📱 NLP-Based WhatsApp Automation Assistant (Python)

---

An NLP-based WhatsApp automation project built using Python for learning and demonstration purposes. This project combines automation, rule-based natural language processing (NLP), and emotion-aware response generation.

⚠️ Important: This is NOT an official WhatsApp bot and does NOT use WhatsApp’s official APIs.

---

🚀 Overview

The goal of this project is to demonstrate how:

• Natural language input can be analyzed
• Emotions can be inferred from text
• Automated yet context-aware replies can be generated
• Messaging workflows can be automated responsibly for learning purposes

This project is not production-ready and is intended only for educational use.

---

✨ Features

✅ 1. NLP-Based Auto Reply Assistant (Clipboard-Based)

• Copy any incoming message text (e.g., from WhatsApp)

• The system:

  • Analyzes the text
  • Detects the emotion using rule-based NLP
  • Generates a contextual reply

• The reply is automatically copied back to your clipboard

• Paste the reply manually wherever required

📌 This assistant does not read WhatsApp messages directly.

---

✅ 2. WhatsApp Bulk Messaging Automation

• Sends the same message to multiple contacts
• Reads phone numbers from an Excel file (.xlsx)
• Uses WhatsApp Web automation
• Introduces random delays between messages to reduce spam-like behavior

📌 Intended strictly for testing and learning purposes.

---

🧠 NLP & Emotion Detection Logic

This project uses rule-based NLP, not machine learning.

Emotion Detection:

• Keyword matching approach

• Supported emotions:

  • Happy
  • Sad
  • Stressed
  • Excited
  • Neutral (default)

If no emotion-related keywords are found, the message is marked as Neutral.

This design keeps the project:

• Lightweight
• Easy to understand
• Beginner-friendly

---

📁 Project Structure

whatsapp_automation/
│
├── main.py                 
├── assistant.py            
├── bulk_message.py         
├── emotion_detection.py    
├── reply.py                
├── context_analyzer.py     
├── requirements.txt
├── README.md
│
└── memory/
    └── inbox.json          

---

🛠 Requirements

• Python 3.x
• Google Chrome
• WhatsApp Web account

Python Libraries

pip install pyperclip pywhatkit openpyxl

or

pip install -r requirements.txt

📌 Make sure you are logged into WhatsApp Web at least once before running bulk messaging.

---

▶️ How to Run

python main.py

You will see the following options:

1. WhatsApp Bulk Messaging
2. NLP Auto Reply Assistant
3. Run Both

---

📝 How to Use the Auto Reply Assistant

1. Choose option 2

2. Copy any message text

3. The system will:

   • Detect emotion
   • Generate a suggested reply
   • Copy it to the clipboard

4. Paste the reply manually

Stop anytime using Ctrl + C

---

📤 How to Use Bulk Messaging

1. Create an Excel file (.xlsx)

2. Add phone numbers in Column A (without country code)

3. Choose option 1

4. Enter:

   • Excel file path
   • Message text

5. WhatsApp Web opens and sends messages automatically

---

⚠️ Warnings & Disclaimer

🚫 WhatsApp Policy Warning

• Uses browser automation (not official APIs)
• May violate WhatsApp Terms of Service
• Can result in temporary or permanent bans

⚠️ Use a test WhatsApp account only

---

🚫 Limitations

• Not fully automated
• Clipboard-based reply system
• Simple keyword-based NLP
• No ML or deep learning models
• Minimal error handling
• Not production-ready

---

🎓 Purpose of This Project

This project is intended for:

• Learning Python automation
• Understanding basic NLP concepts
• Exploring chatbot logic
• College / academic submissions
• GitHub portfolio demonstrations

---

🚀 Possible Improvements

• ML-based or transformer-based emotion detection
• Context-aware multi-turn replies
• GUI or web interface
• Better memory handling
• Official messaging APIs
• Improved NLP preprocessing

---

📜 Disclaimer

This project is created strictly for educational purposes. The author is not responsible for misuse, WhatsApp bans, or policy violations.

---

⭐ If you find this project useful, feel free to star the repository and build on top of it!