A comprehensive Python chatbot designed to interact with users about video games, provide personalized game recommendations, share gaming tips, and engage in meaningful gaming conversations.
- Natural Language Processing: Understands various ways users express their gaming preferences
- Context-Aware Responses: Maintains conversation context for more meaningful interactions
- Intent Recognition: Automatically detects what users want (recommendations, information, tips, etc.)
- Personalized Suggestions: Recommends games based on preferred genres, platforms, and playtime
- Smart Filtering: Filters games by platform availability, genre preferences, and game length
- Detailed Information: Provides ratings, descriptions, platforms, and key features for each game
- Multiple Genres: Action, Adventure, Strategy, Puzzle, Racing, Indie games
- Cross-Platform: Games for PC, PlayStation, Xbox, Nintendo Switch, and Mobile
- Rich Metadata: Ratings, release years, playtime estimates, and detailed descriptions
- Game Features: Tags for game characteristics (open-world, story-rich, multiplayer, etc.)
- Tips & Advice: Practical gaming tips for better gaming experience
- Gaming Facts: Interesting trivia about the gaming industry
- Game Reviews: Detailed reviews with ratings and recommendations
- Colorful CLI: Enhanced terminal interface with colors and emojis
- Easy Commands: Simple commands for help, clearing screen, and exiting
- Demo Mode: Interactive demonstration of chatbot capabilities
- Python 3.7 or higher
- No additional dependencies required (uses Python standard library)
-
Clone or Download the project files:
git clone <repository-url> cd game-chatbot
-
Run the Chatbot:
python demo.py
-
Choose Your Mode:
- Option 1: Interactive Chat
- Option 2: Demo Mode
- Option 3: Exit
help
- Show available commands and examplesclear
- Clear the screenquit
orexit
- End the conversation
You: Recommend me some action games for PC
Bot: ๐ฎ Game Recommendations for You:
1. The Witcher 3: Wild Hunt (2015)
๐ฑ Platforms: PC, PlayStation, Xbox, Switch
โญ Rating: 9.3/10
๐ญ Genre: Action
โฑ๏ธ Playtime: Long
๐ Open-world RPG with rich storytelling and complex characters
You: Tell me about Portal 2
Bot: ๐ฎ Portal 2 (2011)
๐ฑ Platforms: PC, PlayStation, Xbox
โญ Rating: 9.5/10
๐ญ Genre: Puzzle
โฑ๏ธ Playtime: Short
๐ท๏ธ Features: puzzle-solving, physics-based, co-op
๐ Description: Mind-bending puzzle game with clever mechanics
You: Give me a gaming tip
Bot: ๐ก Gaming Tip:
Always save your game progress frequently to avoid losing hours of gameplay!
Would you like another tip or need advice about a specific gaming topic? ๐ฎ
game-chatbot/
โ
โโโ game_chatbot.py # Main chatbot class with all functionality
โโโ demo.py # Interactive demo and CLI interface
โโโ requirements.txt # Project dependencies
โโโ README.md # This documentation
- Object-Oriented Design: Clean, modular code structure
- Intent Detection: Pattern matching for understanding user requests
- Preference Extraction: Automatic extraction of gaming preferences from natural language
- Recommendation Engine: Smart filtering and ranking system
- Action: The Witcher 3, Red Dead Redemption 2, Cyberpunk 2077
- Adventure: Zelda: Breath of the Wild, Uncharted 4
- Strategy: Civilization VI, Total War: Warhammer III
- Puzzle: Portal 2, The Witness
- Racing: Forza Horizon 5, Gran Turismo 7
- Indie: Hades, Celeste
- PC (Windows, Mac, Linux)
- PlayStation (PS4, PS5)
- Xbox (Xbox One, Series X/S)
- Nintendo Switch
- Mobile (iOS, Android)
- Short: Under 10 hours
- Medium: 10-40 hours
- Long: 40+ hours
- Discover New Games: Find games matching your exact preferences
- Platform-Specific Recommendations: Get games for your console or PC
- Time-Based Gaming: Find games that fit your available time
- Learn About Games: Get detailed information before purchasing
- Gaming Knowledge: Learn interesting facts about the gaming industry
- Tips and Tricks: Improve your gaming experience
- Reviews: Get honest opinions about popular games
- NLP Example: Study natural language processing in gaming context
- Chatbot Architecture: Learn about intent detection and response generation
- Recommendation Systems: Understand preference-based filtering
Edit the games_db
dictionary in game_chatbot.py
:
'your_genre': [
{
'name': 'Game Name',
'platform': ['PC', 'PlayStation'],
'rating': 8.5,
'year': 2023,
'playtime': 'medium',
'description': 'Game description',
'features': ['feature1', 'feature2']
}
]
Add to the gaming_tips
or gaming_facts
lists:
self.gaming_tips.append("Your new gaming tip here!")
self.gaming_facts.append("Your interesting gaming fact here!")
Modify the detect_intent
method to recognize new conversation patterns:
elif any(word in user_input for word in ['your', 'keywords']):
return 'your_new_intent'
- External Game APIs: Integration with Steam, IGDB, or other game databases
- User Profiles: Persistent user preferences and gaming history
- Advanced NLP: More sophisticated natural language understanding
- Web Interface: Browser-based chat interface
- Voice Integration: Speech-to-text and text-to-speech capabilities
- Game Reviews Scraping: Real-time review data from gaming websites
- Social Features: Game recommendations based on friends' preferences
- Database Integration: SQLite or PostgreSQL for game data
- Machine Learning: ML-based recommendation algorithms
- API Development: REST API for integration with other applications
- Testing Suite: Comprehensive unit and integration tests
- Configuration File: YAML/JSON configuration for easy customization
- Fork the Repository: Create your own copy of the project
- Add Features: Implement new games, improve algorithms, or enhance UI
- Submit Issues: Report bugs or suggest improvements
- Documentation: Help improve documentation and examples
# Clone your fork
git clone <your-fork-url>
cd game-chatbot
# Create a virtual environment (optional)
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install development dependencies
pip install -r requirements.txt
# Run tests (if available)
python -m pytest
This project is open source and available under the MIT License.
- Game Data: Curated from popular gaming websites and databases
- Python Community: For excellent documentation and libraries
- Gaming Community: For inspiration and feedback
If you encounter issues or have questions:
- Check the documentation above
- Try the demo mode to see expected behavior
- Create an issue in the project repository