CrazeAnalytix/services/geminiService.ts

import { GoogleGenAI } from "@google/genai";
import { AggregatedData } from "../types";

// Declare process to avoid TypeScript errors without causing aggressive bundler shims
declare const process: any;

const SYSTEM_INSTRUCTION = `
You are a senior data analyst assistant for a retail dashboard called "Craze Analytix".
You have access to a detailed report of the currently filtered sales data.
The data includes Sell Out (Revenue in €), Units Sold, Product Lines, Customers/Markets, and Seasonality trends.

Your goal is to answer user questions specific to the provided data.
- If asked about "Trends" or "Seasonality", look at the Monthly Seasonality section.
- If asked about "Growth" or "Decline", look at the Top/Bottom Movers sections.
- If asked about specific Product Lines, look at the Product Line Breakdown.
- Always format numbers clearly (e.g., "€1.2M", "€5,200", "15k units").
- When comparing years, calculate the percentage difference if not explicitly provided.
- Keep answers professional, concise, and business-focused.
`;

// Helper to get API key safely
const getApiKey = (): string | undefined => {
  try {
    return process.env.API_KEY;
  } catch (e) {
    return undefined;
  }
};

const formatCurrency = (val: number) => `€${val.toLocaleString(undefined, {minimumFractionDigits: 0, maximumFractionDigits: 0})}`;
const formatUnits = (val: number) => `${val.toLocaleString()} units`;

export const queryGemini = async (
  question: string, 
  context: AggregatedData, 
  filteredRecordCount: number
): Promise<string> => {
  
  const apiKey = getApiKey();

  if (!apiKey) {
    return "API Key is missing. Please configure your environment variables (API_KEY) or check your .env file.";
  }

  try {
    const ai = new GoogleGenAI({ apiKey });

    // --- CONTEXT GENERATION ---
    // We construct a structured report mirroring the dashboard charts
    
    // 1. Totals by Year (KPI Cards)
    const yearlySummary = Object.entries(context.totalsByYear)
        .sort((a, b) => parseInt(b[0]) - parseInt(a[0])) // Descending years
        .map(([year, data]) => `  - ${year}: ${formatCurrency(data.sellOut)} | ${formatUnits(data.units)}`)
        .join('\n');

    // 2. Seasonality (Line Chart Data)
    // We simplify this to a CSV-like list for the AI to parse trends
    const seasonalitySummary = context.seasonality.map(p => {
        // Extract values for each year in the point
        const yearValues = context.availableYears.map(y => `${y}: ${formatCurrency(p[y] as number || 0)}`).join(', ');
        return `  - ${p.name}: [${yearValues}]`;
    }).join('\n');

    // 3. Top Movers (Growth Table) - Limit to Top 10
    const growthSummary = context.topMovers.slice(0, 10).map(m => 
        `  - ${m.line}: +€${m.sellOutGrowthValue.toLocaleString()} (${m.sellOutGrowthPercentage.toFixed(1)}%)`
    ).join('\n');

    // 4. Declining Movers (Decline Table) - Limit to Top 10
    const declineSummary = context.bottomMovers.slice(0, 10).map(m => 
        `  - ${m.line}: -€${Math.abs(m.sellOutGrowthValue).toLocaleString()} (${m.sellOutGrowthPercentage.toFixed(1)}%)`
    ).join('\n');

    // 5. Product Lines Overview (Bar Charts) - Limit to Top 50 to save tokens but give depth
    const topLinesSummary = context.byLine.slice(0, 50).map((l, i) => 
        `  ${i+1}. ${l.name}: ${formatCurrency(l.value)} | ${formatUnits(l.units)}`
    ).join('\n');

    // 6. Customer Distribution (Customer Chart)
    const customerSummary = context.byCustomer.map(c => 
        `  - ${c.name}: ${formatCurrency(c.value)}`
    ).join('\n');

    const fullReport = `
REPORT CONTEXT:
----------------
GLOBAL TOTALS:
Total Sell Out: ${formatCurrency(context.totalSellOut)}
Total Units: ${formatUnits(context.totalUnits)}
Records Analyzed: ${filteredRecordCount}
Years Available: ${context.availableYears.join(', ')}

YEARLY BREAKDOWN:
${yearlySummary}

MONTHLY SEASONALITY (Revenue Trends):
${seasonalitySummary}

FASTEST GROWING LINES (Year-over-Year):
${growthSummary}

DECLINING LINES (Year-over-Year):
${declineSummary}

TOP PRODUCT LINES (Revenue & Units):
${topLinesSummary}

PERFORMANCE BY CUSTOMER:
${customerSummary}
`;

    const response = await ai.models.generateContent({
      model: 'gemini-2.5-flash',
      contents: `Context Data:\n${fullReport}\n\nUser Question: ${question}`,
      config: {
        systemInstruction: SYSTEM_INSTRUCTION,
      }
    });

    return response.text || "I couldn't generate a response based on the data provided.";
  } catch (error: any) {
    console.error("Gemini API Error:", error);
    
    if (error.message && error.message.includes("Not implemented on this platform")) {
       return "System Error: The AI SDK detected a platform mismatch.";
    }

    return `Error: ${error.message || "An unexpected error occurred while analyzing the data."}`;
  }
};