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Merge branch 'main' of hf.co:spaces/ds-meteors/find_my_book
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README.md
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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# Recommendation system project
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# Find my book
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### Additional subtask:
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# Find a dish for me
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# Elbrus Bootcamp | Phase-2 | Team Project
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## Team
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* [Daniil Lvov](https://huggingface.co/Norgan97)
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* [Dmitry Budazhapov](https://huggingface.co/DmitryDorzhievich)
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* [Larisa Khlapushina](https://huggingface.co/Larrisa)
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___
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## Tasks
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In this project, our team has developed a book search system based on user requests.
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The service takes a user's description of the book as input and returns a specified number of suitable options.
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In addition to book search, we can offer a system that will find the desired dish and a link to its recipe.
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In this case, the user only needs to enter an approximate description of the characteristics and ingredients.
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We have implemented a mechanism for analyzing and comparing textual descriptions of dishes,
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which allows for effectively finding alternatives and variations of desired dishes.
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The work represents an innovative approach to searching for culinary recipes and can be useful for both professional chefs and culinary enthusiasts.
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___
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## Contents
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1. Parsing information from websites.
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2. Vectorization using a model *rubert-tiny2*
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3. Finding similar vectors using *faiss*.
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___
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## Deployment
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The service is implemented on [Streamlit](https://huggingface.co/spaces/ds-meteors/find_my_book)
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_
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## How to run locally?
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## To run the provided applications on your computer, follow these steps:
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1. Clone this repository to your local machine.
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2. Install the required libraries by running the command *pip install -r requirements.txt* in your terminal or command prompt.
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3. Once the libraries are installed, navigate to the repository's directory in your terminal.
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4. Run the command *streamlit run main.py* in your terminal to start the application.
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This will launch the Streamlit server, and you can access the applications by opening a browser window and navigating to the specified URL.
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