Perspective

c

A comprehensive online directory for all immigration programs and portfolio management services for Indian HNIs.

Perspective AI appears to be a Next.js-based web application designed to provide AI-powered life coaching services. The project aims to leverage artificial intelligence to offer personalized advice, goal setting, and potentially habit tracking for users seeking personal development assistance.

Tech Stack

Frontend:

  • Next.js 14.2.12 (React framework)

  • React 18

  • TypeScript

  • Tailwind CSS for styling

  • Radix UI for accessible component primitives

  • Framer Motion for animations

Backend:

  • Next.js API routes

  • OpenAI API (v4.62.1) for AI-powered analysis

Development Tools:

  • ESLint for code linting

  • TypeScript for static typing

  • Vercel for deployment (inferred from .gitignore)

System Architecture Overview

Frontend Layer

The frontend is built using Next.js, a React-based framework for server-side rendering and static site generation.


Key components:

  • app/page.tsx: The main entry point for the application, rendering the PerspectiveApp component.

  • React components (not directly visible in the provided files, but inferred from the project structure).

  • Tailwind CSS for styling (inferred from the components.json configuration).

The frontend is built using Next.js, a React-based framework for server-side rendering and static site generation.


Key components:

  • app/page.tsx: The main entry point for the application, rendering the PerspectiveApp component.

  • React components (not directly visible in the provided files, but inferred from the project structure).

  • Tailwind CSS for styling (inferred from the components.json configuration).

The frontend is built using Next.js, a React-based framework for server-side rendering and static site generation.


Key components:

  • app/page.tsx: The main entry point for the application, rendering the PerspectiveApp component.

  • React components (not directly visible in the provided files, but inferred from the project structure).

  • Tailwind CSS for styling (inferred from the components.json configuration).

Backend Layer

The backend is implemented using Next.js API routes, which allow for serverless function-like endpoints.


Key component:

  • app/api/analyze/route.ts: Handles POST requests for text analysis.

The backend is implemented using Next.js API routes, which allow for serverless function-like endpoints.


Key component:

  • app/api/analyze/route.ts: Handles POST requests for text analysis.

The backend is implemented using Next.js API routes, which allow for serverless function-like endpoints.


Key component:

  • app/api/analyze/route.ts: Handles POST requests for text analysis.

AI Integration Layer

The application integrates with OpenAI's GPT models for natural language processing and analysis.


Key features:

  • Uses the OpenAI API client (version 4.62.1).

  • Implements function calling for structured AI responses.

The application integrates with OpenAI's GPT models for natural language processing and analysis.


Key features:

  • Uses the OpenAI API client (version 4.62.1).

  • Implements function calling for structured AI responses.

The application integrates with OpenAI's GPT models for natural language processing and analysis.


Key features:

  • Uses the OpenAI API client (version 4.62.1).

  • Implements function calling for structured AI responses.

The application integrates with OpenAI's GPT models for natural language processing and analysis.


Key features:

  • Uses the OpenAI API client (version 4.62.1).

  • Implements function calling for structured AI responses.

Data Flow

  1. User input is collected through the frontend interface (PerspectiveApp component).

  1. The frontend sends a POST request to the /api/analyze endpoint.

  2. The API route processes the request:

  • Validates and sanitizes input.

  • Sends the input to the OpenAI API for analysis.

  • Receives and processes the AI's response.

  1. The structured analysis is sent back to the frontend.

  2. The frontend displays the analysis results to the user.

Configuration and Environment

  • next.config.mjs: Configures Next.js settings (currently empty, allowing for future customization).

  • .env.local (not tracked in git): Stores environment variables, including the OpenAI API key.

  • components.json: Configures UI component settings and aliases.

Deployment

While not explicitly specified, the project structure suggests deployment on a platform like Vercel, which is optimized for Next.js applications.

Security Considerations

  • API key management through environment variables.

  • Server-side API calls to OpenAI, protecting the API key from client-side exposure.

Core Functionality

The core of the backend functionality does several important things:

a. It sets up the OpenAI client with the API key from environment variables.

b. It defines a POST route that accepts user responses.

c. It sends these responses to the OpenAI API with a specific system prompt and user message.

d. It uses function calling to structure the AI's response.

e. It handles errors and returns the structured response or an error message.

import { NextResponse } from 'next/server'
import OpenAI from 'openai'

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
})

export async function POST(req: Request) {
  const { responses } = await req.json()

  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [
        {
          role: "system",
          content: "You are an empathetic AI assistant tasked with analyzing a user's psychological and emotional state based on their responses to reflective prompts. Provide a brief, insightful, and compassionate analysis, along with an emotional landscape, key themes, and extracted action items."
        },
        {
          role: "user",
          content: `Based on the following user responses, provide an analysis of their psychological and emotional state, including an emotional landscape (with emojis), key themes, and extracted action items: "${responses}"`
        }
      ],
      functions: [
        {
          name: "analyze_response",
          description: "Analyze the user's response and provide insights",
          parameters: {
            // ... (function parameters)
          }
        }
      ],
      function_call: { name: "analyze_response" }
    })

    const result = JSON.parse(completion.choices[0].message.function_call?.arguments || '{}')
    return NextResponse.json(result)
  } catch (error) {
    console.error('OpenAI API error:', error)
    return NextResponse.json({ error: 'Failed to analyze responses' }, { status: 500 })
  }
}

Product Development Suggestions

Core Features

  • User Management: Create a robust user authentication system to track progress and preferences.

  • Data Visualization: Utilize charts and graphs to provide clear insights into emotional trends.

  • Customizable Prompts: Allow users and therapists to tailor reflective prompts.

Functionality Extensions

  • Multilingual Support: Expand the analysis capabilities to accommodate various languages.

  • Wearable Integration: Incorporate physiological data from wearable devices for enhanced emotional analysis.

  • Peer Review: Implement a system for mental health professionals to validate AI-generated insights.

Technical Enhancements

  • Comprehensive Testing: Develop a thorough test suite to ensure product quality and reliability.

  • Privacy and Security: Implement advanced encryption and consider local processing options to protect user data.

  • Performance Optimization: Optimize API calls and caching strategies to improve response times.

  • Accessibility: Conduct an accessibility audit and make necessary adjustments to ensure the application is usable by everyone.