https://github.com/headless-start/cs2-endtoend-chatbot

This repository contains a simple end to end Counter Strike 2 chat bot.
https://github.com/headless-start/cs2-endtoend-chatbot

chatbot counter-strike-2 css flask html5 nltk python3 scikit-learn streamlit

Last synced: about 2 months ago
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This repository contains a simple end to end Counter Strike 2 chat bot.

• Host: GitHub
• URL: https://github.com/headless-start/cs2-endtoend-chatbot
• Owner: headless-start
• License: mit
• Created: 2025-02-07T17:55:09.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-09T13:40:51.000Z (over 1 year ago)
• Last Synced: 2025-04-03T02:14:49.237Z (about 1 year ago)
• Topics: chatbot, counter-strike-2, css, flask, html5, nltk, python3, scikit-learn, streamlit
• Language: Python
• Homepage: https://en.wikipedia.org/wiki/Counter-Strike_2
• Size: 138 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Counter-Strike 2 Chatbot  

## 📌 Project Overview  

This project is an end-to-end **Counter-Strike 2 Chatbot** that provides real-time answers to user queries about the game. Built entirely using **Flask**, the chatbot allows users to ask questions about game economy, map strategies, skins, and more.  

The backend leverages **TF-IDF vectorization** and **Logistic Regression** for intent classification, ensuring precise and relevant responses.  

---

## 🚀 Key Features  

1. **Flask-Powered Chat Interface**:

   - The entire chatbot runs on Flask, handling both frontend and backend.  

   - Users can type questions and receive instant responses.  

   - Supports queries about game economy, map strategies, skins, and general gameplay tips.    

2. **Intent Classification with Machine Learning**:  

   - Utilizes **TF-IDF (Term Frequency-Inverse Document Frequency)** for feature extraction.  

   - **Logistic Regression** model classifies user queries into predefined categories.  

   - Ensures fast and accurate response generation.  

---

## 🔍 How It Works  

1. **User Input**:  

   - Users enter their queries via the Flask-based chat interface (e.g., "What is the best economy strategy for Mirage?").  

2. **Backend Processing**:  

   - The input text is processed using **TF-IDF vectorization**.  

   - The **Logistic Regression model** predicts the intent of the query.  

   - A relevant response is retrieved from the predefined knowledge base.  

3. **Response Generation**:  

   - Flask sends the generated response back to the user via the chat interface.  

---

## 🛠 System Requirements  

### Dependencies  

- Python 3.8+  

- Required Libraries:  

  ```bash

  pip install flask nltk pandas scikit-learn

  

---

## 📄 License  

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.