Updated README.md

.gitignore

@@ -1,3 +1,4 @@
 .env
 .DS_Store
-tumai
+tumai
+venv

README.md

@@ -5,36 +5,109 @@ sdk: streamlit
 sdk_version: "1.22.0"
 app_file: app.py
 ---
+
 # GoEmotions Dashboard - Analyzing Emotions in Text
 
-This is a Python script that uses Streamlit, Plotly, and the Hugging Face API to create a web-based dashboard for analyzing emotions in text.
+This is a Python script that uses Streamlit, Plotly, and the Hugging Face Inference API to create a web-based dashboard for analyzing emotions in text. Finally this dashboard is deployed on Hugging Face Spaces using GitHub Actions.
+
+## Pre-requisites:
 
-## Requirements:
+- Python 3.7 or higher
 
-Python 3.7 or higher
-Hugging Face API
+## Project Structure:
+```dir
+GoEmotions/
+├── app.py
+├── requirements.txt
+├── .env
+├── README.md
+├── assets/
+└── .github/workflows
+```
 
-## Installation
+## Setup
 
-- Clone this repository to your local machine.
-- Install the required packages using pip:
+`Step 1` - Clone this repository to your local machine using the following command, or open the repository in GitHub Codespaces.
+
+```bash
+git clone https://github.com/devansh-srivastav/GoEmotions.git
+```
+
+`Step 2` - Create and activate a new python virtual environment: (This step can be skipped if working on GitHub Codespaces!)
+
+```bash
+python -m venv venv
+```
+
+```bash
+venv\Scripts\activate
+```
+
+`Step 3` - Install the required packages using pip: (This step can be skipped if working on GitHub Codespaces as it automatically installs the requirements!)
 
 ```bash
 pip install -r requirements.txt
 ```
 
-- Create a free account on the Hugging Face website to get an API key.
+`Step 4`- Create a free account on the [Hugging Face website](https://huggingface.co/) and generate an API key (read).
+
+`Step 5` - Navigate to the root directory of the project.
+
+```bash
+cd GoEmotions
+```
+
+`Step 6`
 
 - Create a `.env` file in the root directory of the project and add your
 - Hugging Face API key like this: `HF_API_KEY=<your_api_key_here>`
 
-## Usage
+`Step 7` - Run the Streamlit app.
+
+```bash
+streamlit run app.py
+```
+
+or 
+
+```bash
+python -m streamlit run app.py
+```
+
+- If you want to run this application on GitHub Codespaces, you will need to add the following flags to the `streamlit run` command:
+
+```bash
+python -m streamlit run app.py --server.enableCORS false --server.enableXsrfProtection false
+```
+
+## Deployment to Spaces (CI/CD)
+
+`Step 1`
+Commit your code and push it to your GitHub repository
+
+`Step 2`
+Create a new Space on Hugging Face, add it as an additional remote to git and force push your code on Spaces:
+
+```bash
+git remote add space https://huggingface.co/spaces/HF_USERNAME/SPACE_NAME
+```
+
+```bash
+git push --force space main
+```
+
+`Step 3`
+In the main.yml, add your Hugging Face username and Space name to the variables 'HF_username' and 'HF_space_name'
+
+`Step 4`
+Create a new API key on Hugging Face (write) and add it as a secret to your GitHub Repository naming it as 'HF_TOKEN'.
+
+`Step 5`
+Trigger the CI/CD pipeline by a push or a pull request to your main branch.
 
-- Navigate to the root directory of the project.
-- Run the Streamlit app by typing `streamlit run app.py` or `python -m streamlit run app.py` in the command line.
-- For GitHub Codespaces, Run: `python -m streamlit run app.py --server.enableCORS false --server.enableXsrfProtection false`
+## Usage:
 
 - A web-based dashboard will open in your default browser.
 - Type or paste a text input in the text box provided.
-- The dashboard will display the detected emotions in a set of gauges, with each gauge representing the intensity of a specific emotion category.
-- The gauge colors are based on a predefined color map for each emotion category.
+- The dashboard will visualise the detected emotions in a set of gauges, with each gauge representing the intensity of a specific emotion category. The gauge colors are based on a predefined color map for each emotion category.
+- Moreover, the dashboard will display the results from Hate Speech Analysis and Sexism Detection models.