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A newer version of the Gradio SDK is available: 6.24.0

Upgrade

🚀 VISH AI - Quick Start Guide

Installation & Setup (5 minutes)

Step 1: Install Dependencies 

pip install -r requirements.txt

What gets installed:

  • Gradio (UI)
  • FastAPI (API)
  • Transformers (Phi-3)
  • PEFT (LoRA training)
  • Datasets (data handling)

Step 2: Start the Server 

python start.py

Or manually:

python -m app.main

Step 3: Open Browser

Visit: http://localhost:7860

🎯 First Steps

1. Try the Chat

  • Go to "💬 VISH Assistant" tab
  • Type: "Tell me about artificial intelligence"
  • Click Send
  • Notice the interaction ID in the response

2. Submit Feedback

  • Copy the interaction ID (e.g., a1b2c3d4)
  • Go to "⭐ Feedback" tab
  • Paste the ID
  • Rate 1-5 stars
  • Click "Submit Feedback"

3. Check Statistics

  • Go to "📊 Statistics" tab
  • Click "🔄 Refresh Stats"
  • See your interactions and ratings

4. Train the Model (After 10+ interactions)

  • Go to "🎓 Training (Admin)" tab
  • Set minimum samples: 10
  • Set epochs: 3
  • Enter admin key: vish-admin-2024 (default)
  • Click "🚀 Start Training"
  • Wait 10-30 minutes for training

📝 Category Examples

General Assistant 

Category: assistant
Question: "What is machine learning?"

Resume Builder 

Category: resume
Question: "Help me write a software engineer resume"

Research 

Category: research
Question: "Explain quantum computing"

Business 

Category: business
Question: "How do I create a business plan?"

🔐 Admin Key

Default admin key: vish-admin-2024

Change it:

export VISH_ADMIN_KEY="your-secret-key"

Or in .env file:

VISH_ADMIN_KEY=your-secret-key

📊 Understanding the System

Data Flow

  1. User chats → Saved to data/vish_dataset.jsonl
  2. User rates → Saved to data/feedback.jsonl
  3. Training runs → Creates models/vish-ai-mini/latest/
  4. Model reloads → Uses improved version automatically

File Structure 

data/
  ├── vish_dataset.jsonl     # All interactions
  ├── feedback.jsonl         # User ratings
  └── research_data.jsonl    # Research data

models/
  └── vish-ai-mini/
      ├── latest/            # LoRA adapters
      └── metadata.json      # Version info

🎓 Training Process

When to Train

  • After collecting 10+ interactions
  • After significant feedback
  • Weekly/monthly for continuous improvement

Training Time

  • CPU: 10-30 minutes
  • GPU: 2-5 minutes

What Gets Trained

  • High-quality interactions (rating ≥ 3)
  • Deduplicated data
  • LoRA adapters only (efficient!)

Model Versions

Each training creates a version:

  • v20241016_143022
  • v20241017_095234
  • Latest version is used automatically

🚀 Deployment

Hugging Face Spaces

  1. Create Space: https://huggingface.co/new-space
  2. Upload files:
    • app/ folder
    • requirements.txt
    • Dockerfile
    • README.md
  3. Set hardware: CPU Basic (free) or T4 GPU
  4. Wait for build (~15-20 minutes first time)
  5. Done! Your AI is live

Docker 

# Build
docker build -t vish-ai .

# Run
docker run -p 7860:7860 \
  -v $(pwd)/data:/app/data \
  -v $(pwd)/models:/app/models \
  -e VISH_ADMIN_KEY=your-key \
  vish-ai

⚡ Quick Tips

  1. Start with general questions to build dataset
  2. Rate honestly - only good data improves the model
  3. Train regularly - weekly is good
  4. Check stats - monitor improvement
  5. Backup data - copy /data and /models regularly

🐛 Common Issues

"Model not loaded"

  • Wait for initial download (~7GB, 10-15 min)
  • Check logs for errors
  • Verify internet connection

"Insufficient data for training"

  • Need at least 10 interactions
  • Check: curl http://localhost:7860/api/stats

"Out of memory"

  • Use quantization (edit model_handler.py)
  • Reduce batch size in retrain.py
  • Upgrade to GPU

📚 Next Steps

  1. Explore API: Visit http://localhost:7860/docs
  2. Read Full README: See README_SELF_TRAINING.md
  3. Customize: Edit system prompts in gradio_ui.py
  4. Integrate: Use API endpoints in your apps

🎉 Success!

You now have a self-improving AI assistant that:

  • ✅ Learns from your conversations
  • ✅ Improves with your feedback
  • ✅ Trains automatically with LoRA
  • ✅ Tracks performance over time
  • ✅ Works on free-tier hardware

Happy chatting! 🤖

Built with ❤️ by Vishwas | Questions? Open an issue!