SmartText_AI

A compact, shareable README for the SmartText_AI project — a Python/Jupyter notebook collection showcasing NLP features implemented in the NLP Cloud Playground. Ready to drop into a GitHub repository alongside your notebook SmartText_AI.ipynb.

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Project overview

This repository contains experiments and example code demonstrating common NLP tasks performed in the NLP Cloud Playground, including:

• Named Entity Recognition (NER)
• Sentiment Analysis
• Language Detection
• Chatbot (conversational assistant example)
• Intent Classification
• Grammar Correction

The intention of this repo is to be a reproducible, easy-to-follow notebook(s) + supporting scripts to: 1) show how each NLP feature works, 2) provide packaged examples so others can reproduce your results, and 3) serve as a demo portfolio project.

Quick start — run locally

1. Clone the repo:

git clone https://github.com/<your-username>/SmartText_AI.git
cd SmartText_AI

2. Create & activate a virtual environment (recommended):

python -m venv .venv
# macOS / Linux
source .venv/bin/activate
# Windows
.venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Provide credentials (if using NLP Cloud or any API)

• Copy .env.example to .env and fill your keys (e.g. NLP_CLOUD_API_KEY or other service tokens).

5. Open the notebook or run example scripts

jupyter lab    # or jupyter notebook
# or run a script
python src/ner_example.py

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Example usage snippets

(Keep the notebook as the canonical, commented walkthrough. Put short runnable examples in src/.)

NER (example)

from nlpcloud import Client
client = Client("spacy", api_key="${NLP_CLOUD_API_KEY}")
text = "Barack Obama was born in Hawaii and served as US president."
entities = client.entities(text)
print(entities)

Sentiment (example)

# pseudocode — adapt to your chosen library
from transformers import pipeline
sentiment = pipeline("sentiment-analysis")
print(sentiment("I love working with NLP!"))

Language detection

from langdetect import detect
print(detect("Ceci est un test"))  # 'fr'

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requirements.txt (starter)

jupyterlab
nlpcloud
transformers
torch
spacy
langdetect
python-dotenv
requests
black    # optional, for formatting
flake8   # optional, for linting

Adjust versions to match your runtime.

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.gitignore (starter)

.venv/
__pycache__/
.ipynb_checkpoints/
.env
.DS_Store
*.pyc

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Security & credentials

• Never commit .env or any file containing API keys. Add those to .gitignore and provide an .env.example with placeholder variable names (no secrets).
• If you accidentally push keys, rotate them immediately.

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Notebook tips for GitHub

• Clear large outputs before committing to reduce repo size (Kernel ▶ Restart & Clear Output).
• Optionally convert the notebook to a .py script for easier code review:

jupyter nbconvert --to script SmartText_AI.ipynb

• Use nbstripout or git filters to remove outputs automatically on commit.

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CI / GitHub Actions (optional)

Add a basic workflow to lint or run a quick smoke test on pushes (file: .github/workflows/ci.yml). Example tasks: pip install -r requirements.txt, run flake8, run a small smoke script.

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License

Suggested: MIT License. Include LICENSE file if you want to make this open-source.

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README badges (optional)

• Python version
• Build / CI
• License

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Contributing

Short notes: open issues, PRs welcome, keep notebooks small, add tests for scripts.

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Contact

Your Name — your.email@example.com

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If you want, I can also:

• generate a ready requirements.txt from the notebook imports,
• produce a .github/workflows/ci.yml sample,
• add an MIT license file,
• create small src/ example scripts extracted from the notebook.

Tell me which of those you'd like and I will add them to the repo files.