The problem Ayur solves

Ayur Healthcare Platform revolutionizes access to healthcare by offering a comprehensive suite of services, making essential medical tasks more convenient and ensuring safer, more personalized care for users:

Appointment Scheduling: Easily book appointments with healthcare providers, eliminating the hassle of long wait times and streamlining the scheduling process.

Telemedicine Consultations: Access healthcare professionals remotely for consultations, enabling timely medical advice and reducing the need for physical visits to healthcare facilities.

Predictive Disease Diagnosis: Utilize advanced AI algorithms for early detection of health issues, empowering proactive management of medical conditions and prevention of complications.

Personalized Medicine Recommendations: Receive tailored medication suggestions based on individual health profiles and predictive analytics, enhancing treatment effectiveness and reducing adverse reactions.

AI Health Kiosk: Interact with intelligent health kiosks equipped with cutting-edge technology for on-the-go consultations and health assessments, ensuring convenient access to healthcare services in various settings.

Community Health Education: Engage with educational resources and community initiatives to promote health awareness and empower individuals with the knowledge to make informed decisions about their well-being.

Ayur Healthcare Platform transforms traditional healthcare practices, offering a user-centric approach that prioritizes accessibility, convenience, and personalized care, ultimately leading to improved health outcomes and enhanced quality of life for users.

Challenges we ran into

Challenges Faced:

During the development of Ayur Healthcare Platform, one significant challenge revolved around deploying OpenCV and machine learning (ML) models effectively within the platform. OpenCV deployment posed complexities due to the heavy computational requirements, while integrating ML models required careful consideration of performance and scalability.

How We Overcame It:

Exploration of Deployment Options: We initially attempted to deploy OpenCV and ML models directly within the frontend application. However, this approach resulted in performance bottlenecks and increased load times.

Utilization of Streamlit: After experimentation, we found that hosting the OpenCV models separately using Streamlit provided a lightweight and efficient environment for execution. Streamlit's simplicity and ease of deployment allowed us to overcome performance issues and ensure optimal functionality of the models.

Integration with React Application: We established communication between the React frontend and Streamlit backend using RESTful APIs, enabling seamless integration of the ML models into the platform. This approach facilitated data exchange and ensured a cohesive user experience across the platform.