agent.js

const { InferenceClient } = require("@huggingface/inference");
const { google } = require('googleapis');
const nodemailer = require('nodemailer');
require('dotenv').config();
const EXPERIENCE = [
    {
        company: "Unikul Solutions Pvt Ltd.",
        role: "Software Developer",
        date: "Aug 2024 - Present",
        description: "Assisted in developing web-based modules using Python and RESTful APIs. Backend-database integration for analytics dashboards. Built API endpoints. Git version control, CI/CD pipelines. Managed a team and mentored juniors. Implemented Python automation scripts."
    },
    {
        company: "Nexus Info",
        role: "AI/ML Intern",
        date: "Jun 2024 - Jul 2024",
        description: "Data cleaning, feature engineering, and model training using Python and Scikit-learn. Developed chatbots using Python, Flask, and Ollama API. JSON structured data storage."
    }
];
const MY_SKILLS = "Python, REST APIs, FastAPI,Javascript,React.js, Node.js, Flask, Scikit-learn, JSON, Git, CI/CD, Automation.";
const hf = new InferenceClient(process.env.HF_TOKEN);

// --- 1. YOUR RESUME DICTIONARY & SOCIAL LINKS ---
const RESUMES = {
    "dev_mnc": "https://drive.google.com/file/d/1V3MdkM4PdDPqHYHoZ5X0-RgJ6RgLQit1/view?usp=sharing",
    "dev_startup": "https://drive.google.com/file/d/1XHDyoRFuhMcOdQx8Pc_79VxXtdPDYsRw/view?usp=sharing",
    "analyst_mnc": "https://drive.google.com/file/d/17MewuAi1JNWoi2kwRCEMFj_tUjzvpX5k/view?usp=sharing",
    "analyst_startup": "https://drive.google.com/file/d/1JO-hzswARdQ2sJ2Q1ApY-fbmhg1giKQh/view?usp=sharing"
};



const SOCIALS = `
Best Regards,
Kanak Megha
Portfolio: https://kanak-megha-portfolio.vercel.app/
GitHub: https://github.com/kanakmegha
LinkedIn: https://linkedin.com/in/kanakmegha
`;

// --- 2. SETUP ---
// --- 2. AUTH SETUP ---
const auth = new google.auth.OAuth2(process.env.GOOGLE_CLIENT_ID, process.env.GOOGLE_CLIENT_SECRET, "https://developers.google.com/oauthplayground");
auth.setCredentials({ refresh_token: process.env.GOOGLE_REFRESH_TOKEN });
const sheets = google.sheets({ version: 'v4', auth });

const transporter = nodemailer.createTransport({
    service: 'gmail',
    auth: {
        type: 'OAuth2',
        user: process.env.EMAIL_USER,
        clientId: process.env.GOOGLE_CLIENT_ID,
        clientSecret: process.env.GOOGLE_CLIENT_SECRET,
        refreshToken: process.env.GOOGLE_REFRESH_TOKEN,
    },
});

const experienceContext = EXPERIENCE.map(e => `${e.role} at ${e.company}: ${e.description}`).join("\n");

async function runAgent() {
    try {
        console.log("📊 Fetching all data from Sheet1...");
        
        // READ FROM SHEET1
        const response = await sheets.spreadsheets.values.get({
            spreadsheetId: process.env.SHEET_ID,
            range: 'Sheet1!A2:G', 
        });

        const rows = response.data.values;
        if (!rows || rows.length === 0) return console.log("No data found.");

        for (let i = 0; i < rows.length; i++) {
            const rowIndex = i + 2;
            const [companyRaw, roleRaw, exp, location, email, status] = rows[i] || [];
            
            // Skip logic: Checks Column F (index 5) in the data we just fetched
            if (status?.toLowerCase() === 'sent' || !email || !email.includes('@')) continue;

            const company = companyRaw || "your company";
            const role = roleRaw || "Software Developer";

            try {
                console.log(`\n📧 [Row ${rowIndex}] Processing: ${company}`);

                // --- 3. AI RESEARCH ---
                let category = "dev_mnc"; 
                const research = await hf.chatCompletion({
                    model: "meta-llama/Llama-3.2-1B-Instruct",
                    messages: [{ role: "user", content: `Categorize: Company=${company}, Role=${role}. Reply ONLY with one: dev_mnc, dev_startup, analyst_mnc, or analyst_startup.` }],
                    max_tokens: 10
                });
                const aiResult = research.choices[0].message.content.trim().toLowerCase();
                if (RESUMES[aiResult]) category = aiResult;

                // --- 4. STRICT TRUTH GENERATION ---
                const emailResponse = await hf.chatCompletion({
                    model: "meta-llama/Llama-3.2-1B-Instruct",
                    messages: [
                        { 
                            role: "system", 
                            content: `You are Kanak Megha. STRICT KNOWLEDGE: ${experienceContext} and ${MY_SKILLS}. FORBIDDEN: R, Apache, Hadoop, SQL, Spark, "5 years". RULES: 2 paragraphs only. Start directly with the text. No headers/greetings.` 
                        },
                        { role: "user", content: `Write a cover letter body for ${role} at ${company}.` }
                    ],
                    max_tokens: 250
                });

                let aiBody = emailResponse.choices[0].message.content.trim();
                const forbiddenPrefixes = ["subject", "dear", "sincerely", "regards", "best", "thanks", "application"];
                aiBody = aiBody.split('\n')
                               .filter(line => !forbiddenPrefixes.some(pref => line.toLowerCase().startsWith(pref)))
                               .join('\n').trim();

                const finalEmail = `Dear Hiring Manager,\n\n${aiBody}\n\nI have included my resume link below for your review:\n${RESUMES[category]}\n\n${SOCIALS.trim()}`;

                // --- 5. SEND ---
                await transporter.sendMail({
                    from: process.env.EMAIL_USER,
                    to: email,
                    subject: `Application: ${role} at ${company} - Kanak Megha`,
                    text: finalEmail,
                });

                // --- 6. WRITE BACK TO SHEET1 (Syncing indices) ---
                // F is index 5 (status), G is index 6 (category)
                await sheets.spreadsheets.values.update({
                    spreadsheetId: process.env.SHEET_ID,
                    range: `Sheet1!F${rowIndex}:G${rowIndex}`, 
                    valueInputOption: 'USER_ENTERED',
                    resource: { values: [['Sent', category]] },
                });

                console.log(`✅ Success: Sent ${category} to ${company}`);

                // Throttle to avoid Quota Exceeded (6 seconds)
                await new Promise(r => setTimeout(r, 6000));

            } catch (innerError) {
                console.error(`⚠️ Failed row ${rowIndex}:`, innerError.message);
                if (innerError.message.includes("quota")) return;
            }
        }
        console.log("\n🏁 All rows processed!");
    } catch (error) {
        console.error("❌ CRITICAL ERROR:", error.message);
    }
}

runAgent();