BookFinder is a web application that helps users discover books similar to their favorites by analyzing book descriptions and genres. It leverages natural language processing to compare descriptions and generate personalized recommendations from a custom dataset.
- 🔍 Search by title & author using the Google Books API
- ✂️ Summarize descriptions with a pretrained
t5-smallmodel - 📚 Find similar books using sentence embeddings
- 🔗 Clickable Libgen links for quick access to recommended titles
- Frontend: Streamlit
- Backend: Flask API + precomputed embeddings
- ML Models: Transformers (T5), Sentence-Transformers
- Data Handling: Pandas, NumPy
- Infrastructure: Docker, Kubernetes, Terraform, GKE Autopilot
- APIs: Google Books API
- Frontend (Streamlit) runs on port
8501, calls backend API - Backend (Flask) runs on port
5000, loads a CSV with 500+ books and precomputed embeddings - Deployment: Dockerized, deployed via Kubernetes
- Cloud Option: Full deployment via Terraform to GCP using GKE Autopilot
BookFinder/
├── app/ # Frontend (Streamlit)
│ ├── app.py
│ ├── Dockerfile
│ └── requirements.txt
├── backend/ # Backend (Flask)
│ ├── app.py
│ ├── book_embeddings.csv
│ ├── Dockerfile
│ └── requirements.txt
├── k8s/ # Kubernetes manifests (Kustomize-ready)
│ ├── kustomization.yaml
│ ├── frontend-deployment.yaml
│ ├── frontend-service.yaml
│ ├── backend-deployment.yaml
│ ├── backend-service.yaml
│ ├── configmap.yaml
│ └── secret.yaml
├── iac/ # Terraform files for GCP Autopilot
│ ├── main.tf
│ ├── variables.tf
│ └── terraform.tfvars
├── scripts/ # Utility scripts for dataset preperation and model training
│ └── generate_embeddings.py
└── README.md
- Kubernetes (e.g. Minikube or Docker Desktop)
kubectlandkustomize- Docker (if building images locally)
- A Google Books API key
# Clone the repository
git clone https://github.com/bennymestel/BookFinder.git
cd BookFinder
# Apply manifests using Kustomize
kubectl apply -k k8s/
# Streamlit will be available at:
http://localhost- Google Cloud SDK
- Terraform
- A GCP account with billing enabled
- Docker images published on DockerHub:
bennymestel/book-finder-frontendbennymestel/book-finder-backend
# Clone and enter the Terraform directory
cd BookFinder/iac
# Set up variables
# Edit terraform.tfvars with your GCP project ID and region
# Authenticate with GCP
gcloud auth login
gcloud auth application-default login
# Deploy the infrastructure
terraform init
terraform apply
# Connect to GKE (replace REGION with the region you specified in terraform.tfvars)
gcloud container clusters get-credentials book-finder-cluster --region=REGION
# Deploy the app
kubectl apply -k ../k8s/
# Get external IP
kubectl get svc book-finder-frontendAccess your deployed app at:
http://<EXTERNAL-IP>
Store your Google Books API key securely:
- In
k8s/secret.yaml - Or as an environment variable
Streamlit config and secrets can be set via mounted secrets.toml.
To regenerate book_embeddings.csv:
python scripts/generate_embeddings.pyReplace the CSV in backend/, rebuild the image, and redeploy.
Prebuilt images are hosted on DockerHub:
- Frontend:
bennymestel/book-finder-frontend - Backend:
bennymestel/book-finder-backend
- App runs locally and in the cloud
- Fully containerized and K8s-deployable
- Infrastructure as Code with Terraform
- Public GCP-compatible deployment path