Language Prediction Web App

Overview

The Language Prediction Web App is a tool for predicting the language of a given text. Built using FastAPI for the backend and HTML, CSS, and JavaScript for the frontend, this application provides a user-friendly interface for language detection. It handles various inputs and displays appropriate messages for both prediction and errors.

Features

• Language Prediction: Predict the language of a given text using an ML model.
• Responsive Design: Optimized for both desktop and mobile devices.
• Error Handling: Displays a user-friendly message when no text is entered.
• Loading Indicator: Shows a loading message while processing the prediction.

Project Structure

The project is organized as follows:

Language-predictor/
├── app
│   ├── main.py
│   ├── model
│   │   ├── model.py
│   │   └── trained_pipeline-0.1.0.pkl
│   ├── requirements.txt
│   ├── static
│   │   ├── css
│   │   │   └── styles.css
│   │   └── js
│   │       └── script.js
│   └── templates
│       └── index.html
└── README.md

Getting Started

Prerequisites

Ensure you have the following installed:

• Python 3.7+: Required for running the FastAPI application.
• pip: Python package installer.

Installation

1. Clone the Repository

   git clone https://github.com/ganeshborkar31/Language-predictor.git
   cd Language-predictor
   

2. Create and Activate a Virtual Environment

   python -m venv myvenv
   source myvenv/bin/activate  # On Windows, use `myvenv\Scripts\activate`
   

3. Install the required packages:

   pip install -r requirements.txt
   

4. Ensure requirements.txt includes:

   fastapi
   uvicorn
   pydantic
   scikit-learn
   
   
   
   

5. Run the FastAPI application:

   cd app
   python -m uvicorn app.main:app --reload
   

The application will be available at http://127.0.0.1:8000.

Usage

1. Open your browser and navigate to http://127.0.0.1:8000.
2. Enter the text you want to analyze in the text box.
3. Click the "Predict" button.
4. The predicted language or an error message will be displayed below the button.

API Endpoints

POST /predict/

• Request Body:

  {
  "text": "input text"
  }
  

• Response:

  • On success:

    {
    "language": "Predicted Language"
    }

  • On error (empty input):

    {
    "error": "Please enter some text."
    }

Troubleshooting

1. Error: TypeError: expected string or bytes-like object, got 'TextInput'

   • Solution: Ensure the input is a string and properly extracted in the FastAPI endpoint.

2. To ensure that your Python server remains running after you disconnect from an SSH session, you can use several methods. Given your command python -m uvicorn main:app --host 0.0.0.0 --port 8000, here are some effective ways to keep it running:

• Using nohup

  1. The nohup (no hang-up) command allows processes to continue running in the background even after you log out from the SSH session.

     • Run the Command with nohup:

       nohup python -m uvicorn main:app --host 0.0.0.0 --port 8000 > server.log 2>&1 &

  2. nohup: Prevents the process from being stopped when you log out.

     server.log 2>&1: Redirects both stdout and stderr to server.log. &: Runs the command in the background.

     • Verify the Process:

       • You can check if the server is running by looking for the process:

       ps aux | grep uvicorn

  3. Stop the Process (if needed):

     • To stop the process, you’ll need to find its process ID (PID) and then kill it:

       kill <PID>
     

Contact

• ganeshborkar107@gmail.com

Summary of the README.md Sections

1. Overview: Brief introduction to what the project does.
2. Project Structure: Overview of the project directory and files.
3. Getting Started: Instructions to set up and run the project locally.
4. Usage: How to use the web application.
5. API Endpoints: Details of the API endpoint used by the frontend.
6. Troubleshooting: Common issues and their solutions.
7. Contact: Contact information for feedback or questions.

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