https://github.com/kefirchk/animatica-backend
Animatica is my diploma project that generates videos from text and images using neural networks. It automates animation creation with image generation, image-to-video conversion, and post-processing
https://github.com/kefirchk/animatica-backend
animation fastapi gan image-to-video machine-learning neural-networks python pytorch video-generation
Last synced: 2 months ago
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Animatica is my diploma project that generates videos from text and images using neural networks. It automates animation creation with image generation, image-to-video conversion, and post-processing
- Host: GitHub
- URL: https://github.com/kefirchk/animatica-backend
- Owner: kefirchk
- License: mit
- Created: 2025-03-22T13:41:29.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-22T18:18:09.000Z (7 months ago)
- Last Synced: 2025-03-22T18:36:02.381Z (7 months ago)
- Topics: animation, fastapi, gan, image-to-video, machine-learning, neural-networks, python, pytorch, video-generation
- Language: Python
- Homepage:
- Size: 2.93 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Animatica-backend
***Animatica*** is my diploma project that generates videos from text and
images using neural networks. It automates animation creation with image generation,
image-to-video conversion, and post-processing.
## Deploying on Local
### Setting up the environment
#### Step 1: Create a virtual environment
###### *Linux/macOS:*
```bash
python3 -m venv venv
source venv/bin/activate
```
###### *Windows:*
```bash
python -m venv venv
source venv/Scripts/activate
```
#### Step 2: Install requirements
```bash
cd app
pip install -r requirements.txt
```
#### Step 3: Create env files
```bash
# env/api.env
API_BASE_URL=http://localhost:8080
API_MODE=local
LOG_LEVEL=debug
FRONTEND_BASE_URL=http://localhost:8501
SESSION_SECRET_KEY=your_session_secret_key
LOCALHOST_CLIENT_ORIGIN=http://localhost:5173
ALLOWED_ORIGINS=localhost
```
```bash
# env/auth.env
SECRET_KEY=your-secret-key
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30
REFRESH_TOKEN_EXPIRE_DAYS=30
TOKEN_ISSUER=Animatica
```
```bash
# env/db.env
DB_HOST=db
DB_PORT=5432
DB_NAME=animatica
DB_USER=postgres
DB_PASS=postgres
```
```bash
# env/ml-engine.env
ML_ENGINE_BASE_URL=http://ml-engine:90
ML_ENGINE_KEY=your-secret-key
ML_ENGINE_KEY_HEADER=X-ML-Engine-Key
```
```bash
# env/stripe.env
PUBLIC_KEY=your-public-key
SECRET_KEY=your-secret-key
```
#### Step 4: Run server
```bash
uvicorn src.main:app --host 0.0.0.0 --port 80
```
Available endpoints:
- http://localhost:8080/docs (Swagger docs).
## System Design
## Deploying via Docker
Below are the basic commands to manage docker.
###### Docker-compose
```bash
docker-compose up --build
docker-compsose down
```