pipprograms-tanuj 1.2.0

A hosted Python library containing 16 text processing and NLP programs, accessible from anywhere.

🚀 Pipprograms: 16 NLP & Text Processing Programs

A hosted Python library containing 16 text processing, NLP, and AI programs. You can package this library and host it (e.g., on PyPI or GitHub) to import it and run it from anywhere in the world.

---

✨ Features

• CLI Interface: Show or run any program with a single terminal command.
• Python API: Import and run/inspect the programs directly inside your own Python scripts.
• Automated Fallbacks: Programs automatically generate sample datasets if the default paths (like Colab /content/ paths) are missing, ensuring they work out-of-the-box on any machine.
• Backwards Compatible: Works with modern PEP 517 build backends and older setup.py-based systems.

---

📂 Included Programs

Program #	Name	Description	Key Libraries
1	TF-IDF Summarizer	Extracts key sentences to summarize text	nltk, numpy
2	Custom Word Cloud	Generates overlapping-free word frequency visual clouds	nltk, matplotlib
3	Sentiment Classifier (LR)	Sentiment Classifier using balanced Logistic Regression	pandas, scikit-learn
4	Sentiment Classifier (N-gram)	Classifies sentiment using custom-sized n-grams & Logistic Regression	pandas, scikit-learn
5	Sentiment Classifier (NB)	Classifies sentiment using balanced Naive Bayes	pandas, scikit-learn
6	Spam Detector	Classifies Ham vs. Spam using Random Forest	pandas, scikit-learn
7	Document Topic Modeling	Latent Dirichlet Allocation (LDA) document clustering	PyPDF2, scikit-learn
8	One Hot Encoding	Custom script to perform One Hot Encoding	numpy, pandas
9	TF-IDF + SVM	Text Classifier using TF-IDF and Support Vector Machine	scikit-learn
10	TF-IDF + KNN	Text Classifier using TF-IDF and K-Nearest Neighbors	scikit-learn
11	CountVectorizer + SVM	Text Classifier using CountVectorizer Bag-of-Words and SVM	scikit-learn
12	CountVectorizer + KNN	Text Classifier using CountVectorizer Bag-of-Words and KNN	scikit-learn
13	Visual for LDA (Bar)	Basic bar chart visualization for LDA topic distribution	matplotlib
14	Visual for LDA (Cloud)	Word cloud visualization for LDA topics	wordcloud, matplotlib
15	Single PDF in LDA	Runs LDA topic modeling on text extracted from a single PDF	PyPDF2, scikit-learn
16	Gemini Content Generator	Content generation using the Gemini API	google-genai

---

🛠️ Installation

1. From PyPI (Standard & Recommended)

Once the package is published to PyPI (see details below), anyone in the world can install it without Git:

pip install pipprograms-tanuj

2. From a Direct URL (No Git Required)

If you host the built .whl file on any web server, public storage (S3, Dropbox, website, etc.), anyone can install it directly via the URL:

pip install https://your-domain.com/path/pipprograms-1.0.0-py3-none-any.whl

3. Local Installation (for development)

If you want to run or test it locally from the source folder:

pip install -e .

---

🖥️ Command Line Usage

Once installed, the pipprograms command will be globally available on your system.

1. List all programs

pipprograms list

2. View the source code of a program (e.g. Program 1)

pipprograms show 1

3. Run a program (e.g. Program 3)

pipprograms run 3

---

🐍 Python Usage

You can import pipprograms in any python script:

import pipprograms

# 1. Print the source code of Program 1
pipprograms.show(1)

# 2. Get the source code as a string
code_str = pipprograms.get_code(1)

# 3. Run Program 1
pipprograms.run(1)

---

📤 How to Host and Publish this Package

Option A: Hosting on PyPI (Free and Public)

To make your package available via pip install pipprograms (or your chosen package name):

1. Create an account on PyPI.
2. Install Build Tools:

   pip install --upgrade build twine
   

3. Build the Distribution Archives:

   python -m build
   

   This will create a dist/ directory containing .tar.gz and .whl files.
4. Upload to PyPI:

   python -m twine upload dist/*
   

   Note: You will be prompted to enter your PyPI token/credentials.

Option B: Hosting Wheel (.whl) files on Cloud/HTTP Servers (No Git required)

If you don't want to publish to PyPI and want to keep it private or host it yourself:

1. Build the wheel file:

   python -m build
   

2. Upload the wheel file (located in dist/pipprograms-1.0.0-py3-none-any.whl) to any online storage that supports direct downloads (e.g. your personal web server, AWS S3, Google Cloud Storage, or a direct link from Dropbox/OneDrive).
3. Install it on any machine using the direct URL:

   pip install https://your-server.com/pipprograms-1.0.0-py3-none-any.whl