# 🚀 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! 🎉