Developed an app to extract text from PDFs using libraries such as PyMuPDF and llama-parse for text extraction.
A high-level architecture diagram that illustrates the structure of a monolithic application deployed inside a Docker container. It shows the interaction between the user, the frontend, and the backend.
Basic flow for the sequence diagram:
- The User sends a PDF to the Frontend.
- The Frontend receives the PDF.
- The Frontend sends the PDF to the Backend for processing.
- The Backend uses PyMuPDF to extract text from the PDF.
- The Backend uses Llama-Parse to process (parse or analyze) the extracted text.
- The Backend returns the processed text to the Frontend.
- The Frontend displays the text to the User.
pdf_text_extractor/
├── .dockerignore # Files and folders to exclude from Docker builds
├── .env # Environment variables (keep secret)
├── .env.example # Example environment variables
├── .gitignore # Git ignore rules
├── .python-version # Python version used in the project
├── .venv/ # Local virtual environment (ignored in git)
├── data/ # Folder for input/output data (PDFs, extracted text, etc.)
├── dist/ # Distribution or build files
├── docs/ # Documentation files
├── main.py # Entry point of the application
├── notebook/ # Jupyter notebooks for experiments or testing
├── pyproject.toml # Project dependencies and metadata
├── README.md # Project README file
├── Dockerfile # Dockerfile to build the container
├── src/ # Source code for the project
└── uv.lock # Dependency lock file for uv✅ 1. Clone the repository
git clone https://github.com/estelacode/pdf_text_extractor.git
cd pdf_text_extractor✅ 2. Create and activate a virtual environment
py -3.13 -m venv .venv
.venv\Scripts\activate # Windows
# or
source .venv/bin/activate # Linux/macOS✅ 3. Install UV
pip install uv✅ 4. Install dependences from .toml file.
uv pip install -e .✅ 5. Configure the .env file
☑️ Step 1: Create Virtual Enviroment
py -3.13 -m venv .venv☑️ Step 2: Activate Virtual Enviroment
.venv\Script\activate☑️ Step 3: Install UV
pip install uv☑️ Step 4: Create a project with uv
uv init☑️ Step 5: Link my local repository to my Github remote repository
git remote add origin https://github.com/estelacode/pdf_text_extractor.git
git remote -v # Verify the remote repository is added☑️ Step 6: Add first commit and push the current branch and set the remote as upstream
git add README.md
git commit -m "README.md"
git push --set-upstream origin master
git push -u origin master☑️ Step 7: Add and remove dependencies
uv add [OPTIONS] <PACKAGES>... # Add dependencies to the project
uv remove [OPTIONS] <PACKAGES>... # Remove dependencies from the project.cd pdf_text_extractor
uv run main.py
# navigate to http://localhost:7860/uv builduv pip freeze > requirements.txtuv pip install -e . #
uv pip install --editable . # Install the editable package based on the provided local file path.- Generate the whl file in the dist folder
uv build- Build a Docker image with the code and dependencies from my project.
docker build -t pdf_text_extractor . # build your Docker image
docker images # list docker images
docker rmi <docker_image-id> # remove the docker image with image id (Ex.1bec6217270e)- Create and run a docker container
docker run -d -p 8080:8080 pdf_text_extractor
docker ps -a # list all the docker containers
docker rm -f <container-id> #remove the docker container with id (Ex.cf99422731ef)- Create a docker container with enviroment variables
docker run -d -p 8080:8080 -e LLAMA_CLOUD_API_KEY="XXXXXXXXXXXXXXXXXXXXXXXXXX" pdf_text_extractorEstela Madariaga