⚡ Streamlit-native resume classification and analysis tool deployed on Hugging Face Spaces.
🎯 Single-port, production-ready application with full AI-powered analysis.
TalentLens is a modern, automated resume intake, classification, and scoring application.
It provides:
- 🔍 Fast Classification → Quick category prediction (Tech, Finance, Healthcare, Education, etc.)
- 🤖 Deep AI Analysis → Full extraction of skills, strengths, recommended roles, and a numerical match score
- 📜 Persistent History → All analyses stored locally in SQLite for audit & exploration
🌐 Live Demo: TalentLens on Hugging Face Spaces
- 🐍 Python 3.10+
- 📦 pip or uv package manager
# Clone and setup
git clone https://github.com/frankTheCodeBoy/TalentLens.git
cd TalentLens
cp .env.example .env
# Add your Hugging Face API key to .env (optional, for AI summaries)
# HUGGINGFACE_API_KEY=hf_your_token_here
# Install dependencies
pip install -r requirements.txt
# Run the app
streamlit run app.pyThe app will open in your browser at http://localhost:8501.
- 🗂️ Tab 1: Fast Classification → Upload PDFs, get instant category predictions, view charts
- 🧠 Tab 2: Deep AI Analysis → Extract skills, strengths, roles, scores, and improvement tips
- 📊 Tab 3: History & Search → Browse, filter, and audit past analyses with SQLite persistence
- 🐳 Dockerfile included → Hugging Face Spaces auto-builds and deploys
- 🔑 Secrets tab → Add
HUGGINGFACE_API_KEYfor AI summaries
- 🐍 Python (Streamlit, Pandas, Joblib)
- 🤗 Hugging Face (Summarization API)
- 🗄️ SQLite (Persistent history)
- 📦 Docker (Spaces deployment)
Francis Olum (Frank)
Analytics Engineer & Open‑Source Advocate
- 🌐 GitHub: @frankTheCodeBoy
- 🤗 Hugging Face Spaces: TalentLens
- 🐛 Report Issues
- 💬 Join Discussions
- ⭐ Star this repo to support the project and follow updates!
See LICENSE file for details.
❤️ Made with love by Francis Olum