docs/CLOUD_DEPLOYMENT.md

🌐 Free Cloud Deployment Guide

🎯 Best Free Options for Your Project

✅ Recommended: Streamlit Community Cloud

• Perfect for your project (Streamlit frontend)
• Completely free
• Easy GitHub integration
• Custom domain support

✅ Alternative: Hugging Face Spaces

• Free GPU/CPU hosting
• Perfect for AI/ML projects
• Great for showcasing AI models

✅ Backup: Railway/Render

• Full-stack deployment
• Free tiers available
• Good for production demos

---

🚀 Option 1: Streamlit Community Cloud (RECOMMENDED)

Prerequisites:

1. GitHub account (free)
2. Streamlit account (free - sign up with GitHub)

Step 1: Prepare Your Repository

Create these files for Streamlit Cloud deployment:

requirements.txt (for Streamlit Cloud)

# Core dependencies
streamlit==1.28.2
requests==2.31.0
pandas==2.1.3
numpy==1.24.3
python-dateutil==2.8.2

# Visualization
plotly==5.17.0
altair==5.1.2

# UI components
streamlit-option-menu==0.3.6
streamlit-aggrid==0.3.4.post3

# For language detection (lightweight)
langdetect==1.0.9

streamlit_app.py (Entry point)

# Streamlit Cloud entry point
import streamlit as st
import sys
import os

# Add frontend directory to path
sys.path.append(os.path.join(os.path.dirname(__file__), 'frontend'))

# Import the main app
from app import main

if __name__ == "__main__":
    main()

.streamlit/config.toml (Streamlit configuration)

[server]
headless = true
port = 8501

[browser]
gatherUsageStats = false

[theme]
primaryColor = "#FF6B6B"
backgroundColor = "#FFFFFF"
secondaryBackgroundColor = "#F0F2F6"
textColor = "#262730"

Step 2: Create Cloud-Compatible Backend

Since Streamlit Cloud can't run your FastAPI backend, we'll create a lightweight version:

cloud_backend.py (Mock backend for demo)

"""
Lightweight backend simulation for Streamlit Cloud deployment
This provides mock responses that look realistic for demos
"""

import random
import time
from typing import Dict, List
import pandas as pd
from datetime import datetime

class CloudTranslationService:
    """Mock translation service for cloud deployment"""
    
    def __init__(self):
        self.languages = {
            "en": "English", "hi": "Hindi", "bn": "Bengali", 
            "gu": "Gujarati", "kn": "Kannada", "ml": "Malayalam",
            "mr": "Marathi", "or": "Odia", "pa": "Punjabi",
            "ta": "Tamil", "te": "Telugu", "ur": "Urdu",
            "as": "Assamese", "ne": "Nepali", "sa": "Sanskrit"
        }
        
        # Sample translations for realistic demo
        self.sample_translations = {
            ("hello", "en", "hi"): "नमस्ते",
            ("smartphone", "en", "hi"): "स्मार्टफोन",
            ("book", "en", "hi"): "किताब",
            ("computer", "en", "hi"): "कंप्यूटर",
            ("beautiful", "en", "hi"): "सुंदर",
            ("hello", "en", "ta"): "வணக்கம்",
            ("smartphone", "en", "ta"): "ஸ்மார்ட்ஃபோன்",
            ("book", "en", "ta"): "புத்தகம்",
            ("hello", "en", "te"): "నమస్కారం",
            ("smartphone", "en", "te"): "స్మార్ట్‌ఫోన్",
        }
        
        # Mock translation history
        self.history = []
        self._generate_sample_history()
    
    def _generate_sample_history(self):
        """Generate realistic sample history"""
        sample_data = [
            ("Premium Smartphone with 128GB storage", "प्रीमियम स्मार्टफोन 128GB स्टोरेज के साथ", "en", "hi", 0.94),
            ("Wireless Bluetooth Headphones", "वायरलेस ब्लूटूथ हेडफोन्स", "en", "hi", 0.91),
            ("Cotton T-Shirt for Men", "पुरुषों के लिए कॉटन टी-शर्ट", "en", "hi", 0.89),
            ("Premium Smartphone with 128GB storage", "128GB சேமிப்பகத்துடன் பிரீமியம் ஸ்மார்ட்ஃபோன்", "en", "ta", 0.92),
            ("Wireless Bluetooth Headphones", "వైర్‌లెస్ బ్లూటూత్ హెడ్‌ఫోన్‌లు", "en", "te", 0.90),
        ]
        
        for i, (orig, trans, src, tgt, conf) in enumerate(sample_data):
            self.history.append({
                "id": i + 1,
                "original_text": orig,
                "translated_text": trans,
                "source_language": src,
                "target_language": tgt,
                "model_confidence": conf,
                "created_at": "2025-01-25T10:30:00",
                "corrected_text": None
            })
    
    def detect_language(self, text: str) -> Dict:
        """Mock language detection"""
        # Simple heuristic detection
        if any(char in text for char in "अआइईउऊएऐओऔकखगघचछजझटठडढणतथदधनपफबभमयरलवशषसह"):
            return {"language": "hi", "confidence": 0.95, "language_name": "Hindi"}
        elif any(char in text for char in "அஆஇஈஉஊஎஏஐஒஓஔகஙசஞடணதநபமயரலவழளறன"):
            return {"language": "ta", "confidence": 0.94, "language_name": "Tamil"}
        else:
            return {"language": "en", "confidence": 0.98, "language_name": "English"}
    
    def translate(self, text: str, source_lang: str, target_lang: str) -> Dict:
        """Mock translation with realistic responses"""
        time.sleep(1)  # Simulate processing time
        
        # Check for exact matches first
        key = (text.lower(), source_lang, target_lang)
        if key in self.sample_translations:
            translated = self.sample_translations[key]
            confidence = round(random.uniform(0.88, 0.96), 2)
        else:
            # Generate realistic-looking translations
            if target_lang == "hi":
                translated = f"[Hindi] {text}"
            elif target_lang == "ta":
                translated = f"[Tamil] {text}"
            elif target_lang == "te":
                translated = f"[Telugu] {text}"
            else:
                translated = f"[{self.languages.get(target_lang, target_lang)}] {text}"
            
            confidence = round(random.uniform(0.82, 0.94), 2)
        
        # Add to history
        translation_id = len(self.history) + 1
        self.history.append({
            "id": translation_id,
            "original_text": text,
            "translated_text": translated,
            "source_language": source_lang,
            "target_language": target_lang,
            "model_confidence": confidence,
            "created_at": datetime.now().isoformat(),
            "corrected_text": None
        })
        
        return {
            "translated_text": translated,
            "source_language": source_lang,
            "target_language": target_lang,
            "confidence": confidence,
            "translation_id": translation_id
        }
    
    def get_history(self, limit: int = 50) -> List[Dict]:
        """Get translation history"""
        return self.history[-limit:]
    
    def submit_correction(self, translation_id: int, corrected_text: str, feedback: str = "") -> Dict:
        """Submit correction"""
        for item in self.history:
            if item["id"] == translation_id:
                item["corrected_text"] = corrected_text
                break
        
        return {
            "correction_id": random.randint(1000, 9999),
            "message": "Correction submitted successfully",
            "status": "success"
        }
    
    def get_supported_languages(self) -> Dict:
        """Get supported languages"""
        return {
            "languages": self.languages,
            "total_count": len(self.languages)
        }

# Global instance
cloud_service = CloudTranslationService()

Step 3: Modify Frontend for Cloud

frontend/cloud_app.py (Cloud-optimized version)

"""
Cloud-optimized version of the Multi-Lingual Catalog Translator
Works without FastAPI backend by using mock services
"""

import streamlit as st
import sys
import os

# Add parent directory to path to import cloud_backend
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from cloud_backend import cloud_service

# Copy your existing app.py code here but replace API calls with cloud_service calls
# For example:

st.set_page_config(
    page_title="Multi-Lingual Catalog Translator",
    page_icon="🌐",
    layout="wide"
)

def main():
    st.title("🌐 Multi-Lingual Product Catalog Translator")
    st.markdown("### Powered by IndicTrans2 by AI4Bharat")
    st.markdown("**🚀 Cloud Demo Version**")
    
    # Add a banner explaining this is a demo
    st.info("🌟 **This is a cloud demo version with simulated AI responses**. The full version with real IndicTrans2 models runs locally and can be deployed on cloud infrastructure with GPU support.")
    
    # Your existing UI code here...
    # Replace API calls with cloud_service calls

if __name__ == "__main__":
    main()

Step 4: Deploy to Streamlit Cloud

1. Push to GitHub:

   git add .
   git commit -m "Add Streamlit Cloud deployment"
   git push origin main
   

2. Deploy on Streamlit Cloud:

   • Go to share.streamlit.io
   • Sign in with GitHub
   • Click "New app"
   • Select your repository
   • Set main file path: streamlit_app.py
   • Click "Deploy"

3. Your app will be live at: https://[your-username]-[repo-name]-streamlit-app-[hash].streamlit.app

---

🤗 Option 2: Hugging Face Spaces

Perfect for AI/ML projects with free GPU access!

Step 1: Create Space Files

app.py (Hugging Face entry point)

import gradio as gr
import requests
import json

def translate_text(text, source_lang, target_lang):
    # Your translation logic here
    # Can use the cloud_backend for demo
    return f"Translated: {text} ({source_lang} → {target_lang})"

# Create Gradio interface
demo = gr.Interface(
    fn=translate_text,
    inputs=[
        gr.Textbox(label="Text to translate"),
        gr.Dropdown(["en", "hi", "ta", "te", "bn"], label="Source Language"),
        gr.Dropdown(["en", "hi", "ta", "te", "bn"], label="Target Language")
    ],
    outputs=gr.Textbox(label="Translation"),
    title="Multi-Lingual Catalog Translator",
    description="AI-powered translation for e-commerce using IndicTrans2"
)

if __name__ == "__main__":
    demo.launch()

requirements.txt (for Hugging Face)

gradio==3.50.0
transformers==4.35.0
torch==2.1.0
fasttext==0.9.2

Step 2: Deploy to Hugging Face

1. Create account at huggingface.co
2. Create new Space
3. Upload your files
4. Your app will be live at https://huggingface.co/spaces/[username]/[space-name]

---

🚂 Option 3: Railway (Full-Stack)

For deploying both frontend and backend:

Step 1: Create Railway Configuration

railway.json

{
  "build": {
    "builder": "NIXPACKS"
  },
  "deploy": {
    "startCommand": "streamlit run streamlit_app.py --server.port $PORT --server.address 0.0.0.0",
    "healthcheckPath": "/",
    "healthcheckTimeout": 100
  }
}

Step 2: Deploy

1. Go to railway.app
2. Connect GitHub repository
3. Deploy automatically

---

📋 Quick Setup for Streamlit Cloud

Let me create the necessary files for you: