
- 热度: 150.8
- 关键词: AI客服, 聊天机器人, 朴素贝叶斯, 神经网络, 意图识别, NLP, Flask, 全栈开发
- 页面链接: https://www.zingnex.cn/en/forum/thread/ai-9c71912d
- Canonical: https://www.zingnex.cn/forum/thread/ai-9c71912d
- Markdown 来源: floors_fallback

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## Project Guide for Full-Stack AI Customer Service Chatbot Based on Dual-Model Architecture

This article introduces a student project from the Artificial Intelligence course of Bahauddin Zakariya University (Pakistan) in the Spring 2026 semester, demonstrating how to build a dual-model AI customer service system combining Naive Bayes and Artificial Neural Network (ANN). The project covers full-process technical details from NLP preprocessing to production deployment, with a tech stack including Python, NLTK, Scikit-learn, Keras, Flask, Supabase, etc., aiming to provide a flexible and efficient solution for customer service intent recognition.

## Project Background and Dataset Description

**Project Background**: This project is a term project for the Artificial Intelligence course of Bahauddin Zakariya University (Pakistan) in the Spring 2026 semester, developed and maintained by WaleedChughtai-106. The source code is hosted on GitHub (link: https://github.com/WaleedChughtai-106/AI-Customer-Support-Chatbot).
**Dataset**: Adopts Bitext's customer service intent dataset, containing 21,534 user utterances covering 27 customer service scenario intents (e.g., order, account, product, payment-related). The dataset is split into 70% training, 15% validation, and 15% testing to ensure the reliability of model evaluation.

## Detailed Explanation of NLP Preprocessing Pipeline

The project implements a complete text preprocessing pipeline:
1. **Tokenization**: Use NLTK's punkt tokenizer to split words;
2. **Cleaning and Standardization**: Convert to lowercase, remove punctuation/special characters, filter numbers;
3. **Stopword Removal**: Use NLTK's stopword list to remove high-frequency low-information words;
4. **Lemmatization**: Reduce words to their base form via WordNet (e.g., running→run).
This pipeline ensures the规范性 of input text and lays the foundation for subsequent feature extraction and model training.

## Dual-Model Architecture Design and Switching Mechanism

**Dual-Model Design**:
- **Naive Bayes Model**: An efficient classification algorithm based on probability theory, with a test set accuracy of 98.58%. Its advantages include fast training/inference, low memory usage, and suitability for resource-constrained environments;
- **ANN Model**: A multi-layer perceptron built with Keras, with an accuracy of 98.45%. Its advantages include capturing complex non-linear patterns and strong expressive power.
**Switching Mechanism**: Flexibly select models via the environment variable `MODEL_TYPE` (optional values: nb/ann/knn), supporting A/B testing or canary release in production environments.

## System Architecture and API Design

**Three-Layer Architecture**:
- **Data Layer**: Supabase PostgreSQL stores chat history (user messages, replies, intents, confidence scores, etc.), with a session_id index to improve query efficiency;
- **Business Layer**: Flask backend provides RESTful APIs:
  - `POST /api/chat`: Receive messages and return replies, intents, and confidence scores;
  - `GET /api/history/<session_id>`: Query session history;
  - `GET /api/status`: System health check;
- **Presentation Layer**: Native HTML/CSS/JS chat interface, supporting model switching and session statistics display.

## Deployment and Testing Plan

**Deployment Process**:
1. Push code to GitHub;
2. Create a Web Service on Render platform and link to the repository;
3. Configure environment variables (FLASK_SECRET_KEY, SUPABASE_URL, etc.);
4. Complete automatic deployment (Render automatically installs dependencies and downloads NLTK corpora).
**High Availability**: Automatically fall back to Naive Bayes when ANN is unavailable;
**Testing**: 38 automated tests (18 unit tests to verify preprocessing, 20 integration tests to verify API behavior), executed using pytest.

## Performance Evaluation and Project Summary

**Performance Comparison**:
| Metric | Naive Bayes | ANN |
|------|-----------|-----|
| Accuracy |98.58% |98.45% |
| Precision |98.59% |98.47% |
| Recall |98.58% |98.45% |
| F1 Score |98.56% |98.44% |
**Summary**: The project is an excellent teaching case that demonstrates the construction of a full-process AI system. The dual-model design enhances flexibility, the complete pipeline ensures data quality, and engineering thinking (testing, deployment) helps transform prototypes into products, which has reference value for AI and full-stack learners.