This GitHub repository for the Smart Bridge externship program. Our Flask website effortlessly translates sign language gestures, detects and interprets emotions, fostering inclusive and seamless communication.

Link of the Demo Video of the Project:

• Name: CH Keerthana

  • Registraion number: 208x1a05d6
  • Gmail: [email protected]

• Name: N Ajay Kumar

  • Registraion number: 208x1a05f5
  • Gmail: [email protected]

• Name: Y Bujji Krishna Veni

  • Registraion number: 208x1a05i6
  • Gmail: [email protected]

• Name: T Somanad Sandeep

  • Registraion number: 218x1a0520
  • Gmail: [email protected]

• *Name:K Gowtham *

  • Registraion number: 208x1a05j0*
  • Gmail: [email protected]

.
├── Project Report
│   └── Vehicle damageDamageAssessmentAndCostEstimator_Report.pdf
├── README.md
├── Project
│   ├── Flask_app
│   ├── IPYNB Files
│   └── README.md
└── TeamMembers_Assignments
    ├── 20BKT0097_Rishabh
    ├── 20BKT0123_Tejeswar
    ├── 20BKT0101_Harika
    ├── 20BRS1038_Aniket
    └── README.md

This Repo contains the following structure:

• Project Report: Contains the SmartBridge Externship Project report in PDF format.
• README.md: The main readme file providing an overview of the repository.
• Project: This directory includes the following subdirectories:
  • Flask_app: Contains the code implementation and all the files required to launch the Flask Application.
  • IPYNB Files: Contains the IPYNB files used for training our Emotion and Sign Language Detection models.
  • README.md: Provides additional information about the SignLanguage_and_EmotionDetection folder.
• TeamMembers_Assignments: Contains separate directories for each team member's assignments as assigned by the SmartBridge Externship program. Each directory is named after the respective team member's registration number.

This organized structure helps categorize the different components and assignments related to the SmartBridge Externship project.

Steps for running the Flask Application is mentioned in the ./Project/Flask_app/ directory

The IPYNB files for training the Convolutional Neural Networks (CNN) in all three models ofdamage estimation, along with other specifications and details can be found in the ./Project/IPYNB Files/ directory.

By accessing these IPYNB files, users can gain insights into the training process, model architecture, and evaluation metrics employed for both Emotion and Sign Language Detection. Additionally, the associated Datasets utilized for training these models are also included.