setup_instructions.md

🚀 Setup Instructions for New ABTestPredictor Repository

Files to Upload to Your New Hugging Face Space

1. Core Application Files

• app.py - Main application with dual-AI integration
• requirements.txt - Python dependencies
• packages.txt - System packages
• README.md - Documentation

2. Data Files

• metadata.js - Category definitions and mappings
• confidence_scores.js - Confidence scores for Industry + Page Type combinations
• patterbs.json - Pattern descriptions for Gemini Pro analysis

3. Model Files

• model/multimodal_cat_mappings_GGG.json - Category mappings for GGG model
• Upload multimodal_gated_model_2.7_GGG.pth directly via Hugging Face Files tab

🔑 Required API Keys (Set in Spaces Settings)

Secrets to Add:

1. Name: PERPLEXITY_API_KEY Value: Your Perplexity API key (starts with pplx-)

2. Name: GEMINI_API_KEY
   Value: Your Google Gemini API key

🚀 Upload Process

Option 1: Manual Upload

1. Go to your new Hugging Face Space
2. Upload all files via the web interface
3. Set the API keys in Settings → Variables and secrets

Option 2: Git Upload

1. Clone your new repository: git clone https://huggingface.co/spaces/nitish-spz/ABTestPredictor
2. Copy all files from this directory to the cloned directory
3. Commit and push: git add . && git commit -m "Complete app setup" && git push

✅ Verification

After upload, your space should show:

• ✅ Dual-AI powered analysis tabs
• ✅ Enhanced model architecture loaded
• ✅ 359 pattern detection capabilities
• ✅ Confidence scoring with training statistics

🎯 Features Ready

• Smart Auto-Prediction: AI categorization + pattern detection
• Manual Selection: Traditional dropdown interface
• Batch Prediction: CSV file processing
• Enhanced Results: Comprehensive analysis with confidence metrics

Your enhanced A/B test predictor with dual-AI analysis is ready to deploy! 🎉