![Screenshot 2023-09-05 at 13 21 13](https://private-user-images.githubusercontent.com/111709030/265625909-38010bfb-3c78-4345-bcea-9ceb0d7f7fe1.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MjMzNjc2MjYsIm5iZiI6MTcyMzM2NzMyNiwicGF0aCI6Ii8xMTE3MDkwMzAvMjY1NjI1OTA5LTM4MDEwYmZiLTNjNzgtNDM0NS1iY2VhLTljZWIwZDdmN2ZlMS5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwODExJTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDgxMVQwOTA4NDZaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1lYTRjNWY5NzJiZTM2NmQwY2MwM2FlMmMwN2JkYzViMmM1MWQwYzcxNTMxNjlhOWVkM2Y0YzMzMzg5MTViMTg3JlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.Nd_nI4MO0T6vP2b1rb3pVC4QX_VC6L3_MWLoP7N7QKg)
This is a Python-based web application that performs Emotion Detection using Natural Language Processing (NLP) techniques. It leverages a pre-trained machine learning model to analyze text input and determine the underlying emotion expressed in the text.
The web application is built using the Flask web framework, making it easy to deploy and interact with via a web browser. Users can input text, trigger the analysis, and receive the detected emotion as a result.
- Text input for analysis
- Emotion detection using NLP
- User-friendly web interface
- Flask-based web application
- Easy deployment and integration
Before you begin, ensure you have met the following requirements:
- Python (>= 3.6)
- Flask (
pip install flask
) - [Additional dependencies, if any]
-
Clone this repository:
-
Navigate to the project directory:
-
Install required dependencies:
-
Start the Flask application:
-
Open a web browser and navigate to
http://localhost:5000
(or the appropriate address where your app is running). -
Enter the text you want to analyze in the provided input field.
-
Click the "Run Emotion Analysis" button to trigger the emotion detection.
-
The detected emotion will be displayed on the web page.
Contributions are welcome! If you would like to contribute to this project, please follow these steps:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Make your changes and commit them.
- Push your changes to your fork.
- Create a pull request with a clear description of your changes.
This project is licensed under the Apache License 2.0 - see the LICENSE.md file for details.