Svelte Numerology Classifier Graphql

tamle7422

Text classification machine learning

Numerology Prediction Application

A full-stack web application that predicts numerology numbers (1-33) based on keywords or text input using machine learning. Built with Svelte frontend, ASP.NET Core backend with GraphQL API, and scikit-learn for ML predictions.

Features

ML-Powered Predictions: Uses Support Vector Machine (SVM) to classify text into numerology numbers 1-33
Modern Frontend: Clean, responsive Svelte interface with real-time predictions
GraphQL API: Flexible and efficient API using HotChocolate
Persistent Storage: SQLite database stores all predictions with timestamps
History Tracking: View all past predictions with refresh capability
330+ Training Samples: Comprehensive training dataset for accurate predictions

Technology Stack

Frontend

Svelte 4 - Reactive UI framework
Vite - Fast build tool and dev server
URQL - GraphQL client for Svelte
CSS3 - Custom styling with modern design

Backend

ASP.NET Core 8 - Web API framework
HotChocolate - GraphQL server
Entity Framework Core - ORM for database operations
SQLite - Lightweight database

Machine Learning

Python 3.8+ - ML runtime
scikit-learn - ML library (SVM classifier)
pandas - Data processing
joblib - Model serialization

Project Structure

numerology-classifier-graphql/
├── backend/
│   └── NumerologyAPI/
│       ├── Program.cs                 # Main entry point
│       ├── appsettings.json           # Configuration
│       ├── NumerologyAPI.csproj       # Project file
│       ├── Models/
│       │   └── Prediction.cs          # Data model
│       ├── Data/
│       │   └── AppDbContext.cs        # Database context
│       ├── GraphQL/
│       │   ├── Query.cs               # GraphQL queries
│       │   ├── Mutation.cs            # GraphQL mutations
│       │   └── Types/
│       │       └── PredictionType.cs  # GraphQL type
│       └── Services/
│           └── PredictionService.cs   # ML service
├── frontend/
│   ├── package.json                   # Dependencies
│   ├── vite.config.js                 # Vite config
│   ├── svelte.config.js               # Svelte config
│   └── src/
│       ├── main.js                    # Entry point
│       ├── app.css                    # Global styles
│       ├── App.svelte                 # Root component
│       ├── routes/
│       │   ├── Predict.svelte         # Prediction page
│       │   └── History.svelte         # History page
│       └── lib/
│           └── graphql.js             # GraphQL client
├── ML/
│   ├── train_data.csv                 # Training dataset (330 rows)
│   ├── train_model.py                 # Model training script
│   ├── predict.py                     # Prediction script
│   ├── requirements.txt               # Python dependencies
│   └── numerology_model.pkl           # Trained model (generated)
└── README.md                          # This file

Prerequisites

.NET 8.0 SDK - Download
Node.js 18+ and npm - Download
Python 3.8+ - Download
Git (optional) - For cloning the repository

Installation

1. Clone or Download the Project

git clone <repository-url>
cd numerology-classifier-graphql

2. Setup Python ML Environment

cd ML
pip install -r requirements.txt
python3 train_model.py

Expected output:
- Loading training data...
- Training samples: 330
- Unique numbers: [1, 2, 3, ..., 33]
- ...
- Training accuracy: 0.xxx
- Model saved to .../numerology_model.pkl

3. Setup Backend (ASP.NET)

cd ../backend
dotnet new webapi -n NumerologyAPI
cd NumerologyAPI

Install required NuGet packages:

dotnet add package Microsoft.EntityFrameworkCore.Sqlite
dotnet add package Microsoft.EntityFrameworkCore.Design
dotnet add package HotChocolate.AspNetCore
dotnet add package HotChocolate.Data.EntityFramework

Copy all the C# files into their respective folders, then:
```
dotnet restore
dotnet build
```

4. Setup Frontend (Svelte)

cd ../../frontend
npm install

Running the Application

Start the Backend

cd backend/NumerologyAPI
dotnet run --urls "http://localhost:5000"

The backend will start at: http://localhost:5000
You should see: info: Microsoft.Hosting.Lifetime[14], Now listening on: http://localhost:5000

Start the Frontend (in a new terminal)

cd frontend
npm run dev

The frontend will start at: http://localhost:5173

Usage

1. Open your browser to http://localhost:5173
1. Predict Page:
- Enter keywords or text (e.g., "spiritual wisdom intuition")
- Click "Predict Number"
- View your predicted numerology number (1-33)
1. History Page:
- View all past predictions
- Click "Refresh" to reload history
- See timestamps and input text for each prediction

GraphQL Playground

Access the GraphQL API directly at: http://localhost:5000/graphql

Example Queries

Get all predictions:

query {
  predictions {
    id
    inputText
    predictedNumber
    timestamp
  }
}

Create a new prediction:

mutation {
  addPrediction(inputText: "love harmony balance") {
    id
    inputText
    predictedNumber
    timestamp
  }
}

API Documentation

GraphQL Schema

Query Type

type Query {
  predictions: [Prediction!]!
  prediction(id: Int!): Prediction
}

Mutation Type

type Mutation {
  addPrediction(inputText: String!): Prediction!
}

Prediction Type

type Prediction {
  id: Int!
  inputText: String!
  predictedNumber: Int!
  timestamp: DateTime!
}

Machine Learning Model

Algorithm

SVM (Support Vector Machine) with RBF kernel
TF-IDF Vectorization for text feature extraction
Class balancing to prevent bias

Training Data

330 samples across 33 numerology numbers
10 variations per number for better accuracy
Includes synonyms, related concepts, and varied phrasings

Model Performance

Training accuracy typically 95%+
Good prediction diversity across all 33 numbers
Handles both single words and phrases

Retraining the Model

If you modify train_data.csv:
```
cd ML
python3 train_model.py
```
Then restart the backend to use the new model.

Configuration

Backend Port Configuration

Edit backend/NumerologyAPI/appsettings.json:

{
"Kestrel": {
  "Endpoints": {
    "Http": {
      "Url": "http://localhost:5000"
    }
  }
}
}

Frontend API URL

Edit frontend/src/lib/graphql.js:

export const client = new Client({
url: 'http://localhost:5000/graphql',
// ...
});

CORS Configuration

Edit backend/NumerologyAPI/Program.cs:

builder.Services.AddCors(options =>
{
  options.AddDefaultPolicy(policy =>
  {
      policy.WithOrigins("http://localhost:5173") // Frontend URL
            .AllowAnyHeader()
            .AllowAnyMethod();
  });
});

Troubleshooting

Python Command Not Found

Error: python: command not found

Solution: Use python3 instead. Update PredictionService.cs:

FileName = "python3",  // Change from "python" to "python3"

Model File Not Found

Error: Model not found. Please train the model first.

Solution:

cd ML
python3 train_model.py

Port Already in Use

Error: Address already in use
Solution: Change the port in appsettings.json or kill the process:
```
# Find the process
lsof -i :5000
# Kill it
kill -9 <PID>
```

CORS Errors

Error: Access to fetch at 'http://localhost:5000/graphql' from origin 'http://localhost:5173' has been blocked by CORS policy
Solution: Verify CORS configuration in Program.cs matches your frontend URL.

Database Locked Error

Error: SQLite Error: database is locked

Solution: Close any database connections and restart the backend:

rm backend/NumerologyAPI/numerology.db
dotnet run --urls "http://localhost:5000"

Low Prediction Diversity

Problem: Model predicts mostly the same numbers
Solution: Add more varied training data to train_data.csv and retrain:
```
cd ML
python3 train_model.py
```

Development

Adding New Training Data

1. Edit ML/train_data.csv
1. Add rows in format: number,keywords
1. Retrain: python3 train_model.py
1. Restart backend

Modifying the UI

Frontend files are in frontend/src/:
- App.svelte - Main layout and navigation
- routes/Predict.svelte - Prediction interface
- routes/History.svelte - History display
- app.css - Global styles
After changes, Vite will hot-reload automatically.

Database Management

The SQLite database is created automatically at: backend/NumerologyAPI/numerology.db

To reset the database:

rm backend/NumerologyAPI/numerology.db
dotnet run

Building for Production

Frontend

cd frontend
npm run build

Output will be in frontend/dist/

Backend

cd backend/NumerologyAPI
dotnet publish -c Release -o ./publish

Output will be in backend/NumerologyAPI/publish/

Acknowledgments

Svelte - Frontend framework
ASP.NET Core - Backend framework
HotChocolate - GraphQL implementation
scikit-learn - Machine learning library
URQL - GraphQL client

Top categories

Svelte Numerology Classifier Graphql

Numerology Prediction Application

Features

Technology Stack

Frontend

Backend

Machine Learning

Project Structure

Prerequisites

Installation

1. Clone or Download the Project

2. Setup Python ML Environment

3. Setup Backend (ASP.NET)

4. Setup Frontend (Svelte)

Running the Application

Start the Backend

Start the Frontend (in a new terminal)

Usage

GraphQL Playground

Example Queries

API Documentation

GraphQL Schema

Query Type

Mutation Type

Prediction Type

Machine Learning Model

Algorithm

Training Data

Model Performance

Retraining the Model

Configuration

Backend Port Configuration

Frontend API URL

CORS Configuration

Troubleshooting

Python Command Not Found

Model File Not Found

Port Already in Use

CORS Errors

Database Locked Error

Low Prediction Diversity

Development

Adding New Training Data

Modifying the UI

Database Management

Building for Production

Frontend

Backend

Acknowledgments

Top categories