README_HF.md

metadata

title: Vish AI
emoji: 🌟
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.19.2
app_file: app.py
pinned: false
license: mit

Vish AI - Virtual Intelligent System Hub

Production-ready, lightweight, multimodal AI assistant optimized for Hugging Face Spaces

Features

• 💬 Chat Assistant: Natural conversation using DistilGPT2 (82MB)
• 📝 Text Summarization: Condense articles with DistilBART (300MB)
• 😊 Sentiment Analysis: Emotion detection with DistilBERT (255MB)
• 🔐 Supabase Integration: User authentication & logging
• ⚡ Fast Performance: 0.5-3s response time on CPU

Performance

• Total Model Size: ~650MB
• Memory Usage: <2GB RAM
• CPU Optimized: No GPU required
• Free Tier Friendly: Runs on HF basic tier

Configuration

Required Secrets (in Space Settings)

NEXT_PUBLIC_SUPABASE_URL=your_supabase_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_anon_key

Optional Secrets

SUPABASE_JWT_SECRET=your_jwt_secret
SUPABASE_SERVICE_ROLE_KEY=your_service_role_key

Models Used

Model	Size	Purpose	Speed
DistilGPT2	82MB	Chat	~0.5-2s
DistilBART-CNN-6-6	300MB	Summarization	~1-3s
DistilBERT-SST2	255MB	Sentiment	~0.3-1s

🌐 Integration

API Usage

import requests

response = requests.post(
    "https://vishwas896-vish-ai.hf.space/api/predict",
    json={
        "data": ["Hello Vish AI!", [], ""],
        "fn_index": 0  # 0=chat, 1=summarize, 2=sentiment
    }
)

Next.js/React Integration

const callVishAI = async (message: string) => {
  const res = await fetch('YOUR_HF_SPACE_URL/api/predict', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      data: [message, [], ""],
      fn_index: 0
    })
  });
  const result = await res.json();
  return result.data[0];
};

🗄️ Supabase Setup

Run this SQL in your Supabase project:

CREATE TABLE vish_ai_logs (
    id BIGSERIAL PRIMARY KEY,
    user_email TEXT,
    prompt TEXT,
    response TEXT,
    model_type TEXT,
    timestamp TIMESTAMPTZ DEFAULT NOW()
);

CREATE INDEX idx_vish_ai_logs_user ON vish_ai_logs(user_email);
CREATE INDEX idx_vish_ai_logs_timestamp ON vish_ai_logs(timestamp DESC);

🛠️ Local Development

# Clone repository
git clone https://huggingface.co/spaces/Vishwas896/Vish-AI
cd Vish-AI

# Install dependencies
pip install -r requirements.txt

# Set environment variables
export NEXT_PUBLIC_SUPABASE_URL="your_url"
export NEXT_PUBLIC_SUPABASE_ANON_KEY="your_key"

# Run application
python app.py

📈 Usage Stats

• Model Loading Time: 30-60s (first run only)
• Response Time: 0.5-3s per request
• Concurrent Users: Up to 10-20 on free tier
• Storage: ~2GB (models cached)

🔒 Security

• Environment variables for sensitive keys
• Row-level security on Supabase
• Optional JWT authentication
• Anonymous mode supported

📝 License

MIT License - Free for personal and commercial use

🙏 Credits

• Hugging Face: Model hosting
• Supabase: Backend infrastructure
• Gradio: UI framework

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Built for the VIJ Project | GitHub | Supabase