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: 7 days 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 (8 days ago)
• Default Branch: main
• Last Pushed: 2025-02-08T08:46:56.000Z (7 days ago)
• Last Synced: 2025-02-08T16:17:40.357Z (7 days 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: 136 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 using **Streamlit** for the frontend and **Flask** for the backend, the chatbot allows users to ask questions about game economy, map strategies, skins, and more.  

**Goal**: To create an interactive and informative chatbot for Counter-Strike 2 players.  

---

## 🚀 Key Features  

1. **Interactive Chat Interface**:  

   - Built using **Streamlit** for a user-friendly interface.  

   - Users can type questions and receive instant responses.   

   - Flask handles the logic for processing user queries and generating responses.  

   - Supports queries about game economy, map strategies, skins, and more.  

2. **Dynamic Response Generation**:  

   - Uses predefined knowledge bases and APIs to provide accurate and relevant answers.  

---

## 🔍 How It Works  

1. **User Input**:  

   - Users type their questions in the Streamlit interface (e.g., "What is the best economy strategy for Mirage?").  

2. **Backend Processing**:  

   - The query is sent to the Flask backend, which processes it and retrieves the relevant information.  

3. **Response Generation**:  

   - The backend sends the response back to the Streamlit frontend, which displays it to the user.  

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## 🛠 System Requirements  

### Dependencies  

- Python 3.8+  

- Libraries: `streamlit`, `flask`, `requests`, `nltk`, `pandas`  

- Hardware: Any modern CPU (GPU not required)  

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## 📄 License  

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