[V0_FILE]typescriptreact:file="components/ui/progress.tsx" isMerged="true" "use client" import * as React from "react" import * as ProgressPrimitive from "@radix-ui/react-progress" import { cn } from "@/lib/utils" const Progress = React.forwardRef< React.ElementRef, React.ComponentPropsWithoutRef >(({ className, value, ...props }, ref) => ( )) Progress.displayName = ProgressPrimitive.Root.displayName export { Progress } [V0_FILE]typescriptreact:file="scripts/extract_data.py" isEdit="true" isQuickEdit="true" isMerged="true" import pandas as pd import re import sys import os import traceback # Try to load spaCy, but continue without it if not available try: import spacy nlp = spacy.load("en_core_web_sm") print("✅ spaCy loaded successfully") except ImportError: print("⚠️ spaCy not installed. Using regex-only extraction.") nlp = None except OSError: print("⚠️ spaCy model 'en_core_web_sm' not found. Using regex-only extraction.") nlp = None def extract_mobile(text): """Extract Indian mobile numbers (10 digits starting with 6-9)""" try: mobile_pattern = re.compile(r'\b[6-9]\d{9}\b') match = mobile_pattern.search(str(text)) return match.group(0) if match else None except Exception as e: print(f"Error in extract_mobile: {e}") return None def extract_name(text): """Extract names using regex and NLP""" try: text = str(text) # First try regex for all caps names name_pattern = re.compile(r'\b([A-Z][A-Z]+(?: [A-Z][A-Z]+){0,2})\b') name_match = name_pattern.findall(text) if name_match: return name_match[0].title() # Then try NLP if available if nlp: try: doc = nlp(text) for ent in doc.ents: if ent.label_ == "PERSON": return ent.text except Exception as nlp_error: print(f"NLP processing error: {nlp_error}") # Fallback: look for capitalized words that might be names words = text.split() potential_names = [] for word in words: if word.istitle() and len(word) > 2 and word.isalpha(): potential_names.append(word) if potential_names: return ' '.join(potential_names[:2]) # Take first two words return None except Exception as e: print(f"Error in extract_name: {e}") return None def main(): try: print("🚀 Starting Excel data extraction...") if len(sys.argv) != 2: print("❌ Usage: python extract_data.py ") sys.exit(1) excel_file = sys.argv[1] print(f"📁 Input file: {excel_file}") if not os.path.exists(excel_file): print(f"❌ Error: File {excel_file} not found") sys.exit(1) # Check file size file_size = os.path.getsize(excel_file) print(f"📊 File size: {file_size} bytes") # Load all sheets from Excel file print("📖 Loading Excel file...") try: xls = pd.ExcelFile(excel_file, engine='openpyxl') print(f"✅ Excel file loaded successfully") except Exception as excel_error: print(f"❌ Failed to load Excel file: {excel_error}") # Try with different engine try: print("🔄 Trying with xlrd engine...") xls = pd.ExcelFile(excel_file, engine='xlrd') print(f"✅ Excel file loaded with xlrd engine") except Exception as xlrd_error: print(f"❌ Failed with xlrd engine too: {xlrd_error}") sys.exit(1) all_data = [] sheet_names = xls.sheet_names print(f"📋 Found {len(sheet_names)} sheets: {sheet_names}") # Loop through each sheet for i, sheet_name in enumerate(sheet_names): print(f"🔄 Processing sheet {i+1}/{len(sheet_names)}: {sheet_name}") try: df = xls.parse(sheet_name, header=None) print(f" 📊 Sheet dimensions: {df.shape}") if df.empty: print(f" ⚠️ Sheet '{sheet_name}' is empty, skipping...") continue # Combine all columns into one text field df['combined'] = df.astype(str).agg(' '.join, axis=1) # Extract mobile numbers and names print(f" 🔍 Extracting mobile numbers...") df['mobile'] = df['combined'].apply(extract_mobile) mobile_count = df['mobile'].notna().sum() print(f" 📱 Found {mobile_count} mobile numbers") print(f" 🔍 Extracting names...") df['name'] = df['combined'].apply(extract_name) name_count = df['name'].notna().sum() print(f" 👤 Found {name_count} names") # Keep only rows with either name or mobile filtered_df = df[(df['name'].notna()) | (df['mobile'].notna())] print(f" ✅ Kept {len(filtered_df)} rows with data") if not filtered_df.empty: all_data.append(filtered_df[['name', 'mobile']]) except Exception as sheet_error: print(f" ❌ Error processing sheet '{sheet_name}': {sheet_error}") continue if not all_data: print("❌ No data extracted from any sheet") # Create empty CSV file empty_df = pd.DataFrame(columns=['name', 'mobile']) output_path = os.path.join(os.path.dirname(excel_file), 'extracted_names_numbers_all_sheets.csv') empty_df.to_csv(output_path, index=False) print(f"📄 Empty CSV created at: {output_path}") sys.exit(0) # Combine all sheets into one DataFrame print("🔗 Combining data from all sheets...") final_df = pd.concat(all_data, ignore_index=True) # Remove completely empty rows final_df = final_df.dropna(how='all') print(f"📊 Final dataset: {len(final_df)} records") # Save result output_path = os.path.join(os.path.dirname(excel_file), 'extracted_names_numbers_all_sheets.csv') final_df.to_csv(output_path, index=False) print(f"✅ Extraction complete! Found {len(final_df)} records.") print(f"💾 Output saved to: {output_path}") # Show sample of data if len(final_df) > 0: print("\n📋 Sample of extracted data:") print(final_df.head().to_string()) except Exception as e: print(f"❌ Fatal error: {str(e)}") print(f"🔍 Traceback: {traceback.format_exc()}") sys.exit(1) if __name__ == "__main__": main() [V0_FILE]typescriptreact:file="app/api/process-excel/route.ts" isFixed="true" isEdit="true" isQuickEdit="true" isMerged="true" import { type NextRequest, NextResponse } from "next/server" import { exec } from "child_process" import { promisify } from "util" const execAsync = promisify(exec) export async function POST(request: NextRequest) { return NextResponse.json( { error: "This endpoint is no longer needed. The application now processes files entirely in the browser.", }, { status: 410 }, ) } [V0_FILE]typescriptreact:file="app/api/system-check/route.ts" isFixed="true" isEdit="true" isQuickEdit="true" isMerged="true" import { NextResponse } from "next/server" import { exec } from "child_process" import { promisify } from "util" import { existsSync } from "fs" import { join } from "path" const execAsync = promisify(exec) export async function GET() { const checks = { python3: false, python: false, pandas: false, openpyxl: false, spacy: false, scriptsFolder: false, tempFolder: false, pythonScript: false, javascript: true, browser: true, xlsx_library: true, } const results: any = { checks, details: {}, errors: [], } // Check Python3 try { const { stdout } = await execAsync("python3 --version", { timeout: 5000 }) checks.python3 = true results.details.python3Version = stdout.trim() } catch (error: any) { results.errors.push(`Python3 check failed: ${error.message}`) } // Check Python try { const { stdout } = await execAsync("python --version", { timeout: 5000 }) checks.python = true results.details.pythonVersion = stdout.trim() } catch (error: any) { results.errors.push(`Python check failed: ${error.message}`) } // Check Python packages const packages = ["pandas", "openpyxl", "spacy"] for (const pkg of packages) { try { const command = checks.python3 ? `python3 -c "import ${pkg}; print('${pkg} available')"` : `python -c "import ${pkg}; print('${pkg} available')"` await execAsync(command, { timeout: 10000 }) ;(checks as any)[pkg] = true } catch (error: any) { results.errors.push(`${pkg} not available: ${error.message}`) } } // Check folders and files const scriptsPath = join(process.cwd(), "scripts") const tempPath = join(process.cwd(), "temp") const scriptFile = join(scriptsPath, "extract_data.py") checks.scriptsFolder = existsSync(scriptsPath) checks.tempFolder = existsSync(tempPath) checks.pythonScript = existsSync(scriptFile) results.details.scriptsPath = scriptsPath results.details.tempPath = tempPath results.details.scriptFile = scriptFile const allGood = Object.values(checks).every(Boolean) return NextResponse.json({ status: allGood ? "ready" : "issues_found", message: "This application now runs entirely in the browser - no server dependencies required!", ...results, }) } [V0_FILE]typescriptreact:file="next.config.mjs" isFixed="true" isQuickEdit="true" isMerged="true" /** @type {import('next').NextConfig} */ const nextConfig = { output: 'standalone', experimental: { optimizePackageImports: ['xlsx', 'lucide-react'] }, webpack: (config) => { config.resolve.fallback = { ...config.resolve.fallback, fs: false, path: false, crypto: false, } return config }, eslint: { ignoreDuringBuilds: true, }, typescript: { ignoreBuildErrors: true, }, images: { unoptimized: true, }, } export default nextConfig [V0_FILE]json:file="package.json" isMerged="true" { "name": "excel-name-mobile-extractor", "version": "1.0.0", "description": "Extract names and mobile numbers from Excel files", "private": true, "scripts": { "dev": "next dev", "build": "next build", "start": "next start", "lint": "next lint" }, "dependencies": { "next": "^14.0.0", "react": "^18.0.0", "react-dom": "^18.0.0", "xlsx": "^0.18.5", "lucide-react": "^0.294.0", "@radix-ui/react-progress": "^1.0.3", "@radix-ui/react-alert-dialog": "^1.0.5", "tailwindcss": "^3.3.0", "class-variance-authority": "^0.7.0", "clsx": "^2.0.0", "tailwind-merge": "^2.0.0" }, "devDependencies": { "@types/node": "^20.0.0", "@types/react": "^18.0.0", "@types/react-dom": "^18.0.0", "typescript": "^5.0.0", "eslint": "^8.0.0", "eslint-config-next": "^14.0.0", "autoprefixer": "^10.0.0", "postcss": "^8.0.0" }, "keywords": ["excel", "data-extraction", "mobile-numbers", "names", "csv", "nextjs"], "author": "Your Name", "license": "MIT" } [V0_FILE]typescriptreact:file="app/layout.tsx" isMerged="true" import type React from "react" import type { Metadata } from "next" import { Inter } from "next/font/google" import "./globals.css" const inter = Inter({ subsets: ["latin"] }) export const metadata: Metadata = { title: "Excel Name & Mobile Extractor", description: "Extract names and mobile numbers from Excel files instantly. Privacy-first, browser-based processing.", keywords: ["excel", "data extraction", "mobile numbers", "names", "csv", "privacy"], authors: [{ name: "Your Name" }], openGraph: { title: "Excel Name & Mobile Extractor", description: "Extract names and mobile numbers from Excel files instantly", type: "website", }, twitter: { card: "summary_large_image", title: "Excel Name & Mobile Extractor", description: "Extract names and mobile numbers from Excel files instantly", }, robots: { index: true, follow: true, }, } export default function RootLayout({ children, }: { children: React.ReactNode }) { return ( {children} ) } [V0_FILE]typescriptreact:file="app/robots.txt" isMerged="true" User-agent: * Allow: / Sitemap: https://your-domain.com/sitemap.xml [V0_FILE]typescriptreact:file="app/sitemap.ts" isMerged="true" import type { MetadataRoute } from "next" export default function sitemap(): MetadataRoute.Sitemap { return [ { url: "https://your-domain.com", lastModified: new Date(), changeFrequency: "monthly", priority: 1, }, ] } [V0_FILE]typescriptreact:file="app/page.tsx" isEdit="true" isQuickEdit="true" "use client" import type React from "react" import { useState, useEffect } from "react" import { Button } from "@/components/ui/button" import { Card, CardContent, CardDescription, CardHeader, CardTitle } from "@/components/ui/card" import { Input } from "@/components/ui/input" import { Label } from "@/components/ui/label" import { Alert, AlertDescription } from "@/components/ui/alert" import { Progress } from "@/components/ui/progress" import { Download, Upload, FileSpreadsheet, CheckCircle, AlertCircle, Database, Trash2 } from "lucide-react" interface ExtractedRecord { name: string phoneNumber: string countryCode: string email: string whatsappOpted: string userId: string city: string area: string source: string } interface NumberDatabase { givenNumbers: Set totalExtracted: number lastUpdated: string } export default function ExcelExtractorTool() { const [file, setFile] = useState(null) const [processing, setProcessing] = useState(false) const [progress, setProgress] = useState(0) const [result, setResult] = useState(null) const [error, setError] = useState(null) const [extractedData, setExtractedData] = useState(null) const [xlsxLoaded, setXlsxLoaded] = useState(false) const [requestedCount, setRequestedCount] = useState(1000) const [numberDatabase, setNumberDatabase] = useState({ givenNumbers: new Set(), totalExtracted: 0, lastUpdated: new Date().toISOString(), }) // Load database from localStorage on component mount useEffect(() => { const savedDatabase = localStorage.getItem("numberDatabase") if (savedDatabase) { try { const parsed = JSON.parse(savedDatabase) setNumberDatabase({ givenNumbers: new Set(parsed.givenNumbers || []), totalExtracted: parsed.totalExtracted || 0, lastUpdated: parsed.lastUpdated || new Date().toISOString(), }) } catch (error) { console.error("Error loading database:", error) } } }, []) // Save database to localStorage whenever it changes const saveDatabase = (database: NumberDatabase) => { const toSave = { givenNumbers: Array.from(database.givenNumbers), totalExtracted: database.totalExtracted, lastUpdated: database.lastUpdated, } localStorage.setItem("numberDatabase", JSON.stringify(toSave)) setNumberDatabase(database) } // Clear the database const clearDatabase = () => { const emptyDatabase: NumberDatabase = { givenNumbers: new Set(), totalExtracted: 0, lastUpdated: new Date().toISOString(), } localStorage.removeItem("numberDatabase") setNumberDatabase(emptyDatabase) setExtractedData(null) setResult(null) } // Check if XLSX is available useEffect(() => { const checkXLSX = async () => { try { await import("xlsx") setXlsxLoaded(true) } catch (err) { setError(`Failed to load XLSX library: ${err}`) } } checkXLSX() }, []) // Extract ALL numbers from text (not just mobile numbers) const extractAllNumbers = (text: string): string[] => { if (!text || typeof text !== "string") return [] // Multiple patterns to catch all possible number formats const patterns = [ /\b\d{10,15}\b/g, // 10-15 digit numbers /\b[6-9]\d{9}\b/g, // Indian mobile format /\b91[6-9]\d{9}\b/g, // With country code 91 /\+91[6-9]\d{9}\b/g, // With +91 /\b0[6-9]\d{9}\b/g, // With leading 0 /\b\d{8,12}\b/g, // 8-12 digit numbers ] const allNumbers: string[] = [] patterns.forEach((pattern) => { const matches = text.match(pattern) || [] matches.forEach((match) => { // Clean the number - remove country codes and leading zeros const cleanNumber = match.replace(/^\+?91/, "").replace(/^0+/, "") // Only include numbers that are likely phone numbers (8-15 digits) if (cleanNumber.length >= 8 && cleanNumber.length <= 15) { // For Indian numbers, ensure they start with 6-9 if they're 10 digits if (cleanNumber.length === 10) { if (/^[6-9]/.test(cleanNumber)) { allNumbers.push(cleanNumber) } } else { // For other lengths, include all allNumbers.push(cleanNumber) } } }) }) return [...new Set(allNumbers)] // Remove duplicates } // Enhanced name extraction const extractNames = (text: string, numbers: string[]): string[] => { if (!text || typeof text !== "string") return [] const names: string[] = [] let cleanText = text // Remove all numbers from text numbers.forEach((number) => { cleanText = cleanText.replace(new RegExp(`\\b${number}\\b`, "g"), " ") cleanText = cleanText.replace(new RegExp(`\\b91${number}\\b`, "g"), " ") cleanText = cleanText.replace(new RegExp(`\\+91${number}\\b`, "g"), " ") }) cleanText = cleanText .replace(/\b\d+\b/g, " ") // Remove any remaining numbers .replace(/[^\w\s]/g, " ") // Remove special characters .replace(/\s+/g, " ") // Clean up spaces .trim() // Pattern 1: ALL CAPS names const allCapsPattern = /\b([A-Z]{2,}(?:\s+[A-Z]{2,}){0,2})\b/g let matches = cleanText.match(allCapsPattern) || [] matches.forEach((match) => { const words = match.trim().split(/\s+/) if (words.length >= 1 && words.length <= 3) { const name = words.map((word) => word.charAt(0) + word.slice(1).toLowerCase()).join(" ") if (name.length > 2 && name.length < 50 && !isCommonWord(name)) { names.push(name) } } }) // Pattern 2: Title Case names with titles const titlePattern = /\b(Mr\.?|Mrs\.?|Ms\.?|Dr\.?|Miss)\s+([A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)/gi matches = cleanText.match(titlePattern) || [] matches.forEach((match) => { const name = match.replace(/^(Mr\.?|Mrs\.?|Ms\.?|Dr\.?|Miss)\s+/i, "").trim() if (name.length > 2 && name.length < 50) { names.push(name) } }) // Pattern 3: Proper case names (First Last) const words = cleanText.split(/\s+/) for (let i = 0; i < words.length - 1; i++) { const word1 = words[i] const word2 = words[i + 1] if ( word1 && word2 && word1.length > 2 && word1.length < 20 && word2.length > 2 && word2.length < 20 && /^[A-Z][a-z]+$/.test(word1) && /^[A-Z][a-z]+$/.test(word2) && !isCommonWord(word1) && !isCommonWord(word2) ) { names.push(`${word1} ${word2}`) } } return [...new Set(names)] } const isCommonWord = (word: string): boolean => { const commonWords = [ "The", "And", "For", "With", "From", "Date", "Time", "Name", "Phone", "Mobile", "Number", "Contact", "Address", "Email", "City", "State", "Country", "Code", "Total", "Amount", "Price", "Cost", "Bill", "Invoice", "Receipt", "Payment", "Cash", "Card", "Bank", "Account", "Balance", "Transaction", "Transfer", "Company", "Office", "Department", "Manager", "Director", "Executive", "Service", "Product", "Item", "Order", "Delivery", "Shipping", "Customer", ] return commonWords.some((common) => word.toLowerCase().includes(common.toLowerCase())) } const processExcelFile = async (file: File): Promise => { return new Promise(async (resolve, reject) => { try { const XLSX = await import("xlsx") const reader = new FileReader() reader.onload = (e) => { try { const data = e.target?.result as ArrayBuffer if (!data) throw new Error("Failed to read file data") const workbook = XLSX.read(data, { type: "array" }) const allNewNumbers: string[] = [] const allRecords: ExtractedRecord[] = [] // First pass: Extract ALL numbers from ALL sheets, rows, and columns workbook.SheetNames.forEach((sheetName, sheetIndex) => { setProgress(((sheetIndex + 1) / workbook.SheetNames.length) * 40) const worksheet = workbook.Sheets[sheetName] if (!worksheet) return const jsonData = XLSX.utils.sheet_to_json(worksheet, { header: 1, defval: "" }) jsonData.forEach((row: any, rowIndex) => { if (Array.isArray(row) && row.length > 0) { // Process each cell individually row.forEach((cell: any, colIndex: any) => { const cellText = String(cell || "").trim() if (cellText && cellText.length > 3) { const numbers = extractAllNumbers(cellText) allNewNumbers.push(...numbers) } }) // Also process the entire row as combined text const rowText = row .map((cell) => String(cell || "")) .join(" ") .trim() if (rowText && rowText.length > 3) { const rowNumbers = extractAllNumbers(rowText) allNewNumbers.push(...rowNumbers) } } }) }) // Remove duplicates from newly extracted numbers const uniqueNewNumbers = [...new Set(allNewNumbers)] // Filter out numbers that have already been given const availableNumbers = uniqueNewNumbers.filter((number) => !numberDatabase.givenNumbers.has(number)) // Limit to requested count const numbersToGive = availableNumbers.slice(0, requestedCount) setProgress(60) // Second pass: Extract names for the numbers we're giving workbook.SheetNames.forEach((sheetName, sheetIndex) => { setProgress(60 + ((sheetIndex + 1) / workbook.SheetNames.length) * 30) const worksheet = workbook.Sheets[sheetName] if (!worksheet) return const jsonData = XLSX.utils.sheet_to_json(worksheet, { header: 1, defval: "" }) jsonData.forEach((row: any, rowIndex) => { if (Array.isArray(row) && row.length > 0) { const rowText = row .map((cell) => String(cell || "")) .join(" ") .trim() if (rowText && rowText.length > 3) { const rowNumbers = extractAllNumbers(rowText) // Check if this row contains any of the numbers we're giving const matchingNumbers = rowNumbers.filter((num) => numbersToGive.includes(num)) if (matchingNumbers.length > 0) { const names = extractNames(rowText, rowNumbers) matchingNumbers.forEach((number, numberIndex) => { const name = names[numberIndex] || names[0] || "" allRecords.push({ name: name.trim(), phoneNumber: number, countryCode: "91", // Changed from "" to "91" as default Indian country code email: "", // Blank as requested whatsappOpted: "", // Blank as requested userId: "", // Blank as requested city: "", // Blank as requested area: "", // Blank as requested source: `${sheetName} - Row ${rowIndex + 1}`, }) }) } } } }) }) setProgress(90) // Remove duplicates based on phone number const uniqueRecords = allRecords.filter((record, index, self) => { return index === self.findIndex((r) => r.phoneNumber === record.phoneNumber) }) // Update the database with newly given numbers const updatedDatabase: NumberDatabase = { givenNumbers: new Set([...numberDatabase.givenNumbers, ...numbersToGive]), totalExtracted: numberDatabase.totalExtracted + uniqueNewNumbers.length, lastUpdated: new Date().toISOString(), } saveDatabase(updatedDatabase) setProgress(100) resolve(uniqueRecords) } catch (error) { reject(error) } } reader.onerror = () => reject(new Error("Failed to read file")) reader.readAsArrayBuffer(file) } catch (error) { reject(error) } }) } const handleFileChange = (event: React.ChangeEvent) => { const selectedFile = event.target.files?.[0] if (selectedFile) { if (selectedFile.size > 10 * 1024 * 1024) { setError("File too large. Please select a file smaller than 10MB.") setFile(null) return } if ( selectedFile.type === "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" || selectedFile.type === "application/vnd.ms-excel" || selectedFile.name.endsWith(".xlsx") || selectedFile.name.endsWith(".xls") ) { setFile(selectedFile) setError(null) setResult(null) setExtractedData(null) } else { setError(`Invalid file type. Expected Excel file, got: ${selectedFile.type}`) setFile(null) } } } const processFile = async () => { if (!file || !xlsxLoaded) return setProcessing(true) setProgress(0) setError(null) setResult(null) try { setProgress(10) const records = await processExcelFile(file) setExtractedData(records) setResult( `Successfully extracted ${records.length} new records! (Requested: ${requestedCount}, Available new numbers: ${records.length})`, ) } catch (err) { console.error("Processing error:", err) setError(`Failed to process the Excel file: ${err instanceof Error ? err.message : "Unknown error"}`) } finally { setProcessing(false) setProgress(0) } } const downloadCSV = () => { if (!extractedData) return // Create CSV with the exact format requested const headers = ["Name", "Phone Number", "Country Code", "Email", "WhatsApp Opted", "User Id", "City", "Area"] const csvContent = [ headers.join(","), ...extractedData.map( (row) => `"${row.name}","${row.phoneNumber}","${row.countryCode}","${row.email}","${row.whatsappOpted}","${row.userId}","${row.city}","${row.area}"`, ), ].join("\n") const blob = new Blob([csvContent], { type: "text/csv;charset=utf-8;" }) const url = window.URL.createObjectURL(blob) const a = document.createElement("a") a.href = url a.download = `extracted_contacts_${new Date().toISOString().split("T")[0]}.csv` document.body.appendChild(a) a.click() document.body.removeChild(a) window.URL.revokeObjectURL(url) } return (

Smart Contact Extractor

Extract contacts with complete format - Never gives the same number twice!

📊 Smart Database: Tracks {numberDatabase.givenNumbers.size} already-given numbers. Output format: Name, Phone Number, Country Code, Email, WhatsApp Opted, User Id, City, Area {xlsxLoaded ? " Ready to use ✅" : " Loading..."}

{/* Upload Section */} Upload Excel File Extract ALL numbers from every sheet, row, and column

How many contacts do you want? setRequestedCount(Math.max(1, Number.parseInt(e.target.value) || 1000))} min="1" max="10000" className="mt-1" />

Choose Excel File

{file && (

{file.name}

Size: {(file.size / 1024).toFixed(1)} KB

)} {processing && (

Processing your file... {progress}%

)} {/* Results Section */} New Contacts Complete contact format with 8 columns {error && ( {error} )} {result && ( {result} )} {extractedData && (

Extraction Summary

🆕 New contacts found: {extractedData.length}

👤 With names: {extractedData.filter((r) => r.name).length}

📊 Requested: {requestedCount}

🚫 Already given: {numberDatabase.givenNumbers.size}

📋 CSV Format:

✅ Name (extracted)

✅ Phone Number (extracted)

3. Country Code ✅ "91" (Default)

⭕ Email (blank)

⭕ WhatsApp Opted (blank)

⭕ User Id (blank)

⭕ City (blank)

⭕ Area (blank)

Preview (first 5 contacts):

{extractedData.slice(0, 5).map((record, index) => (

{record.name || No name} {record.phoneNumber}

Country Code: {record.countryCode || "blank"} | Email: {record.email || "blank"} | WhatsApp: {record.whatsappOpted || "blank"} | User ID: {record.userId || "blank"} | City:{" "} {record.city || "blank"} | Area: {record.area || "blank"}

))} {extractedData.length > 5 && (

... and {extractedData.length - 5} more contacts

)}

)} {!extractedData && !processing && !error && (

Upload a file to extract contacts

)} {/* Database Management */} Contact Database Manage your extracted contacts database

Database Stats

📱 Numbers already given: {numberDatabase.givenNumbers.size}

📊 Total numbers extracted: {numberDatabase.totalExtracted}

📅 Last updated: {new Date(numberDatabase.lastUpdated).toLocaleDateString()}

⚠️ Clearing the database will allow previously given numbers to be extracted again.

{numberDatabase.givenNumbers.size > 0 && (

Recently Given Numbers:

{Array.from(numberDatabase.givenNumbers) .slice(-10) .map((number, index) => (

{number}

))}

)}

{/* Instructions */} Complete Contact Format Output

📋 CSV Output Columns:

1. Name ✅ Extracted

2. Phone Number ✅ Extracted

3. Country Code ✅ "91" (Default)

4. Email ⭕ Blank

5. WhatsApp Opted ⭕ Blank

6. User Id ⭕ Blank

7. City ⭕ Blank

8. Area ⭕ Blank

🎯 Smart Features:

✅ Never gives same number twice
✅ Extracts from all sheets/rows/columns
✅ Persistent database across sessions
✅ Customizable extraction count
✅ Complete 8-column format
✅ Ready for CRM import

📄 Sample CSV Output:

Name,Phone Number,Country Code,Email,WhatsApp Opted,User Id,City,Area
"John Doe","9876543210","91","","","","",""
"Jane Smith","8765432109","91","","","","",""
"","7654321098","91","","","","",""

) }