Search and summarize the web with ease!
Project description
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
andfilter_value
parameters in thesearch
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.
Project details
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