AI Team Builder - Inter IIT 13.0

Setup Instructions

Setup video in docs/.

1. Create and Activate Python Virtual Environment

python -m venv env
source env/bin/activate

2. Install Dependencies

pip install -r requirements.txt

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Running the APIs and UI

Run Model API

1. Navigate to the model/ directory:

   cd model

2. Start the Model API:

   python model_API.py

Run Squads API

1. Open a new terminal with the same virtual environment activated.
2. Run the Squads API:

   python Squads_API.py

Run Aggregate Stats API

1. Open a new terminal with the same virtual environment activated.
2. Run the Aggregate Stats API:

   python Aggregate_stats_API.py

Run Streamlit App

1. Open a new terminal with the same virtual environment activated.
2. Navigate to the model/ directory:

   cd model

3. Start the Streamlit app:

   streamlit run app1.py

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Running the Product UI

1. Install Yarn (if not already installed):

npm install --global yarn

2. Navigate to the UI/ Folder

cd UI

3. Install Dependencies

yarn install

4. Start the Development Server

yarn dev

5. Access the UI

Open your browser and navigate to:

http://localhost:3000

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Directory Structure

• model/: Contains all the logic for the methodology implemented and other experiments.
• data_preprocessing/: Handles data cleaning, transformation, and preparation.
• UI/: Contains the Product UI providing an interactive experience to predict the optimal team.

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Dependencies

Ensure you have the following installed:

• Python (version specified in requirements.txt)
• Node.js (latest stable version)
• Yarn (latest stable version)

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How to Run the Product UI?

Refer to the Product UI README for detailed instructions.

Steps (summary):

1. Install Yarn if not already installed:

   npm install --global yarn

2. Navigate to the UI/ folder:

   cd UI

3. Install dependencies:

   yarn install

4. Start the development server:

   yarn dev

5. Access the UI in your browser at:

   http://localhost:3000
   

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How to Run the Model UI?

Refer to the Model README for detailed instructions.

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Preprocessing

Refer to the Data Preprocessing README for instructions on data preprocessing.