Stock Prediction System

To run streamlit application:

1. Go to file directory ..Debasish Test\Previous Model

streamlit run app.py

To Access frontend side of project, cd into Utkarsh Test.

To Access backend side of project, cd into Debasish Test and Amal Test.

To Access and run project as a whole, cd into final_project.

2. For running final project.

• Go to directory in final project.

cd "C:\Users\Debasish Ray\Desktop\stock\StockPredictor\final_project"

• Run the app file in streamlit.

streamlit run app.py

• Go to directory in stock_frontend

cd "stock_frontend"

• Run the scripts.

npm run start

• Then ,start the server by navigating in the file. (final_project\stock_frontend\data_backend)

cd data_backend

• Run node server

node server.js

Note: This project is still in production and will not resemble the final product.

Note (Information)

For this project, we have included a different repository with different models trained on different epoch cycles and parameters, which are usable and integratable in this project. Link to Model's Repository

docker run debasishray/streamlit-app:v1.0
docker stop debasishray/streamlit-app:v1.0

Github Packages

1. Create a replica of Docker image with different tag.
2. Check the image created.
3. Authenticate by using PAT (Personal Access Token).
4. Push that image in GitHub Packages.

docker tag debasishray/streamlit-app:v1.0 webapp

docker tag webapp ghcr.io/debasishray16/stockpredictor/webapp:latest
docker image ls

# For authentication
echo "pat-value" | docker login ghcr.io -u debasishray16 --password-stdin

# ghcr.io/<username>/<repository>
docker push ghcr.io/debasishray16/stockpredictor/webapp:latest