https://github.com/hibatillah/deep-learning
Text Sentiment Analysis and Audio Classification
https://github.com/hibatillah/deep-learning
audio-classification deep-learning flask nextjs python shadcn-ui tailwindcss text-sentiment-analysis
Last synced: 3 months ago
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Text Sentiment Analysis and Audio Classification
- Host: GitHub
- URL: https://github.com/hibatillah/deep-learning
- Owner: hibatillah
- Created: 2024-11-17T16:31:39.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-01-30T05:58:36.000Z (over 1 year ago)
- Last Synced: 2025-03-27T15:57:26.328Z (over 1 year ago)
- Topics: audio-classification, deep-learning, flask, nextjs, python, shadcn-ui, tailwindcss, text-sentiment-analysis
- Language: TypeScript
- Homepage: https://deeplearning-sic.vercel.app
- Size: 164 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
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README
## Deep Learning Project
Deep Learning project for text sentiment analysis and audio classifications.
### Tech Stack
- Deep learning project using Flask `v3.1.0`
- Web project using Next.js `v14.2.17`
## Development
1. Clone this repository
```bash
git clone https://github.com/hibatillah/deep-learning
```
2. Create virtual environment for python
```bash
# ./deep_learning
python -m venv venv
```
3. Activate virtual environment (venv)
```bash
# ./deep_learning
venv\Scripts\activate
```
> venv active based on session.
4. Install Depedencies
```bash
# ./deep_learning
pip install -r requirements.txt
```
```bash
# ./
# ./web
npm install
```
5. Run project
> Use all commands to run in the project `root`.
> Next command will run `web` and `deep_learning` project concurrently.
```bash
npm run dev
```
> You can run project separately using next command.
```bash
# for web
npm run dev:next
# for deep_learning
npm run dev:py
```
> [!IMPORTANT]
> Make sure to activate `venv` before running the project, specifically for `deep_learning` project.
6. Open `web` project at [localhost:3000](http://localhost:3000) and `deep_learning` project at [localhost:8000](http://localhost:8000)