🔧 AI Dataset Studio - Complete Troubleshooting Guide

🚨 Immediate Fix for Current Error

Error: "DatasetStudio is not defined"

NameError: name 'DatasetStudio' is not defined

✅ SOLUTION: Replace your current app.py with the complete fixed version I provided above.

Quick Fix Steps:

Replace app.py - Use the complete version from the artifacts above
Add missing files - Download all the files I've provided
Restart your Space - The error will be resolved

📁 Files You Need (Complete Checklist)

File	Status	Purpose
✅ `app.py`	Replace yours	Main application (complete version)
❌ `app_minimal.py`	Missing	Fallback version (basic deps only)
✅ `requirements.txt`	Have it	Dependencies
✅ `README.md`	Have it	Documentation
✅ `config.py`	Have it	Configuration
❌ `utils.py`	Incomplete	Utility functions
❌ `startup.py`	Missing	Smart launcher
❌ `TROUBLESHOOTING.md`	Missing	This guide

🚀 Quick Deployment Options

Option 1: Immediate Fix (Recommended)

# Use the complete app.py I provided above
# This fixes the DatasetStudio error immediately

Option 2: Minimal Version (Guaranteed to Work)

# Use app_minimal.py as your main app.py
# This version works with basic dependencies only

Option 3: Smart Startup (Auto-Detect)

# Use startup.py as your main app.py
# Automatically chooses the best version to run

🔍 Common Issues & Solutions

Issue 1: Missing Dependencies

ModuleNotFoundError: No module named 'transformers'
ModuleNotFoundError: No module named 'bs4'

✅ SOLUTIONS:

A. Minimal Installation (Fastest)

pip install gradio pandas requests beautifulsoup4
# Use app_minimal.py

B. Full Installation

pip install gradio pandas requests beautifulsoup4 transformers torch nltk datasets
# Use app.py (full version)

C. Update requirements.txt

gradio>=4.44.0
pandas>=2.0.0
requests>=2.31.0
beautifulsoup4>=4.12.0

Issue 2: Slow Loading

Application taking too long to start
Models downloading...

✅ SOLUTIONS:

Use CPU Basic hardware initially (loads faster)
Try minimal version first (no AI model downloads)
Upgrade to T4 Small for faster AI model loading

Issue 3: Memory Issues

CUDA out of memory
Application crashed

✅ SOLUTIONS:

Start with CPU Basic (free, lower memory)
Use minimal version (smaller memory footprint)
Upgrade gradually (CPU → T4 → A10G as needed)

Issue 4: Import Errors

Failed to import DatasetStudio
Module not found errors

✅ SOLUTIONS:

Replace app.py with the complete version above
Add all missing files from the artifacts
Clear browser cache and refresh

🏥 Emergency Fixes

Nuclear Option: Start Completely Fresh

Create new Space

Use minimal files only:

- app_minimal.py (rename to app.py)
- requirements.txt (basic only)
- README.md

Set hardware to CPU Basic
Test basic functionality first
Gradually add features

Quick Test Commands

# Test basic imports
python -c "import gradio, pandas, requests; print('✅ Basic imports work')"

# Test BeautifulSoup
python -c "from bs4 import BeautifulSoup; print('✅ BeautifulSoup works')"

# Test full app (if using complete version)
python -c "from app import DatasetStudio; print('✅ DatasetStudio works')"

📊 Version Comparison

Feature	Minimal	Full	Smart
Dependencies	4 packages	8+ packages	Auto-detect
Startup Time	30 seconds	2-5 minutes	Variable
Web Scraping	✅ Basic	✅ Advanced	✅ Auto
AI Features	❌ None	✅ All	✅ If available
Export Formats	JSON, CSV	All formats	Auto
Memory Usage	~100MB	~2GB	Variable
Reliability	🟢 High	🟡 Medium	🟢 High

🎯 Deployment Strategy

Step 1: Start Simple

Files: app_minimal.py → app.py, requirements.txt (minimal)
Hardware: CPU Basic
Goal: Verify basic functionality

Step 2: Add Features

Files: Add complete app.py, config.py, utils.py
Hardware: CPU Upgrade
Goal: Test advanced features

Step 3: Full Power

Files: All files
Hardware: T4 Small or higher
Goal: Production deployment

🔄 Troubleshooting Workflow

1. 🚨 ERROR OCCURS
   ↓
2. 🔍 CHECK THIS GUIDE
   ↓
3. 🛠️ APPLY QUICK FIX
   ↓
4. 🧪 TEST SOLUTION
   ↓
5. ✅ SUCCESS OR ⬆️ ESCALATE

Escalation Path:

Try minimal version → app_minimal.py
Check dependencies → Install missing packages
Review logs → Look for specific errors
Contact support → Provide error details

💡 Pro Tips

Development Best Practices

✅ Start minimal, add complexity gradually
✅ Test locally before deploying
✅ Use version control for file management
✅ Monitor Space logs for errors

Performance Optimization

✅ CPU Basic for development/testing
✅ T4 Small for production
✅ Enable persistent storage for large datasets
✅ Use minimal version when possible

Reliability Tips

✅ Always have a fallback version ready
✅ Test with sample URLs before large batches
✅ Monitor Space analytics for usage patterns
✅ Keep dependencies up to date

🆘 Getting Help

Information to Include When Asking for Help:

1. Exact error message
2. Files you're using (app.py vs app_minimal.py)
3. Hardware type (CPU Basic, T4 Small, etc.)
4. Dependencies installed
5. Space logs (if available)

Quick Health Check Script:

import sys
print(f"Python: {sys.version}")

try:
    import gradio
    print(f"✅ Gradio: {gradio.__version__}")
except ImportError:
    print("❌ Gradio not available")

try:
    from bs4 import BeautifulSoup
    print("✅ BeautifulSoup available")
except ImportError:
    print("❌ BeautifulSoup not available")

try:
    from app import DatasetStudio
    print("✅ DatasetStudio available")
except ImportError as e:
    print(f"❌ DatasetStudio error: {e}")

🎉 Success Indicators

You'll know everything is working when you see:

🚀 Starting AI Dataset Studio...
📊 Features: ✅ AI Models | ✅ Advanced NLP | ✅ HuggingFace Integration
✅ DatasetStudio initialized successfully
✅ Interface created successfully
Running on local URL: http://0.0.0.0:7860

If you see this, you're ready to create amazing datasets! 🎯

📞 Support Channels

📖 Documentation: README.md in your Space
💬 Community: HuggingFace Discussions
🐛 Bug Reports: Include logs and error details
📧 Direct Help: Describe your setup and error

Remember: Every issue has a solution - start with the minimal version and build up! 💪