🚀 Spam Detection System

A full-stack application that detects Spam / Smishing / Offensive content using Machine Learning. The system includes:

• 🧠 ML Model (Python)
• ⚡ Python API (Flask / FastAPI)
• 🌐 Node.js Backend
• 💻 React Web App
• 📱 React Native Mobile App (Android & iOS)

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📌 Project Architecture

User Input (Web / Mobile)
        ↓
React / React Native UI
        ↓
Node.js Backend (API Gateway)
        ↓
Python ML API (Model Inference)
        ↓
Prediction (Spam / Ham / Offensive)

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🧠 Machine Learning Model

📊 Dataset

• CSV format:

  • text / message
  • label (spam / ham / offensive)

⚙️ Algorithms Used

• Logistic Regression
• Naive Bayes
• Linear SVM (Best Accuracy)

📈 Performance

• Accuracy: ~97–98%

• Metrics:

  • Precision
  • Recall
  • F1-score
  • Confusion Matrix

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🏋️ Model Training (Python)

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
import pickle

# Load dataset
X = df['text']
y = df['label']

# Vectorization
vectorizer = TfidfVectorizer()
X_vec = vectorizer.fit_transform(X)

# Model
model = LinearSVC()
model.fit(X_vec, y)

# Save
pickle.dump(model, open("model.pkl", "wb"))
pickle.dump(vectorizer, open("vectorizer.pkl", "wb"))

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🐍 Python API (Flask)

📦 Install Dependencies

pip install flask scikit-learn

🚀 API Code

from flask import Flask, request, jsonify
import pickle

app = Flask(__name__)

model = pickle.load(open("model.pkl", "rb"))
vectorizer = pickle.load(open("vectorizer.pkl", "rb"))

@app.route('/predict', methods=['POST'])
def predict():
    data = request.json['text']
    vec = vectorizer.transform([data])
    prediction = model.predict(vec)[0]
    return jsonify({"result": prediction})

if __name__ == "__main__":
    app.run(port=5000)

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🌐 Node.js Backend

📦 Install

npm install express axios cors

⚙️ Server Code

const express = require("express");
const axios = require("axios");
const cors = require("cors");

const app = express();
app.use(cors());
app.use(express.json());

app.post("/predict", async (req, res) => {
  try {
    const response = await axios.post("http://localhost:5000/predict", {
      text: req.body.text,
    });
    res.json(response.data);
  } catch (err) {
    res.status(500).send("Error");
  }
});

app.listen(3000, () => console.log("Node server running"));

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💻 React Frontend

📦 Setup

npm create vite@latest
npm install axios

⚛️ Example Component

import { useState } from "react";
import axios from "axios";

function App() {
  const [text, setText] = useState("");
  const [result, setResult] = useState("");

  const handlePredict = async () => {
    const res = await axios.post("http://localhost:3000/predict", { text });
    setResult(res.data.result);
  };

  return (
    <div>
      <h1>Spam Detection</h1>
      <input onChange={(e) => setText(e.target.value)} />
      <button onClick={handlePredict}>Check</button>
      <p>{result}</p>
    </div>
  );
}

export default App;

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📱 React Native App (Android & iOS)

📦 Setup

npx create-expo-app
npm install axios

📲 Example Code

import { useState } from "react";
import { View, Text, TextInput, Button } from "react-native";
import axios from "axios";

export default function App() {
  const [text, setText] = useState("");
  const [result, setResult] = useState("");

  const predict = async () => {
    const res = await axios.post("http://YOUR_IP:3000/predict", { text });
    setResult(res.data.result);
  };

  return (
    <View>
      <Text>Spam Detection</Text>
      <TextInput onChangeText={setText} />
      <Button title="Check" onPress={predict} />
      <Text>{result}</Text>
    </View>
  );
}

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🔐 Features

• ✅ Spam / Smishing Detection
• ✅ Offensive Content Classification
• ✅ Real-time Prediction API
• ✅ Cross-platform (Web + Mobile)
• ✅ Scalable Architecture

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🛠 Tech Stack

• Python (ML + API)
• Scikit-learn
• Flask
• Node.js
• Express
• React
• React Native
• Axios

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📌 Future Improvements

• Use Deep Learning (LSTM / BERT / CLIP)
• Multilingual Support
• More accuracy and advanced model
• Include Email predicton perfectly and add mobile numbers also to track

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👨‍💻 Author

Aditya Sharma

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⭐ Contribute

Feel free to fork, improve and contribute to this project!

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