python

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Load data from database
data = pd.read_csv('user_data.csv')  # Assume you've exported the data to a CSV

# Data preprocessing
# Example: Assuming we are predicting user engagement based on user data
features = data.drop('engagement', axis=1)
target = data['engagement']

# Standardizing the features
scaler = StandardScaler()
features_scaled = scaler.fit_transform(features)

# Splitting the data
X_train, X_test, y_train, y_test = train_test_split(features_scaled, target, test_size=0.2, random_state=42)
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Define the model
model = Sequential([
    Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
    Dense(64, activation='relu'),
    Dense(1, activation='sigmoid')  # Assuming binary classification
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train the model
history = model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2)

# Evaluate the model
model.evaluate(X_test, y_test)

# Save the model
model.save('user_engagement_model.h5')
from fastapi import FastAPI, Request
import tensorflow as tf
import numpy as np
from pydantic import BaseModel

# Load the model
model = tf.keras.models.load_model('user_engagement_model.h5')

# Initialize FastAPI app
app = FastAPI()

class UserData(BaseModel):
    feature1: float
    feature2: float
    # Add other features as per your dataset

@app.post("/predict")
async def predict(data: UserData):
    input_data = np.array([[data.feature1, data.feature2, ...]])  # Add all features
    prediction = model.predict(input_data)
    return {"prediction": prediction[0][0]}

# Run the API with `uvicorn app:app --reload`
import { useState } from 'react';
import axios from 'axios';

const Search = () => {
  const [query, setQuery] = useState('');
  const [results, setResults] = useState([]);

  const handleSearch = async () => {
    const response = await axios.post('http://localhost:8000/predict', { 
      feature1: value1,
      feature2: value2,
      // Add other features
    });
    setResults(response.data.prediction);
  };

  return (
    <div>
      <input
        type="text"
        value={query}
        onChange={(e) => setQuery(e.target.value)}
        placeholder="Search"
      />
      <button onClick={handleSearch}>Search</button>
      <div>
        {results && <p>Prediction: {results}</p>}
      </div>
    </div>
  );
};

export default Search;

in pages/visualization.js
import { useEffect, useState } from 'react';
import { Bar } from 'react-chartjs-2';

const Visualization = () => {
  const [data, setData] = useState({});

  useEffect(() => {
    const fetchData = async () => {
      const response = await axios.get('http://localhost:8000/data');
      setData(response.data);
    };
    fetchData();
  }, []);

  const chartData = {
    labels: data.labels,
    datasets: [
      {
        label: 'User Engagement',
        data: data.values,
        backgroundColor: 'rgba(75, 192, 192, 0.6)',
      },
    ],
  };

  return (
    <div>
      <Bar data={chartData} />
    </div>
  );
};

export default Visualization;