# 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:
```cmd
pip install summer-search
```
### Requirements:
- **bs4 (Beautiful Soup 4):**
- **requests:**
- **transformers:**
- **sentencepiece:**
- **tensorflow:**
- **torch:**
> checkout the [requirements.txt](https://github.com/Cozmeh/SummerSearch/blob/main/requirements.txt)
```bash
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
```python
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.
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"description": "\r\n# Summer Search\r\n\r\n\r\n\r\n**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.\r\n\r\n\r\n\r\n## Installation\r\n\r\n\r\n\r\nYou can install the package using pip:\r\n\r\n\r\n\r\n```cmd\r\n\r\npip install summer-search\r\n\r\n```\r\n\r\n\r\n\r\n\r\n\r\n### Requirements:\r\n\r\n\r\n\r\n- **bs4 (Beautiful Soup 4):**\r\n\r\n- **requests:**\r\n\r\n- **transformers:**\r\n\r\n- **sentencepiece:**\r\n\r\n- **tensorflow:**\r\n\r\n- **torch:**\r\n\r\n> checkout the [requirements.txt](https://github.com/Cozmeh/SummerSearch/blob/main/requirements.txt)\r\n\r\n ```bash\r\n\r\n pip install -r requirements.txt\r\n\r\n ```\r\n\r\n \r\n\r\n\r\n\r\nMake sure to install these dependencies before using the `summer-search` package to ensure all the required libraries are available.\r\n\r\n\r\n\r\n## Usage\r\n\r\n\r\n\r\n```python\r\n\r\nfrom SummerSearch import summerSearch\r\n\r\n\r\n\r\n# Create an instance\r\n\r\nsearcher = summerSearch()\r\n\r\nprint(\"Ready to search and summarize!\")\r\n\r\n\r\n\r\n# Perform a search\r\n\r\nwhile True:\r\n\r\n\r\n\r\n # query to search \r\n\r\n search_query = input(\"Enter a search query: \")\r\n\r\n raw_paragraph = searcher.search(search_query=search_query,filter=\"fixed_index\",filter_value=1)\r\n\r\n print(\"Generating summary...\")\r\n\r\n\r\n\r\n #specifying the model\r\n\r\n model = \"t5-small\"\r\n\r\n\r\n\r\n #summerization\r\n\r\n result = searcher.summarize(raw_paragraph, model)\r\n\r\n\r\n\r\n # Print the results\r\n\r\n print(\"\\nSearch Query:\", result[\"search_query\"])\r\n\r\n print(\"\\nSummary:\", result[\"summary\"])\r\n\r\n print(\"\\nReference Link:\", result[\"reference\"])\r\n\r\n print(\"\\nLearn More Links:\", result[\"learn_more\"])\r\n\r\n print(\"\\nAdditional Links:\", result[\"all_links\"])\r\n\r\n```\r\n\r\n\r\n\r\n## Documentation\r\n\r\n\r\n\r\n- `summerSearch` Class\r\n\r\n\r\n\r\n#### Methods\r\n\r\n\r\n\r\n- `search(search_query, filter=\"accuracy\", filter_value=2)`: Performs a search and returns the raw paragraph.\r\n\r\n - `search_query`: The user's search query.\r\n\r\n - `filter`: Filtering option (\"accuracy\" or \"fixed_index\").\r\n\r\n - `filter_value`: Value based on the selected filter (default is 2).\r\n\r\n\r\n\r\n- `summarize(raw_paragraph, model)`: Summarizes the raw paragraph using a specified model.\r\n\r\n - `raw_paragraph`: The raw text to be summarized.\r\n\r\n - `model`: The summarization model to use.\r\n\r\n \r\n\r\n\r\n\r\n## Summarization Models\r\n\r\n\r\n\r\nThe `summerSearch` class supports the following summarization models:\r\n\r\n\r\n\r\n- **t5-small**: A small variant of the T5 (Text-to-Text Transfer Transformer) model for general and basic summaries.\r\n\r\n\r\n\r\n- **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.\r\n\r\n\r\n\r\n- **kabita-choudhary/finetuned-bart-for-conversation-summary**: A fine-tuned BART model for conversation summaries.\r\n\r\n\r\n\r\n\r\n\r\nFeel free to choose the model that best fits your requirements and experiment with different models to observe variations in summarization results.\r\n\r\n\r\n\r\n## Notes\r\n\r\nFeel free to explore and experiment with the package\r\n\r\n- You can always contribute to the package!\r\n\r\n- The package uses a combination of web scraping and summarization techniques to provide relevant information based on the user's search query.\r\n\r\n- The `filter` and `filter_value` parameters in the `search` method allow users to customize the search process based on accuracy or a fixed index.\r\n\r\n- The `summarize` method utilizes the Hugging Face Transformers library for text summarization.\r\n\r\n",
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