Summer Search

summer-search is a Python package that provides a simple interface for searching the web, extracting relevant content, and generating a summary based on the extracted information. The package leverages popular libraries such as requests, BeautifulSoup, and transformers to achieve its functionality.

Installation

You can install the package using pip:

pip install summer-search

Requirements:

• bs4 (Beautiful Soup 4):
• requests:
• transformers:
• sentencepiece:
• tensorflow:
• torch:

checkout the requirements.txt

 pip install -r requirements.txt

Make sure to install these dependencies before using the summer-search package to ensure all the required libraries are available.

Usage

from SummerSearch import summerSearch

# Create an instance
searcher = summerSearch()
print("Ready to search and summarize!")

# Perform a search
while True:

    # query to search 
    search_query = input("Enter a search query: ")
    raw_paragraph = searcher.search(search_query=search_query,filter="fixed_index",filter_value=1)
    print("Generating summary...")

    #specifying the model
    model = "t5-small"

    #summerization
    result = searcher.summarize(raw_paragraph, model)

    # Print the results
    print("\nSearch Query:", result["search_query"])
    print("\nSummary:", result["summary"])
    print("\nReference Link:", result["reference"])
    print("\nLearn More Links:", result["learn_more"])
    print("\nAdditional Links:", result["all_links"])

Documentation

• summerSearch Class

Methods

• search(search_query, filter="accuracy", filter_value=2): Performs a search and returns the raw paragraph.

  • search_query: The user's search query.
  • filter: Filtering option ("accuracy" or "fixed_index").
  • filter_value: Value based on the selected filter (default is 2).

• summarize(raw_paragraph, model): Summarizes the raw paragraph using a specified model.

  • raw_paragraph: The raw text to be summarized.
  • model: The summarization model to use.

Summarization Models

The summerSearch class supports the following summarization models:

• t5-small: A small variant of the T5 (Text-to-Text Transfer Transformer) model for general and basic summaries.

• facebook/bart-large-cnn: The BART (BART: Denoising Sequence-to-Sequence Pre-training) model, specifically the large CNN variant, for general and more proper summaries.

• kabita-choudhary/finetuned-bart-for-conversation-summary: A fine-tuned BART model for conversation summaries.

Feel free to choose the model that best fits your requirements and experiment with different models to observe variations in summarization results.

Notes

Feel free to explore and experiment with the package

• You can always contribute to the package!
• The package uses a combination of web scraping and summarization techniques to provide relevant information based on the user's search query.
• The filter and filter_value parameters in the search method allow users to customize the search process based on accuracy or a fixed index.
• The summarize method utilizes the Hugging Face Transformers library for text summarization.