System Requirements Document (SRD) for swift-prediction

1. Introduction

The swift-prediction project is a modern, responsive web application designed to assist users in predicting outcomes using machine learning models. This document outlines the system requirements for the project, ensuring clarity and alignment with the goals of the application. The project is tailored for Usee, based in India, and incorporates locale-specific considerations such as timezone (IST) and user accessibility.

The primary objective of swift-prediction is to provide an intuitive platform for users to input data and receive predictions, leveraging machine learning technologies. The system will initially simulate predictions using mock data, with provisions for integrating a trained model in the future.

2. System Overview

The swift-prediction system will consist of a responsive web interface and a backend API. The web interface will allow users to interact with the system, input data, and view predictions. The backend will handle data processing, simulate predictions, and return results to the frontend.

Key features include:

• A clean, modern UI with a professional theme.
• Input fields for user data.
• Simulated predictions with clear visual indicators.
• A simple "About" section explaining the benefits of AI in healthcare.

The system will be built using the following technologies:

• Frontend: React for a dynamic and responsive user interface.
• Backend: Python with FastAPI for efficient API handling.
• Database: MySQL for structured data storage.

3. Functional Requirements

• As a User, I should be able to access a homepage with a brief description of the project and a "Check Now" button.
• As a User, I should be able to navigate to a prediction page with input fields for data entry.
• As a User, I should be able to input my age, blood pressure, and symptoms to receive a prediction.
• As a User, I should be able to view the prediction result with a clear message and color indicators.
• As a User, I should be able to access an "About" section explaining the benefits of AI in simple terms.
• As a User, I should be able to use the website seamlessly on both desktop and mobile devices.

4. User Personas

1. General User

• Description: Individuals seeking predictions based on input data.
• Goals: To receive accurate and easy-to-understand predictions.
• Technical Expertise: Basic internet usage skills.
• Needs: A simple, intuitive interface with clear instructions.

2. Administrator

• Description: System administrators managing the backend and ensuring smooth operation.
• Goals: To monitor system performance and update the prediction model when trained.
• Technical Expertise: Proficient in backend technologies and machine learning.

5. Visuals Colors and Theme

The swift-prediction project will adopt a unique, professional color palette that aligns with its medical theme while maintaining a modern aesthetic.

• Background: #F4F9FF (light blue for a calming effect)
• Surface: #FFFFFF (clean white for content areas)
• Text: #333333 (dark gray for readability)
• Accent: #007BFF (vivid blue for buttons and highlights)
• Muted: #B0BEC5 (soft gray for secondary elements)

6. Signature Design Concept

The homepage of swift-prediction will feature an interactive neural network visualization as its centerpiece. This bold design element will immediately captivate users and establish the platform's AI-driven identity.

Details:

• Hero Section: A dynamic, animated neural network diagram that responds to user interactions. Nodes will light up and connect as the user hovers over them, symbolizing the predictive process.
• Background Animation: A subtle gradient shift from light blue to white, creating a sense of depth and motion.
• Micro-interactions: Buttons and icons will have smooth hover effects, with slight scaling and shadow transitions.
• Call-to-Action: A prominent "Check Now" button below the visualization, encouraging users to proceed to the prediction page.

This design will make the homepage unforgettable, blending aesthetics with functionality.

7. Non-Functional Requirements

• The system must load within 3 seconds on a standard broadband connection.
• The website must be mobile-responsive and compatible with all major browsers.
• The backend API must handle up to 100 concurrent requests.
• The system must ensure data privacy and not store user inputs permanently.

8. Tech Stack

Frontend

• React for web development.

Backend

• Python with FastAPI for API development.

Database

• MySQL with Alembic for migrations.

AI Models

• Simulated predictions using mock data (provisions for integrating a trained model later).

Tools

• Docker for local orchestration.
• Kubernetes for server-side orchestration.

9. Assumptions and Constraints

• The prediction model is not trained and will be simulated using mock data.
• Users are expected to have basic internet access and device compatibility.
• The system will initially support English as the primary language.

10. Glossary

• Frontend: The user-facing part of the application.
• Backend: The server-side logic handling data processing and API requests.
• Mock Data: Simulated data used to mimic real-world predictions.
• Responsive Design: A design approach ensuring the website works on all devices.
• Neural Network Visualization: A graphical representation of AI processes.