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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.
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.
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.
Machine Learning
We used to following libraries to train our model.
tensorflowby tensorflow
C++ 164372 Version:v2.9.0-rc1
An Open Source Machine Learning Framework for Everyone
tensorflowby tensorflow
C++ 164372 Version:v2.9.0-rc1 License: Permissive (Apache-2.0)
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.
Kit Solution Source
hackathon_hackmakers_stellaby sumukha-7
Python 0 Version:Current
hackathon_hackmakers_stellaby sumukha-7
Python 0 Version:Current License: Permissive (MIT)
Deployment Information
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)