🌐 Multilingual Translation Web Application

A full-stack web application that translates Hindi text into English, Tamil, and Bengali using a FastAPI backend and an interactive Streamlit frontend.

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🚀 Live Demo

🌐 Frontend

https://hdbcqj9nrnpwk8qpzdvzkr.streamlit.app/

⚡ Backend API

https://svcore-ai-translation-api.onrender.com

📖 API Documentation (Swagger UI)

https://svcore-ai-translation-api.onrender.com/docs

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✨ Features

• Translate Hindi text into:

  • English
  • Tamil
  • Bengali

• FastAPI REST API backend

• Interactive Streamlit user interface

• Real-time translation requests

• Character counter

• Loading spinner

• Clear button

• Error handling

• Modular project structure

• Render deployment support

• Streamlit Community Cloud deployment

• Responsive multi-column translation display

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📂 Project Structure

TASK/
├── backend/
│   ├── main.py
│   └── requirements.txt
│
├── frontend/
│   ├── app.py
│   └── requirements.txt
│
└── docs/
    └── README.md

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🛠 Tech Stack

Backend

• Python
• FastAPI
• Pydantic
• Uvicorn
• Deep Translator

Frontend

• Streamlit
• Requests

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⚙️ Backend Setup

1. Clone Repository

git clone https://github.com/Shadow-linux-png/SVCORE-AI-.git
cd SVCORE-AI-

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2. Navigate to Backend

cd backend

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3. Create Virtual Environment

Windows

python -m venv venv
venv\Scripts\activate

Linux / Mac

python -m venv venv
source venv/bin/activate

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4. Install Dependencies

pip install -r requirements.txt

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5. Run FastAPI Server

uvicorn main:app --reload --port 8000

Backend runs on:

http://localhost:8000

Swagger Documentation:

http://localhost:8000/docs

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⚙️ Frontend Setup

1. Navigate to Frontend

cd frontend

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2. Create Virtual Environment

Windows

python -m venv venv
venv\Scripts\activate

Linux / Mac

python -m venv venv
source venv/bin/activate

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3. Install Dependencies

pip install -r requirements.txt

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4. Run Streamlit Application

streamlit run app.py

Application runs on:

http://localhost:8501

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📡 API Endpoint

Translate Text

Endpoint

POST /translate

Request

{
  "text": "हेलो"
}

Response

{
  "english": "Hello",
  "tamil": "ஹலோ",
  "bengali": "হ্যালো"
}

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🧪 Example Usage

Input

मेरा नाम आर्यन है

Output

English : My name is Aryan
Tamil    : என் பெயர் ஆர்யன்
Bengali  : আমার নাম আর্যন

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☁️ Deployment

Backend Deployment (Render)

Create a new Web Service.

Root Directory

backend

Build Command

pip install -r requirements.txt

Start Command

uvicorn main:app --host 0.0.0.0 --port $PORT

After deployment:

https://your-app-name.onrender.com
https://your-app-name.onrender.com/docs

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Frontend Deployment (Streamlit Community Cloud)

Update the backend URL inside app.py:

FASTAPI_URL = "https://your-render-url.onrender.com"

Deploy using:

• Repository: GitHub Repository
• Branch: main
• Main File Path:

frontend/app.py

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🎯 Learning Outcomes

This project demonstrates:

• REST API development using FastAPI
• API integration using Requests
• Frontend development using Streamlit
• Request and response handling
• Backend and frontend separation
• Deployment of Python applications
• Cloud deployment using Render and Streamlit
• Integration of multilingual translation services
• Building production-ready AI-powered web applications

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👨‍💻 Author

Aryan Shukla B.Tech CSE (AI/ML) Student

Skills

• Python
• FastAPI
• Machine Learning
• Retrieval-Augmented Generation (RAG)
• AI Applications
• Backend Development

GitHub

https://github.com/Shadow-linux-png

LinkedIn

https://www.linkedin.com/in/aryan-shukla-3821b135a/

Portfolio

https://auto-repo-shine.lovable.app/

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