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| title: TalentScout AI | |
| emoji: 🤖 | |
| colorFrom: indigo | |
| colorTo: purple | |
| sdk: streamlit | |
| sdk_version: "1.34.0" | |
| app_file: app.py | |
| pinned: false | |
| # 🤖 TalentScout - AI Hiring Assistant | |
| A sophisticated AI-powered hiring assistant built with Streamlit that streamlines the recruitment process through intelligent candidate screening and technical assessment. | |
| ## 🌟 Features | |
| - **Automated Candidate Screening**: Collects essential candidate information systematically | |
| - **Dynamic Technical Assessment**: Generates relevant technical questions based on candidate's tech stack | |
| - **Interactive Chat Interface**: User-friendly conversational experience | |
| - **Real-time Progress Tracking**: Visual progress indicators for both candidate and recruiter | |
| - **Data Export Functionality**: Export candidate data and responses in JSON format | |
| - **Responsive Design**: Works seamlessly across different screen sizes | |
| - **Input Validation**: Ensures data quality with email and phone number validation | |
| ## 🚀 Live Demo | |
| Try the application live on Hugging Face Spaces: [TalentScout AI Assistant](https://huggingface.co/spaces/YOUR_USERNAME/talentscout-ai) | |
| ## 📋 How It Works | |
| The application follows a structured screening process: | |
| 1. **Welcome & Introduction**: Introduces the AI assistant and process | |
| 2. **Personal Information Collection**: | |
| - Full name | |
| - Email address (with validation) | |
| - Phone number (with validation) | |
| - Years of experience | |
| - Desired positions | |
| - Current location | |
| 3. **Technical Profile**: Tech stack and expertise areas | |
| 4. **Technical Assessment**: Dynamic questions based on provided tech stack | |
| 5. **Completion**: Summary and next steps | |
| ## 🛠️ Technical Stack | |
| - **Frontend**: Streamlit | |
| - **Language**: Python 3.8+ | |
| - **Data Processing**: Built-in Python libraries (json, re, datetime) | |
| - **UI/UX**: Custom CSS styling with Streamlit components | |
| ## 🏗️ Installation & Setup | |
| ### Local Development | |
| 1. **Clone the repository** | |
| ```bash | |
| git clone https://github.com/YOUR_USERNAME/talentscout-ai.git | |
| cd talentscout-ai | |
| ``` | |
| 2. **Create virtual environment** | |
| ```bash | |
| python -m venv venv | |
| source venv/bin/activate # On Windows: venv\Scripts\activate | |
| ``` | |
| 3. **Install dependencies** | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 4. **Run the application** | |
| ```bash | |
| streamlit run app.py | |
| ``` | |
| 5. **Open your browser** and navigate to `http://localhost:8501` | |
| ### Hugging Face Deployment | |
| 1. **Fork/Upload to GitHub** | |
| 2. **Connect to Hugging Face Spaces** | |
| 3. **Select Streamlit as the framework** | |
| 4. **Deploy automatically** | |
| ## 📁 Project Structure | |
| ``` | |
| talentscout-ai/ | |
| ├── app.py # Main application file | |
| ├── requirements.txt # Python dependencies | |
| ├── README.md # Project documentation | |
| ├── DOCUMENTATION.md # Detailed code documentation | |
| └── .gitignore # Git ignore file | |
| ``` | |
| ## 🎯 Key Components | |
| ### CandidateInfo Class | |
| - Dataclass for storing candidate information | |
| - Includes validation and structured data management | |
| ### HiringAssistant Class | |
| - Main application logic | |
| - Conversation flow management | |
| - Technical question generation | |
| - Input validation and processing | |
| ### Conversation Stages | |
| 1. Greeting (0) | |
| 2. Name Collection (1) | |
| 3. Email Collection (2) | |
| 4. Phone Collection (3) | |
| 5. Experience Collection (4) | |
| 6. Position Collection (5) | |
| 7. Location Collection (6) | |
| 8. Tech Stack Collection (7) | |
| 9. Technical Questions (8) | |
| 10. Completion (9) | |
| ## 🔧 Configuration | |
| The application includes several configurable elements: | |
| - **Tech Categories**: Predefined technology categories for question generation | |
| - **Question Templates**: Customizable technical questions for different technologies | |
| - **Validation Rules**: Email and phone number validation patterns | |
| - **UI Styling**: Custom CSS for enhanced user experience | |
| ## 📊 Supported Technologies | |
| The AI assistant can generate technical questions for: | |
| - **Programming Languages**: Python, Java, JavaScript, C++, C#, Go, Rust, PHP, Ruby, Swift, Kotlin | |
| - **Web Frameworks**: Django, Flask, FastAPI, React, Angular, Vue.js, Node.js, Express, Spring, Laravel | |
| - **Databases**: MySQL, PostgreSQL, MongoDB, Redis, SQLite, Oracle, Cassandra, Elasticsearch | |
| - **Cloud Platforms**: AWS, Azure, GCP, Docker, Kubernetes, Terraform | |
| - **Data Science**: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Matplotlib, Seaborn | |
| - **Mobile**: React Native, Flutter, Android, iOS, Xamarin | |
| ## 🎨 UI Features | |
| - **Gradient Header**: Eye-catching header with company branding | |
| - **Sidebar Information**: Real-time candidate data display | |
| - **Progress Tracking**: Visual progress bar and stage indicators | |
| - **Chat Interface**: Conversational UI with distinct message styling | |
| - **Responsive Design**: Adapts to different screen sizes | |
| ## 📤 Data Export | |
| The application provides data export functionality: | |
| - Candidate information summary | |
| - Technical Q&A responses | |
| - Session metadata | |
| - JSON format for easy integration | |
| ## 🚦 Usage Guidelines | |
| 1. **Start Session**: Begin with the welcome message | |
| 2. **Follow Prompts**: Answer each question as prompted | |
| 3. **Technical Questions**: Provide detailed answers for technical assessment | |
| 4. **Review Summary**: Check the completion summary | |
| 5. **Export Data**: Download session data if needed | |
| ## 🔒 Data Privacy | |
| - No data is stored permanently on servers | |
| - Session data is cleared after completion | |
| - Export functionality allows local data storage | |
| - Follows best practices for data handling | |
| ## 🤝 Contributing | |
| 1. Fork the repository | |
| 2. Create a feature branch (`git checkout -b feature/amazing-feature`) | |
| 3. Commit your changes (`git commit -m 'Add amazing feature'`) | |
| 4. Push to the branch (`git push origin feature/amazing-feature`) | |
| 5. Open a Pull Request | |
| ## 📝 License | |
| This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. | |
| ## 🙏 Acknowledgments | |
| - Built with [Streamlit](https://streamlit.io/) | |
| - Inspired by modern recruitment automation needs | |
| - Designed for seamless candidate experience | |
| ## 📞 Support | |
| For support, please create an issue in the GitHub repository or contact the development team. | |
| --- | |
| **TalentScout AI Hiring Assistant** - Streamlining recruitment through intelligent automation 🚀 |