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M7-Mind-Install

Problem-Statement:

We often see many people sharing their emotions and sufferings during a call to their friends or relatives. India reported an average 381 deaths by suicide daily in 2019, totalling 1,39,123 fatalities over the year, according to the latest National Crime Records Bureau (NCRB) data. Seeing suicide as a major concern in our Country we thought of doing something to prevent suicides. In order to prevent suicide, it is necessary to detect suicide-related thoughts and user's suicide ideation in real-world by sentiment analysis method.

Along with that, sometimes it is also important to understand any abusive expressions of the person on the other side of a phone call in order to prevent any future casualities.

Team Details

Team Name: Mind Install

Members:

  • Krishna Patel
  • Astel Thottankara
  • Dhrutik Patel
  • Dhruv Oza
  • Pranav Dherange

What it does

We get various random audio call files from the Internet. We then convert the audio into text and use our Machine Learning model to detect if any suicidal words were used in the particular call.

How we built it

  • The user interface will be built on Flutter where user will given an option to upload an audio file containing the conversation of the interested person
  • The audio file will be then sent to a Flask API deployed on Heroku. The Flask app will contain the logic to analyze the audio file.
  • Firstly it will be converted to a text file using Speech Recognition which will then be feeded into another Machine Learning model where it will be classified as suicidal, abusive, threatening or completely normal
  • The result of the model will then be sent as a response to the Flutter app where it will display the response of the model accordingly.

Tech Stack

  • PRAW(Python Reddit API Wrapper) for scraping reddit data
  • Flask
  • Flutter
  • Machine Learning
  • Speech Recognition
  • LDA(latent Dirichlet Allocation)

Challenges we ran into

  • We were unable to retrieve any dataset from Kaggle or any other platform and that's why we had to scrap data from Reddit's posts in order to get us a dataset.
  • We faced a major issue while using Heroku. Whenever we used to pass our POST request our server the model used to take very much time to process the input, and we came to knew that Heroku terminates it's functionalities of handling requests within 1 minute so we were not able to send response back from our server as our model being highly complex.
  • As we were using unsupervised learning in the model we were unable to predict accuracy, because there wasn't any labelled data.

Accomplishments that we're proud of

  • As our previous model was not able to run on Heroku because of high compelxity, we were successfully able to re-code the whole model with less time complexity.

What we learned

  • A simple phase of ideation about how great it is to discuss your ideas with team-mates and mentors.
  • Research is a necessary phase and include reading blog, research papers, and discussing what things can be implemented.