https://github.com/harshkhasbage77/no-code-ai-automations-platform
Main repository holding frontend submodule and backend directory.
https://github.com/harshkhasbage77/no-code-ai-automations-platform
ai-automation fastapi graph python3 render workflow
Last synced: about 1 month ago
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Main repository holding frontend submodule and backend directory.
- Host: GitHub
- URL: https://github.com/harshkhasbage77/no-code-ai-automations-platform
- Owner: harshkhasbage77
- Created: 2024-11-23T12:24:10.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-19T18:37:19.000Z (over 1 year ago)
- Last Synced: 2025-05-21T20:14:07.261Z (about 1 year ago)
- Topics: ai-automation, fastapi, graph, python3, render, workflow
- Language: Python
- Homepage: https://no-code-ai-automations-platform-backend.onrender.com
- Size: 108 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# No-code AI Automation Platform
This project is a no-code platform for creating and managing AI-driven automation pipelines. It consists of a backend built with FastAPI and a frontend built with React.
Demo: [Click Here](https://react-flow-graphs-frontend.vercel.app/)
Link: https://react-flow-graphs-frontend.vercel.app/
Backend: https://no-code-ai-automations-platform-backend.onrender.com/
## Project Structure
### Backend
The backend is built with FastAPI and provides APIs for managing pipelines. It includes the following key components:
- **main.py**: The main entry point of the backend application.
- **Node, Edge, Pipeline**: Pydantic models representing the structure of a pipeline.
#### Running the Backend
1. Create a virtual environment:
```sh
python -m venv .myenv
```
2. Activate the virtual environment:
```sh
source .myenv/bin/activate
```
3. Install the dependencies:
```sh
pip install -r requirements.txt
```
4. Run the FastAPI server:
```sh
uvicorn main:app --reload
```
The backend server will be available at `http://127.0.0.1:8000`.
### Frontend
The frontend is built with React and provides a drag-and-drop interface for creating pipelines. It includes the following key components:
- **App.js**: The main entry point of the frontend application.
- **PipelineToolbar**: A toolbar for adding nodes to the pipeline.
- **PipelineUI**: The main UI for displaying and managing the pipeline.
- **SubmitButton**: A button for submitting the pipeline to the backend.
#### Running the Frontend
1. Navigate to the `frontend` directory:
```sh
cd frontend
```
2. Install the dependencies:
```sh
npm install
```
3. Start the development server:
```sh
npm start
```
The frontend application will be available at `http://localhost:3000`.
### API Endpoints
#### `POST /pipelines/parse`
Parses a pipeline and returns the number of nodes, number of edges, and whether the graph is a Directed Acyclic Graph (DAG).