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https://github.com/ganeshborkar31/language-predictor
Language Detector web app using Fastapi and vectorization
https://github.com/ganeshborkar31/language-predictor
aws aws-ec2 css deployment fastapi html javascript language language-prediction machine-learning mlops python python3
Last synced: 4 days ago
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Language Detector web app using Fastapi and vectorization
- Host: GitHub
- URL: https://github.com/ganeshborkar31/language-predictor
- Owner: ganeshborkar31
- Created: 2024-08-21T06:41:34.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-08-21T11:11:58.000Z (6 months ago)
- Last Synced: 2024-08-22T08:09:55.707Z (6 months ago)
- Topics: aws, aws-ec2, css, deployment, fastapi, html, javascript, language, language-prediction, machine-learning, mlops, python, python3
- Language: CSS
- Homepage: http://13.233.77.16:8000/
- Size: 434 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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**
```bash
git clone https://github.com/ganeshborkar31/Language-predictor.git
cd Language-predictor
2. **Create and Activate a Virtual Environment**
```bash
python -m venv myvenv
source myvenv/bin/activate # On Windows, use `myvenv\Scripts\activate`
3. **Install the required packages:**
```bash
pip install -r requirements.txt
4. **Ensure requirements.txt includes:**
```Copy code
fastapi
uvicorn
pydantic
scikit-learn
5. **Run the FastAPI application:**
```bash
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:
```json
{
"text": "input text"
}
- Response:
- On success:
```json
{
"language": "Predicted Language"
}
```
- On error (empty input):
```json
{
"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:
```bash
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:
```bash
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:
```bash
kill
## **Contact**
- [email protected]
### 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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