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Build AI Personal Study Assistant Bot

by sumukha Updated: Nov 2, 2021

This is Stella, an AI chatbot that runs on a web browser and capable of maintaining conversations with humans and also handle to-do lists. This project is for the HackMakers hackathon. - from Team Stellars.

development environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

vscodeby microsoft

TypeScript star image 130477 Version:1.66.2

License: Permissive (MIT)

Visual Studio Code

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vscodeby microsoft

TypeScript star image 130477 Version:1.66.2 License: Permissive (MIT)

Visual Studio Code
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notebookby jupyter

Jupyter Notebook star image 8966 Version:v7.0.0a2

License: Others (Non-SPDX)

Jupyter Interactive Notebook

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notebookby jupyter

Jupyter Notebook star image 8966 Version:v7.0.0a2 License: Others (Non-SPDX)

Jupyter Interactive Notebook
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Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

numpyby numpy

Python star image 20101 Version:v1.22.3

License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.

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numpyby numpy

Python star image 20101 Version:v1.22.3 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.
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Text mining

Libraries in this group are used for analysis and processing of unstructured natural language. The data, as in its original form aren't used as it has to go through processing pipeline to become suitable for applying machine learning techniques and algorithms.

nltkby nltk

Python star image 10427 Version:Current

License: Permissive (Apache-2.0)

NLTK Source

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nltkby nltk

Python star image 10427 Version:Current License: Permissive (Apache-2.0)

NLTK Source
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Machine Learning

We used to following libraries to train our model.

kerasby keras-team

Python star image 55007 Version:v2.9.0-rc2

License: Permissive (Apache-2.0)

Deep Learning for humans

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kerasby keras-team

Python star image 55007 Version:v2.9.0-rc2 License: Permissive (Apache-2.0)

Deep Learning for humans
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tensorflowby tensorflow

C++ star image 164372 Version:v2.9.0-rc1

License: Permissive (Apache-2.0)

An Open Source Machine Learning Framework for Everyone

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tensorflowby tensorflow

C++ star image 164372 Version:v2.9.0-rc1 License: Permissive (Apache-2.0)

An Open Source Machine Learning Framework for Everyone
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Request servicing via REST API

Web frameworks help build serving solution as REST APIs. The resources involved for servicing request can be handled by containerising and hosting on hyperscalers.

flaskby pallets

Python star image 58462 Version:2.1.1

License: Permissive (BSD-3-Clause)

The Python micro framework for building web applications.

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flaskby pallets

Python star image 58462 Version:2.1.1 License: Permissive (BSD-3-Clause)

The Python micro framework for building web applications.
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pickleby FriendsOfPHP

PHP star image 1428 Version:v0.7.2

License: Others (Non-SPDX)

PHP Extension installer

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pickleby FriendsOfPHP

PHP star image 1428 Version:v0.7.2 License: Others (Non-SPDX)

PHP Extension installer
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Kit Solution Source

hackathon_hackmakers_stellaby sumukha-7

Python star image 0 Version:Current

License: Permissive (MIT)

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hackathon_hackmakers_stellaby sumukha-7

Python star image 0 Version:Current License: Permissive (MIT)

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Deployment Information

Our solution and all dependent assets are available in the below repository.

The entire solution is available as a package to download and install from the source code repository. Prerequisite: Python3 & VSCode & Git Follow below instructions to download and deploy the solution. 1. Open VSCode 2. Open the command palette using "Ctrl+Shift+P" 3. Type "Git: Clone" and then enter this URL: "https://github.com/sumukha-7/hackathon_hackmakers_stella" 4. Choose a folder on your computer to store these files locally. 5. Run the "Train.py" file to train the model. 6. Next run "app.py" to start the bot. 7. You will then get a localhost url in the terminal, by clicking that url you will be redirected to your browser to interact with our bot. If you face any challenges while running our code, you might not have the library dependencies installed. In that case, open command prompt and run the following command: pip install (dependency_name)

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