taizun 0.1.1

A Python library for simplifying tasks and enhancing generative AI workflows.

taizun

taizun is a Python library that simplifies machine learning tasks by providing utility functions for Natural Language Processing (NLP) and Computer Vision. It leverages free pre-trained models from Hugging Face, making advanced AI tasks accessible and easy to use.

Installation

Install the library using pip:

pip install taizun

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Features

Natural Language Processing (NLP)

1. Summarize Text

   • Summarize long texts into concise, readable summaries.
   • Usage:

     from taizun import summarize_text
     summary = summarize_text("Your long text here...")
     print(summary)
     

2. Named Entity Recognition (NER)

   • Identify entities like people, locations, and organizations in text.
   • Usage:

     from taizun import named_entity_recognition
     entities = named_entity_recognition("Barack Obama was the 44th President of the United States.")
     print(entities)
     

3. Text Generation

   • Generate text from a prompt using GPT-2.
   • Usage:

     from taizun import text_generation
     generated_text = text_generation("Once upon a time,")
     print(generated_text)
     

4. Sentiment Analysis

   • Analyze the sentiment of text (positive, negative, neutral).
   • Usage:

     from taizun import sentiment_analysis
     sentiment = sentiment_analysis("I love this library!")
     print(sentiment)
     

5. Remove Stopwords

   • Remove common stopwords from text.
   • Usage:

     from taizun import remove_stopwords
     cleaned_text = remove_stopwords("This is a sample text with stopwords.")
     print(cleaned_text)
     

6. Word Frequency Analysis

   • Analyze the frequency of words in a text.
   • Usage:

     from taizun import word_frequency_analysis
     frequency = word_frequency_analysis("This is a test. This test is only a test.")
     print(frequency)
     

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Computer Vision

1. Generate Image Caption

   • Generate a natural language caption for an image.
   • Usage:

     from taizun import generate_image_caption
     caption = generate_image_caption("path/to/image.jpg")
     print(caption)
     

2. Classify Image

   • Classify an image using a Vision Transformer (ViT) model.
   • Usage:

     from taizun import classify_image
     label = classify_image("path/to/image.jpg")
     print(label)
     

3. Detect Objects

   • Detect objects in an image with bounding boxes and labels.
   • Usage:

     from taizun import detect_objects
     objects = detect_objects("path/to/image.jpg")
     print(objects)
     

4. Resize Image

   • Resize an image to specified dimensions.
   • Usage:

     from taizun import resize_image
     resized = resize_image("path/to/image.jpg", "path/to/output.jpg", size=(300, 300))
     print(resized)
     

5. Convert to Grayscale

   • Convert an image to grayscale.
   • Usage:

     from taizun import convert_to_grayscale
     grayscale = convert_to_grayscale("path/to/image.jpg", "path/to/output_grayscale.jpg")
     print(grayscale)
     

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