REFilterLeads

This project provides two different implementations for lead verification:

Directory Structure

REFilterLeads/
├── free_api/              # Free API Implementation
│   ├── free_lead_verification.py
│   ├── test_numverify.py
│   ├── test_free_verification.py
│   ├── sample_leads.csv
│   └── sample.env
│
└── forewarn/             # Forewarn API Implementation
    ├── lead_verification.py
    ├── lead_utils.py
    ├── mock_forewarn_api.py
    ├── test_lead_verification.py
    └── Project Guide for Forewarn API Integration.markdown

Free API Implementation

Located in the free_api directory, this implementation uses free APIs for lead verification:

• Numverify for phone verification
• NeverBounce for email verification
• Searchbug for background checks

Setup

1. Copy sample.env to .env and add your API keys
2. Install dependencies: pip install -r requirements.txt
3. Run tests:
   • python test_numverify.py for API testing
   • python test_free_verification.py for unit tests

Forewarn Implementation

Located in the forewarn directory, this implementation uses the Forewarn API for comprehensive lead verification.

Setup

1. Follow the instructions in the Project Guide
2. Install dependencies: pip install -r requirements.txt
3. Run tests: python test_lead_verification.py

Requirements

Both implementations require:

• Python 3.6+
• requests
• python-dotenv
• pytest (for testing)

Install all requirements:

pip install -r requirements.txt

REFilterLeads - Real Estate Lead Verification System

A system for verifying real estate leads using free APIs (Numverify, NeverBounce, and Searchbug) to validate contact information and perform basic background checks.

Setup

1. Install dependencies:

   pip install -r requirements.txt
   

2. Create a .env file in the project root with the following content:

   # API Keys for Lead Verification
   NUMVERIFY_API_KEY=your_numverify_api_key_here
   NEVERBOUNCE_API_KEY=your_neverbounce_api_key_here
   SEARCHBUG_API_KEY=your_searchbug_api_key_here
   
   # Optional: Set to true to use mock API for testing
   USE_MOCK_API=false
   
   # Optional: Set the output directory for JSON files
   OUTPUT_DIR=results
   
   # Optional: Set the log level (DEBUG, INFO, WARNING, ERROR)
   LOG_LEVEL=INFO
   

3. Get your API keys:

   • Numverify: https://numverify.com/
   • NeverBounce: https://www.neverbounce.com/
   • Searchbug: https://www.searchbug.com/

Usage

Verifying Leads

Run the lead verification script with sample leads:

from free_lead_verification import process_new_leads, save_leads_to_json

# Your leads as (name, phone, email) tuples
leads = [
    ("John Doe", "1234567890", "john.doe@example.com"),
    ("Jane Smith", "9876543210", "jane.smith@example.com")
]

# Process leads
verified, flagged = process_new_leads(leads)

# Save results
save_leads_to_json(verified, flagged)

Running Tests

Run the test suite:

python -m pytest test_free_verification.py -v

Features

• Phone number validation using Numverify
• Email verification using NeverBounce
• Basic background checks using Searchbug
• Risk assessment for each lead
• JSON export of verified and flagged leads
• Comprehensive test coverage

Output Format

The system exports verified and flagged leads to separate JSON files with this structure:

[
  {
    "name": "John Doe",
    "phone": "123-456-7890",
    "email": "john.doe@example.com",
    "verification_details": {
      "phone_verification": { ... },
      "email_verification": { ... },
      "background_check": { ... },
      "verification_status": {
        "overall_status": "verified",
        "risk_factors": []
      }
    }
  }
]

Notes

• Ensure compliance with data privacy regulations (e.g., GDPR, CCPA) when handling personal data
• Monitor API usage to manage costs, especially for APIs with per-call pricing
• Consider implementing rate limiting for API calls
• Keep your API keys secure and never commit them to version control

Lead Verification System

A web-based lead verification system with a TypeScript frontend and Python backend.

Setup

1. Install Python dependencies:

pip install -r requirements.txt

2. Install Node.js dependencies:

npm install

3. Build the TypeScript frontend:

npm run build

4. Run the Flask application:

python app.py

The application will be available at http://localhost:5000.

Development

• Frontend TypeScript files are in the src directory
• Compiled JavaScript files are output to dist directory
• Static files (HTML, JS) are served from the static directory
• Python backend code is in the root directory

To watch for TypeScript changes during development:

npm run watch

Project Structure

.
├── src/                    # TypeScript source files
│   ├── main.ts            # Main application logic
│   ├── types.ts           # TypeScript type definitions
│   └── index.html         # Main HTML template
├── static/                # Static files served by Flask
│   ├── js/               # Compiled JavaScript
│   └── index.html        # Copied from src/
├── dist/                  # TypeScript compilation output
├── app.py                # Flask application
├── requirements.txt      # Python dependencies
├── package.json         # Node.js dependencies
└── tsconfig.json        # TypeScript configuration