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pages/api/search.js

@@ -1,29 +1,91 @@
-// This would be your actual API endpoint in a production app
-// For this example, we're using mock data in the frontend
+import { GoogleGenerativeAI } from "@google/generative-ai";
 
-export default function handler(req, res) {
+export default async function handler(req, res) {
   if (req.method !== 'POST') {
     return res.status(405).json({ message: 'Method not allowed' });
   }
 
   try {
-    // In a real implementation, you would:
-    // 1. Validate the request body
-    // 2. Query your database or external API
-    // 3. Return the results
-
-    const mockData = [
-      {
-        name: 'João Silva',
-        position: 'Prefeito',
-        location: `${req.body.query} - ${req.body.locationType}`,
-        status: 'ativo',
-        details: 'Gestor público com experiência em administração municipal.'
-      }
-    ];
-
-    res.status(200).json(mockData);
+    const { query, locationType, startDate, endDate } = req.body;
+
+    if (!query) {
+      return res.status(400).json({ message: 'Query parameter is required' });
+    }
+
+    // Get basic agent data (in a real app, this would come from your database)
+    const basicData = getBasicAgentData(query, locationType);
+
+    // Enhance with Gemini AI
+    const enhancedData = await enhanceWithGemini(basicData, {
+      location: query,
+      locationType,
+      startDate,
+      endDate
+    });
+
+    res.status(200).json(enhancedData);
   } catch (error) {
-    res.status(500).json({ message: 'Internal server error' });
+    console.error('API Error:', error);
+    res.status(500).json({
+      message: 'Internal server error',
+      error: error.message
+    });
+  }
+}
+
+function getBasicAgentData(query, locationType) {
+  // Mock data - replace with actual database query
+  return [
+    {
+      name: 'João Silva',
+      position: 'Prefeito',
+      location: `${query} - ${locationType}`,
+      status: 'ativo',
+      details: 'Gestor público com 10 anos de experiência.'
+    },
+    {
+      name: 'Maria Santos',
+      position: 'Secretária de Educação',
+      location: `${query} - ${locationType}`,
+      status: 'ativo',
+      details: 'Especialista em políticas educacionais.'
+    }
+  ];
+}
+
+async function enhanceWithGemini(agents, context) {
+  if (!process.env.NEXT_PUBLIC_GEMINI_API_KEY) {
+    throw new Error('Gemini API key not configured');
+  }
+
+  const genAI = new GoogleGenerativeAI(process.env.NEXT_PUBLIC_GEMINI_API_KEY);
+  const model = genAI.getGenerativeModel({ model: "gemini-pro" });
+
+  const enhancedAgents = [];
+
+  for (const agent of agents) {
+    const prompt = `
+      Analyze this public agent for potential irregularities:
+
+      Name: ${agent.name}
+      Position: ${agent.position}
+      Location: ${agent.location}
+      Status: ${agent.status}
+      Details: ${agent.details}
+
+      Provide a brief forensic analysis and risk assessment.
+    `;
+
+    const result = await model.generateContent(prompt);
+    const response = await result.response;
+    const analysis = response.text();
+
+    enhancedAgents.push({
+      ...agent,
+      analysis,
+      riskScore: Math.floor(Math.random() * 100) // In real app, this would come from analysis
+    });
   }
+
+  return enhancedAgents;
 }