Skip to main content

A PDF reading and analysis application

Project description

DeepRead

A desktop application for reading and analyzing PDFs with LLM support.

Overview

DeepRead is an Electron application with a Python backend that allows users to:

  • Read and analyze PDF documents
  • Chat with LLMs about the content
  • Extract and process text from PDFs

Installation

Using pip

You can install DeepRead directly from PyPI:

pip install deepread

After installation, you can run the application with:

deepread serve

This will start the server at http://127.0.0.1:8000 by default.

To see all available commands:

deepread --help

Using pre-built binaries

Pre-built binaries for Windows, macOS, and Linux are available on the Releases page.

Development

For local development, you can run:

# Start the frontend and backend for web development
npm run dev:web

# Start with Electron support
npm run dev:electron

Building the Application

Development Mode

During development, the frontend and backend run as separate processes:

  1. Start the backend:
cd backend
python main.py
  1. Start the frontend:
cd frontend
npm run dev

Production Build

To build a standalone executable that includes both the frontend and backend:

# Run the build script from the root directory
python build.py

This will:

  1. Build the React frontend
  2. Package the frontend with the backend using PyInstaller
  3. Create a standalone executable in the backend/dist directory

The build script ensures that the frontend is always built before the backend, and the backend build will fail if the frontend build is not found.

You can then run the application with:

# On macOS/Linux
./backend/dist/backend

# On Windows
backend\dist\backend.exe

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

deepread-0.0.1.tar.gz (7.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

deepread-0.0.1-py3-none-any.whl (8.6 kB view details)

Uploaded Python 3

File details

Details for the file deepread-0.0.1.tar.gz.

File metadata

  • Download URL: deepread-0.0.1.tar.gz
  • Upload date:
  • Size: 7.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for deepread-0.0.1.tar.gz
Algorithm Hash digest
SHA256 bbc71eb63bfbe95b6ead78a6ef7ee6784071cebad41fb7f4992ebb915a20425a
MD5 dcc5ba121b6de53e50ae1ec6940e7ea0
BLAKE2b-256 c137443e8fe6273aa054dd9cc46a7a36425b742fa049a312d2e74f39ca1f062a

See more details on using hashes here.

File details

Details for the file deepread-0.0.1-py3-none-any.whl.

File metadata

  • Download URL: deepread-0.0.1-py3-none-any.whl
  • Upload date:
  • Size: 8.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for deepread-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 0e11705d109780a6bb3bba00e0bcd95d026d918e7b1cff11bf59a34eaeb812a9
MD5 42e22aa10f2d0ab1b6ea36baab27110e
BLAKE2b-256 46a634079410e90716b54fa353c35c8918dc5d8f00f98f0e6116dedb2bd86479

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page