Google Gemini AI Integration

SCRIPT_PROP.setProperty("GEMINI_KEY", "AIza...");

https://generativelanguage.googleapis.com/v1beta/models/gemini-flash-latest:generateContent?key={API_KEY}

{
  "contents": [
    {
      "parts": [
        {
          "text": "Your prompt here"
        }
      ]
    }
  ]
}

{
  "candidates": [
    {
      "content": {
        "parts": [
          {
            "text": "{\"description\": \"Coffee\", \"amount\": 250}"
          }
        ]
      }
    }
  ]
}

{
  "action": "process_text",
  "text": "Coffee with team 350 rupees",
  "mode": "add"
}

You are a financial assistant.
CONTEXT: The user wants to ADD a transaction.
USER INPUT: "Coffee with team 350 rupees"
HISTORY:
Starbucks (250) [Food]
Uber (120) [Transport]
...

TASK:
1. Extract: Description, Amount, Category (Food, Transport, Tech, Invest, Income, Misc).
2. Analyze: Compare this new expense to the HISTORY. Is it higher than usual? Is it a repeat?
3. Insight: Write a very short, witty, or helpful insight (max 15 words) about this specific spend.

OUTPUT JSON STRICTLY:
{
  "description": "String",
  "amount": Number,
  "category": "String",
  "type": "DEBIT" or "CREDIT",
  "insight": "String"
}

{
  "description": "Coffee with team",
  "amount": 350,
  "category": "Food",
  "type": "DEBIT",
  "insight": "Above your usual coffee spend by ₹100. Team bonding is worth it!"
}

{
  "action": "process_text",
  "text": "How much did I spend on food this week?",
  "mode": "ask"
}

You are a financial analyst.
CONTEXT: The user is ASKING a question about their finances.
USER INPUT: "How much did I spend on food this week?"
HISTORY:
Coffee (250) [Food]
Lunch (450) [Food]
Uber (120) [Transport]
...

TASK:
Answer the user's question based strictly on the HISTORY data provided.
Be concise (max 30 words).

OUTPUT JSON STRICTLY:
{
  "answer": "String"
}

{
  "answer": "You spent ₹1,850 on food this week across 8 transactions. That's ₹150 below your weekly average."
}

// Fetch last 40 transactions as context
const historyData = sheet.getRange(
  Math.max(1, sheet.getLastRow() - 40),
  1, 41, 4
).getValues();

const historyContext = historyData
  .map(r => `${r[1]} (${r[2]}) [${r[3]}]`)
  .join("\n");

// Include in prompt
const prompt = `
  HISTORY:
  ${historyContext}
  
  USER INPUT: "${userText}"
  ...
`;

const aiRes = UrlFetchApp.fetch(aiUrl, {
  method: 'post',
  contentType: 'application/json',
  payload: JSON.stringify(aiPayload)
});

const aiJson = JSON.parse(aiRes.getContentText());
const rawText = aiJson.candidates[0].content.parts[0].text;

// Clean markdown code blocks
const cleanJson = rawText
  .replace(/```json/g, '')
  .replace(/```/g, '')
  .trim();

const parsed = JSON.parse(cleanJson);

async function sendToAI(mode) {
  const input = document.getElementById('omniInput');
  const txt = input.value; // "Coffee 250"
  
  const res = await fetch(SCRIPT_URL, {
    method: 'POST',
    body: JSON.stringify({
      action: "process_text",
      text: txt,
      mode: mode // "add" or "ask"
    })
  });
  
  const data = await res.json();
  
  if (data.status === "Success") {
    // Update balance and show AI insight
    appData.balance = parseFloat(data.balance);
    document.getElementById('aiTipBox').innerText = data.ai_response;
  }
}

async function handleManualFallback(text) {
  const amountMatch = text.match(/\d+(\.\d+)?/);
  const amount = amountMatch ? parseFloat(amountMatch[0]) : 0;
  const desc = text.replace(amountMatch ? amountMatch[0] : '', '').trim() || "Manual Expense";
  const type = (desc.toLowerCase().includes('salary') || desc.toLowerCase().includes('income')) ? 'CREDIT' : 'DEBIT';
  
  if (amount > 0) {
    const manualTx = {
      date: new Date().toISOString(),
      description: desc,
      amount: amount,
      category: type === 'CREDIT' ? 'Income' : 'General',
      type: type
    };
    
    // Save locally and sync to sheet
    appData.balance = (type === 'CREDIT') ? (appData.balance + amount) : (appData.balance - amount);
    appData.expenses.push(manualTx);
  }
}

try {
  const aiRes = UrlFetchApp.fetch(aiUrl, {...});
  const aiJson = JSON.parse(aiRes.getContentText());
  const rawText = aiJson.candidates[0].content.parts[0].text;
  const cleanJson = rawText.replace(/```json/g, '').replace(/```/g, '').trim();
  const parsed = JSON.parse(cleanJson);
  
  // Process based on mode...
  
} catch (e) {
  return ContentService.createTextOutput(
    JSON.stringify({
      status: "Error",
      message: "AI Failed: " + e.message
    })
  ).setMimeType(ContentService.MimeType.JSON);
}

const SpeechRecognition = window.SpeechRecognition || window.webkitSpeechRecognition;
let recognition = new SpeechRecognition();

recognition.continuous = false;
recognition.lang = 'en-IN';
recognition.interimResults = false;

recognition.onresult = (event) => {
  const transcript = event.results[0][0].transcript;
  // "Coffee two hundred fifty"
  
  document.getElementById('omniInput').value = transcript;
  // User clicks "ADD" → sends to Gemini for processing
};

// Only send last 40 transactions (not entire sheet)
const historyData = sheet.getRange(
  Math.max(1, sheet.getLastRow() - 40),
  1, 41, 4
).getValues();

OUTPUT JSON STRICTLY:
{
  "description": "String",
  "amount": Number,
  "category": "String"
}

try {
  // AI processing
} catch (e) {
  if (mode === 'add') {
    handleManualFallback(txt); // Regex parsing
  } else {
    showError("AI Offline. Can't analyze right now.");
  }
}