The problem Sanjeevni Ai solves

Shortage of medical professionals.
Lack of comprehensive disease outbreak data
in rural regions to the government.
Personalized Medical Assistance with Context Maintenance.
Enhanced Confidentiality with Blockchain.

Challenges we ran into

Challenges we faced:

Voice Speech Recognition: We struggled with various ML models, but on the first day, none of them worked. Eventually, we successfully implemented voice recognition using the Webspeech API, which understands all regional languages.

Voice Translation: Translating from regional languages to English was a major hurdle since our health-assistant model only understands English. We overcame this challenge by integrating the Google Translate API.

Generating Responses with LLM: Initially, we received very generic answers from the health-assistant model. However, after fine-tuning the model with health research papers, we significantly improved its accuracy.

Text-to-Speech in Regional Languages: Implementing text-to-speech was also a tough task, as we needed to consider the accents of each language. We addressed this challenge by using the Google Cloud Text-to-Speech (TTS) npm library.

Blockchain Smart Contract Implementation for Securing User Info: Interacting with smart contracts without requiring users to create wallets posed a challenge. However, we successfully solved this problem by leveraging the Biconomy Gasless SDK toolkit.