Metadata-Version: 2.4
Name: workflowredo
Version: 1.0.1
Summary: All-in-one AI workflow toolkit: encode images/text/video/files to IDs, training & tokenizer helpers, and 1000+ utility scripts for continual AI development.
Author: workflowredo
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: tensorflow
Requires-Dist: tokenizers
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: torch
Requires-Dist: seaborn
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: jupyter
Requires-Dist: notebook
Requires-Dist: tqdm
Requires-Dist: requests
Requires-Dist: pillow
Requires-Dist: opencv-python
Requires-Dist: nltk
Requires-Dist: spacy
Requires-Dist: sentencepiece
Dynamic: author
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# workflowredo 1.0.1

Library Python all-in-one untuk mempermudah membangun AI berkelanjutan.

## Instalasi

```bash
pip install workflowredo
```

Otomatis menginstall: tensorflow, tokenizers, numpy, pandas, torch, seaborn,
scikit-learn, scipy, jupyter, notebook, tqdm, requests, pillow, opencv-python,
nltk, spacy, sentencepiece.

## Fitur Utama

- `image_to_id(path)` – encode gambar menjadi ID unik + vector embedding.
- `text_to_id(text)` – encode kata/kalimat menjadi token ID.
- `file_to_id(path)` – encode video, zip, pdf, atau file apa pun menjadi ID.
- `Trainer` – helper training PyTorch / TensorFlow.
- `Tokenizer` – wrapper tokenizer cepat.
- `tensorflow_helpers` – util untuk model TF.
- 1000+ script utility di dalam `workflowredo.scripts` untuk mempermudah
  pembuatan AI berkelanjutan (data loading, augmentasi, evaluasi, dll).

## Contoh

```python
import workflowredo as wr

img_id   = wr.image_to_id("foto.jpg")
text_id  = wr.text_to_id("halo dunia")
file_id  = wr.file_to_id("video.mp4")

trainer = wr.Trainer(model, dataset)
trainer.fit(epochs=3)
```
