124 lines
5.9 KiB
TypeScript
124 lines
5.9 KiB
TypeScript
import { defineEventHandler, readBody } from 'h3';
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// Define an interface for the expected request body (subset of AsnafProfile)
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interface AsnafAnalysisRequest {
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monthlyIncome: string;
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otherIncome: string;
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totalIncome: string;
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occupation: string;
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maritalStatus: string;
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dependents: Array<any>; // Or a more specific type if you have one for dependents
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// Add any other fields you deem necessary for OpenAI to analyze
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}
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interface AidSuggestion {
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nama: string;
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peratusan: string;
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}
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// Define an interface for the expected OpenAI response structure (and our API response)
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interface AsnafAnalysisResponse {
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hadKifayahPercentage: string;
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kategoriAsnaf: string;
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kategoriKeluarga: string;
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cadanganKategori: string;
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statusKelayakan: string;
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cadanganBantuan: AidSuggestion[];
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ramalanJangkaMasaPulih: string;
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rumusan: string;
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}
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export default defineEventHandler(async (event): Promise<AsnafAnalysisResponse> => {
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const body = await readBody<AsnafAnalysisRequest>(event);
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// --- Placeholder for Actual OpenAI API Call ---
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// In a real application, you would:
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// 1. Retrieve your OpenAI API key securely (e.g., from environment variables)
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const openAIApiKey = process.env.OPENAI_API_KEY;
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if (!openAIApiKey) {
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console.error('OpenAI API key not configured. Please set OPENAI_API_KEY in your .env file.');
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throw createError({ statusCode: 500, statusMessage: 'OpenAI API key not configured' });
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}
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// 2. Construct the prompt for OpenAI using the data from `body`.
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// IMPORTANT: Sanitize or carefully construct any data from `body` included in the prompt to prevent prompt injection.
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const prompt = `You are an expert Zakat administrator. Based on the following applicant data: monthlyIncome: ${body.monthlyIncome}, totalIncome: ${body.totalIncome}, occupation: ${body.occupation}, maritalStatus: ${body.maritalStatus}, dependents: ${body.dependents.length}.
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Return JSON with keys: hadKifayahPercentage, kategoriAsnaf, kategoriKeluarga, cadanganKategori, statusKelayakan, cadanganBantuan, ramalanJangkaMasaPulih, rumusan.
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For 'cadanganBantuan', provide a JSON array of objects, where each object has a 'nama' (string, name of the aid) and 'peratusan' (string, e.g., '85%', representing suitability). Suggest 2-3 most relevant aid types.
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Example for cadanganBantuan: [{"nama": "Bantuan Kewangan Bulanan", "peratusan": "90%"}, {"nama": "Bantuan Makanan Asas", "peratusan": "75%"}].
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Full JSON Example: {"hadKifayahPercentage": "75%", ..., "cadanganBantuan": [{"nama": "Bantuan Kewangan Bulanan", "peratusan": "90%"}], ...}`;
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// Adjust the prompt to be more detailed and specific to your needs and desired JSON output structure.
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// 3. Make the API call to OpenAI
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try {
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const openAIResponse = await fetch('https://api.openai.com/v1/chat/completions', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'Authorization': `Bearer ${openAIApiKey}`,
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},
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body: JSON.stringify({
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model: 'gpt-3.5-turbo', // Or your preferred model like gpt-4
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messages: [{ role: 'user', content: prompt }],
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// For more consistent JSON output, consider using a model version that officially supports JSON mode if available
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// and set response_format: { type: "json_object" }, (check OpenAI documentation for model compatibility)
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}),
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});
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if (!openAIResponse.ok) {
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const errorData = await openAIResponse.text();
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console.error('OpenAI API Error details:', errorData);
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throw createError({ statusCode: openAIResponse.status, statusMessage: `Failed to get analysis from OpenAI: ${openAIResponse.statusText}` });
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}
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const openAIData = await openAIResponse.json();
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// Parse the content from the response - structure might vary slightly based on OpenAI model/API version
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// It's common for the JSON string to be in openAIData.choices[0].message.content
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if (openAIData.choices && openAIData.choices[0] && openAIData.choices[0].message && openAIData.choices[0].message.content) {
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const analysisResult = JSON.parse(openAIData.choices[0].message.content) as AsnafAnalysisResponse;
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return analysisResult;
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} else {
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console.error('OpenAI response structure not as expected:', openAIData);
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throw createError({ statusCode: 500, statusMessage: 'Unexpected response structure from OpenAI' });
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}
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} catch (error) {
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console.error('Error during OpenAI API call or parsing:', error);
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// Avoid exposing detailed internal errors to the client if they are not createError objects
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if (typeof error === 'object' && error !== null && 'statusCode' in error) {
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// We can infer error has statusCode here, but to be super safe with TS:
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const e = error as { statusCode: number };
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if (e.statusCode) throw e;
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}
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throw createError({ statusCode: 500, statusMessage: 'Internal server error during AI analysis' });
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}
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// --- End of Actual OpenAI API Call ---
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// The simulated response below this line should be REMOVED once the actual OpenAI call is implemented and working.
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/*
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console.log('Received for analysis in server route:', body);
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await new Promise(resolve => setTimeout(resolve, 2000)); // Simulate API delay
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const totalIncomeNumeric = parseFloat(body.totalIncome);
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let percentage = '50%';
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if (totalIncomeNumeric < 1000) percentage = '30%';
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else if (totalIncomeNumeric < 2000) percentage = '65%';
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else if (totalIncomeNumeric < 3000) percentage = '85%';
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else percentage = '110%';
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return {
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hadKifayahPercentage: percentage,
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kategoriAsnaf: 'Simulated - Miskin',
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kategoriKeluarga: 'Simulated - Miskin (50-100% HK)',
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cadanganKategori: 'Simulated - Miskin',
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statusKelayakan: 'Simulated - Layak (Miskin)',
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cadanganBantuan: [
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{ nama: 'Simulated - Bantuan Kewangan Bulanan', peratusan: '80%' },
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{ nama: 'Simulated - Bantuan Pendidikan Anak', peratusan: '65%' }
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],
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ramalanJangkaMasaPulih: 'Simulated - 6 bulan',
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rumusan: 'Simulated - Pemohon memerlukan perhatian segera.'
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};
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*/
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});
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