Deploy: Optimize for Hugging Face Spaces - remove AI APIs and unnecessary files

API_USAGE.md

@@ -1,321 +0,0 @@
-# A/B Test Predictor API - Integration Guide
-
-This guide shows you how to integrate the A/B Test Predictor model into your own applications.
-
-## 🚀 Quick Start
-
-Your Hugging Face Space provides a built-in API that you can call from any application!
-
-**Space URL**: `https://nitish-spz-abtestpredictorv2.hf.space`
-
----
-
-## Method 1: Using Gradio Client (Recommended)
-
-### Python
-
-```python
-from gradio_client import Client
-from PIL import Image
-
-# Connect to your Space
-client = Client("nitish-spz/ABTestPredictorV2")
-
-# Option A: Auto-categorization (AI analyzes images automatically)
-result = client.predict(
-    control_image="./path/to/control.jpg",  # Local file path
-    variant_image="./path/to/variant.jpg",   # Local file path
-    api_name="/predict_with_auto_categorization"
-)
-
-print(result)
-# Output: Full JSON with prediction, categories, pattern, and confidence scores
-
-# Option B: Manual categorization (you provide the categories)
-result = client.predict(
-    control_image="./control.jpg",
-    variant_image="./variant.jpg",
-    business_model="SaaS",
-    customer_type="B2B",
-    conversion_type="High-Intent Lead Gen",
-    industry="B2B Software & Tech",
-    page_type="Conversion",
-    api_name="/predict_single"
-)
-
-print(result)
-```
-
-### JavaScript/Node.js
-
-```javascript
-import { client } from "@gradio/client";
-
-const app = await client("nitish-spz/ABTestPredictorV2");
-
-// Upload images and get prediction
-const result = await app.predict("/predict_with_auto_categorization", [
-    "./control.jpg",  // Control image path
-    "./variant.jpg"   // Variant image path
-]);
-
-console.log(result.data);
-```
-
----
-
-## Method 2: Direct HTTP API Calls
-
-### Python with requests
-
-```python
-import requests
-import base64
-
-# Function to encode image as base64
-def encode_image(image_path):
-    with open(image_path, "rb") as image_file:
-        return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Encode images
-control_b64 = encode_image("control.jpg")
-variant_b64 = encode_image("variant.jpg")
-
-# Make API call
-response = requests.post(
-    "https://nitish-spz-abtestpredictorv2.hf.space/api/predict",
-    json={
-        "data": [control_b64, variant_b64]
-    }
-)
-
-result = response.json()
-print(result)
-```
-
-### JavaScript/Fetch
-
-```javascript
-async function predictABTest(controlImageFile, variantImageFile) {
-    // Convert files to base64
-    const controlBase64 = await fileToBase64(controlImageFile);
-    const variantBase64 = await fileToBase64(variantImageFile);
-    
-    // Make API call
-    const response = await fetch(
-        'https://nitish-spz-abtestpredictorv2.hf.space/api/predict',
-        {
-            method: 'POST',
-            headers: {
-                'Content-Type': 'application/json',
-            },
-            body: JSON.stringify({
-                data: [controlBase64, variantBase64]
-            })
-        }
-    );
-    
-    const result = await response.json();
-    return result;
-}
-
-function fileToBase64(file) {
-    return new Promise((resolve, reject) => {
-        const reader = new FileReader();
-        reader.readAsDataURL(file);
-        reader.onload = () => resolve(reader.result.split(',')[1]);
-        reader.onerror = error => reject(error);
-    });
-}
-```
-
-### cURL
-
-```bash
-# Using Gradio API
-curl -X POST https://nitish-spz-abtestpredictorv2.hf.space/api/predict \
-  -H "Content-Type: application/json" \
-  -d '{
-    "data": [
-      "data:image/jpeg;base64,/9j/4AAQSkZJRg...",
-      "data:image/jpeg;base64,/9j/4AAQSkZJRg..."
-    ]
-  }'
-```
-
----
-
-## Method 3: Embed in Your Website (iframe)
-
-```html
-<iframe
-  src="https://nitish-spz-abtestpredictorv2.hf.space"
-  frameborder="0"
-  width="100%"
-  height="800"
-></iframe>
-```
-
----
-
-## Method 4: React Integration
-
-```jsx
-import React, { useState } from 'react';
-import { client } from "@gradio/client";
-
-function ABTestPredictor() {
-    const [result, setResult] = useState(null);
-    const [loading, setLoading] = useState(false);
-    
-    const handlePredict = async (controlFile, variantFile) => {
-        setLoading(true);
-        try {
-            const app = await client("nitish-spz/ABTestPredictorV2");
-            const prediction = await app.predict("/predict_with_auto_categorization", [
-                controlFile,
-                variantFile
-            ]);
-            setResult(prediction.data);
-        } catch (error) {
-            console.error("Prediction failed:", error);
-        } finally {
-            setLoading(false);
-        }
-    };
-    
-    return (
-        <div>
-            <input 
-                type="file" 
-                onChange={(e) => handlePredict(e.target.files[0], variantFile)} 
-            />
-            {/* Display result */}
-            {result && <pre>{JSON.stringify(result, null, 2)}</pre>}
-        </div>
-    );
-}
-```
-
----
-
-## Response Format
-
-The API returns a JSON object with the following structure:
-
-```json
-{
-  "🎯 Prediction Results": {
-    "🏆 VARIANT WINS": "0.987",
-    "📊 Model Confidence": "64.7%",
-    "📈 Training Data": "1656 samples",
-    "✅ Historical Accuracy": "119/184 correct",
-    "🎯 Win/Loss Ratio": "71 wins in 184 tests"
-  },
-  "🤖 Auto-Detected Categories": {
-    "Business Model": "SaaS",
-    "Customer Type": "B2B",
-    "Conversion Type": "High-Intent Lead Gen",
-    "Industry": "B2B Software & Tech",
-    "Page Type": "Conversion"
-  },
-  "🎯 Detected A/B Test Pattern": {
-    "Pattern": "Double Column Form",
-    "Description": "The variant implements a 'Double Column Form' modification"
-  },
-  "📊 Processing Info": {
-    "Total Processing Time": "29.58s",
-    "AI Categorization": "✅ Perplexity Sonar Reasoning Pro",
-    "Pattern Detection": "✅ Gemini Pro Vision",
-    "Confidence Source": "B2B Software & Tech | Conversion",
-    "Total Patterns Analyzed": 359
-  }
-}
-```
-
----
-
-## Rate Limits & Best Practices
-
-1. **Free Tier**: Hugging Face Spaces have rate limits. For high-volume usage, consider upgrading to a paid plan.
-
-2. **Image Size**: Keep images under 5MB for faster processing.
-
-3. **Caching**: Cache results for identical image pairs to avoid redundant API calls.
-
-4. **Error Handling**: Always implement proper error handling for API calls.
-
-5. **Async Processing**: For batch processing, use async/await or threading.
-
----
-
-## Example: Full Integration in a Web App
-
-```python
-from flask import Flask, request, jsonify
-from gradio_client import Client
-
-app = Flask(__name__)
-predictor = Client("nitish-spz/ABTestPredictorV2")
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    try:
-        # Get images from request
-        control_img = request.files['control_image']
-        variant_img = request.files['variant_image']
-        
-        # Save temporarily
-        control_path = f"/tmp/{control_img.filename}"
-        variant_path = f"/tmp/{variant_img.filename}"
-        control_img.save(control_path)
-        variant_img.save(variant_path)
-        
-        # Run prediction
-        result = predictor.predict(
-            control_path,
-            variant_path,
-            api_name="/predict_with_auto_categorization"
-        )
-        
-        return jsonify({
-            "success": True,
-            "data": result
-        })
-    except Exception as e:
-        return jsonify({
-            "success": False,
-            "error": str(e)
-        }), 500
-
-if __name__ == '__main__':
-    app.run(debug=True)
-```
-
----
-
-## Need Help?
-
-- Check the [Gradio Client Documentation](https://www.gradio.app/guides/getting-started-with-the-python-client)
-- Visit your Space: https://huggingface.co/spaces/nitish-spz/ABTestPredictorV2
-- For issues, check the Space logs
-
----
-
-## Authentication (if you make the Space private)
-
-If you set your Space to private, users will need an authentication token:
-
-```python
-from gradio_client import Client
-
-client = Client(
-    "nitish-spz/ABTestPredictorV2",
-    hf_token="hf_..."  # Your Hugging Face token
-)
-```
-
----
-
-**Happy Integrating! 🚀**
-

DEPLOY_NOW.sh

@@ -0,0 +1,78 @@
+#!/bin/bash
+
+# Quick deployment script for Hugging Face Space
+# This removes unnecessary files and deploys the Space
+
+echo "🚀 Preparing to deploy ABTestPredictor Space..."
+echo ""
+
+# Step 1: Remove unnecessary files
+echo "🗑️  Removing unnecessary files..."
+rm -f patterbs.json
+rm -f metadata.js
+rm -f confidence_scores.js
+rm -f frontend.html
+rm -f index_v2.html
+rm -f API_USAGE.md
+rm -rf model/
+
+echo "✅ Cleanup complete"
+echo ""
+
+# Step 2: Check required files
+echo "📋 Checking required files..."
+required_files=("app.py" "requirements.txt" "packages.txt" "README.md" "confidence_scores.json")
+missing_files=()
+
+for file in "${required_files[@]}"; do
+    if [ ! -f "$file" ]; then
+        missing_files+=("$file")
+    fi
+done
+
+if [ ${#missing_files[@]} -gt 0 ]; then
+    echo "❌ Missing required files:"
+    printf '%s\n' "${missing_files[@]}"
+    exit 1
+fi
+
+echo "✅ All required files present"
+echo ""
+
+# Step 3: Show what will be committed
+echo "📦 Files to be committed:"
+git add .
+git status --short
+echo ""
+
+# Step 4: Confirm before pushing
+read -p "🤔 Do you want to commit and push these changes? (y/n) " -n 1 -r
+echo ""
+
+if [[ $REPLY =~ ^[Yy]$ ]]; then
+    # Commit
+    echo "💾 Committing changes..."
+    git commit -m "Deploy: Remove AI APIs, optimize for Hugging Face Spaces
+
+- Removed Perplexity and Gemini API integrations
+- Removed unnecessary files (patterbs.json, metadata.js, etc.)
+- Model will download from nitish-spz/ABTestPredictor
+- Added categorical data input API
+- Optimized with .dockerignore"
+    
+    # Push
+    echo "🚀 Pushing to Hugging Face Space..."
+    git push origin main
+    
+    echo ""
+    echo "✅ Deployment complete!"
+    echo ""
+    echo "🔍 Monitor your Space build at:"
+    echo "   https://huggingface.co/spaces/SpiralyzeLLC/ABTestPredictor"
+    echo ""
+    echo "⏱️  Expected build time: 5-10 minutes"
+else
+    echo "❌ Deployment cancelled"
+    git reset
+fi
+

SPACE_SETUP_GUIDE.md

@@ -0,0 +1,230 @@
+# Hugging Face Space Setup Guide
+
+## Understanding Your Repositories
+
+You have **TWO** separate repositories:
+
+### 1. Model Repository: `nitish-spz/ABTestPredictor`
+- **Purpose**: Store the model files
+- **Contains**: 
+  - `multimodal_gated_model_2.7_GGG.pth` (789 MB)
+  - `multimodal_cat_mappings_GGG.json`
+- **Access**: Read-only, Space downloads from here
+
+### 2. Space Repository: `SpiralyzeLLC/ABTestPredictor`
+- **Purpose**: Run the Gradio application
+- **Contains**: All application code + downloads model from repo #1
+- **Access**: This is what deploys and runs
+
+## Required Files in Space Repository
+
+Your Space needs these files (but NOT the large model file):
+
+```
+✅ app.py                          # Main application code
+✅ requirements.txt                # Python dependencies  
+✅ packages.txt                    # System dependencies (tesseract-ocr)
+✅ README.md                       # Project documentation
+✅ confidence_scores.json          # Confidence data (14KB)
+✅ .gitattributes                  # Git LFS config
+✅ .dockerignore                   # Build optimization
+❌ model/ folder                   # NOT needed - downloads from model repo
+❌ patterbs.json                   # NOT needed - removed feature
+❌ metadata.js                     # NOT needed - removed feature
+❌ confidence_scores.js            # NOT needed - use .json instead
+```
+
+## How the Model Loading Works
+
+Your `app.py` is configured to:
+1. Check if model exists locally in `model/` folder
+2. If not, download from `nitish-spz/ABTestPredictor` model repository
+3. Cache it for future use
+
+```python
+# In app.py lines 707-748
+if os.path.exists(MODEL_SAVE_PATH):
+    model_path = MODEL_SAVE_PATH
+    print(f"✅ Using local model")
+else:
+    print(f"📥 Downloading from Model Hub...")
+    model_path = download_model_from_hub()
+```
+
+## Deployment Steps
+
+### Step 1: Verify Required Files Exist Locally
+```bash
+cd /Users/nitish/Spiralyze/HuggingFace/Spaces/ABTestPredictor
+
+# Check essential files
+ls -lh app.py requirements.txt packages.txt README.md confidence_scores.json
+
+# Should all exist
+```
+
+### Step 2: Remove Large/Unnecessary Files
+```bash
+# Remove the local model folder (Space will download from model repo)
+rm -rf model/
+
+# Remove unused files from old version
+rm -f patterbs.json metadata.js confidence_scores.js frontend.html index_v2.html
+```
+
+### Step 3: Verify Git Remote Points to Space
+```bash
+git remote -v
+# Should show: https://huggingface.co/spaces/SpiralyzeLLC/ABTestPredictor
+```
+
+### Step 4: Commit and Push to Space
+```bash
+# Add all files
+git add .
+
+# Commit
+git commit -m "Deploy: Add all application files, download model from hub"
+
+# Push to Space
+git push origin main
+```
+
+### Step 5: Monitor Build
+1. Go to https://huggingface.co/spaces/SpiralyzeLLC/ABTestPredictor
+2. Click "Logs" tab
+3. Watch the build progress
+4. First build takes 5-10 minutes (downloading model)
+
+## If Build Fails
+
+### Check These Files Exist in Space Repo:
+```bash
+# Essential files checklist
+app.py                  ✅
+requirements.txt        ✅
+packages.txt           ✅
+README.md              ✅
+confidence_scores.json ✅
+.dockerignore          ✅
+.gitattributes         ✅
+```
+
+### Verify Model Repo is Accessible
+Your app downloads from `nitish-spz/ABTestPredictor`. Verify:
+1. Go to https://huggingface.co/nitish-spz/ABTestPredictor
+2. Check files are visible
+3. Make sure it's **public** (not private)
+
+### Check requirements.txt
+```bash
+cat requirements.txt
+```
+Should contain:
+```
+torch
+transformers
+pandas
+scikit-learn
+Pillow
+gradio
+pytesseract
+spaces
+huggingface_hub
+python-dotenv
+```
+
+### Check packages.txt
+```bash
+cat packages.txt
+```
+Should contain:
+```
+tesseract-ocr
+```
+
+## Common Issues
+
+### Issue 1: "Model file not found"
+**Cause**: Model repo is private or inaccessible
+**Fix**: Make `nitish-spz/ABTestPredictor` public
+
+### Issue 2: "No module named X"
+**Cause**: Missing dependency in requirements.txt
+**Fix**: Add the missing package to requirements.txt
+
+### Issue 3: "Tesseract not found"
+**Cause**: Missing system dependency
+**Fix**: Ensure packages.txt contains `tesseract-ocr`
+
+### Issue 4: Build hangs at "Installing requirements"
+**Cause**: PyTorch is large (~2GB)
+**Fix**: Wait 5-10 minutes, this is normal
+
+## Space Configuration
+
+Your Space should have these settings:
+- **SDK**: Gradio
+- **SDK Version**: 4.44.0
+- **Hardware**: GPU (recommended: T4 or better)
+- **Python Version**: 3.10 (default)
+- **Visibility**: Public or Private (your choice)
+
+## File Size Limits
+
+- **Space repo**: Each file < 50MB (except LFS)
+- **Model repo**: Files > 10MB should use Git LFS
+- **Total Space size**: No hard limit, but keep it reasonable
+
+## Success Indicators
+
+✅ Build completes without errors
+✅ Space status shows "Running"  
+✅ Can access the Gradio interface
+✅ Making predictions returns results
+✅ Logs show "Successfully loaded model"
+
+## Expected First-Run Behavior
+
+```
+🚀 Using device: cuda
+🔥 GPU: Tesla T4
+📥 Model not found locally, downloading from Model Hub...
+📥 Downloading model from Hugging Face Model Hub: nitish-spz/ABTestPredictor
+✅ Model downloaded to: /home/user/.cache/huggingface/...
+✅ Successfully loaded GGG model weights
+✅ Model and processors loaded successfully.
+Running on public URL: https://spiralyzellc-abtestpredictor.hf.space
+```
+
+## Testing After Deployment
+
+### Test 1: Web Interface
+1. Visit your Space URL
+2. Upload test images
+3. Select categories
+4. Click predict
+5. Should see results in ~3-5 seconds
+
+### Test 2: API Client
+```python
+from gradio_client import Client
+
+client = Client("SpiralyzeLLC/ABTestPredictor")
+result = client.predict(
+    "control.jpg",
+    "variant.jpg", 
+    "SaaS", "B2B", "High-Intent Lead Gen",
+    "B2B Software & Tech", "Awareness & Discovery",
+    api_name="/predict_with_categorical_data"
+)
+print(result)
+```
+
+## Need Help?
+
+1. Check Space logs for errors
+2. Review DEPLOYMENT_FIX.md for detailed troubleshooting
+3. Verify all required files are in Space repo
+4. Ensure model repo is public and accessible
+

app.py

@@ -33,6 +33,7 @@ CAT_MAPPINGS_SAVE_PATH = os.path.join(MODEL_DIR, "multimodal_cat_mappings_GGG.js
 # API Configuration - AI API calls removed, using direct categorical inputs
 
 # Hugging Face Model Hub Configuration
+# Point to your model repository (not the Space)
 HF_MODEL_REPO = "nitish-spz/ABTestPredictor"  # Your model repository
 HF_MODEL_FILENAME = "multimodal_gated_model_2.7_GGG.pth"
 HF_MAPPINGS_FILENAME = "multimodal_cat_mappings_GGG.json"

confidence_scores.js

@@ -1,583 +0,0 @@
-/**
- * Confidence scores for Industry + Page Type combinations
- * Generated from holdout_set_statistics.csv (grouping_level = 2)
- * 
- * Key format: "Industry|Page Type"
- * Much higher sample counts and more reliable than 5-feature combinations
- */
-
-const confidenceMapping = {
-  "Automotive & Transportation|Awareness & Discovery": {
-    "accuracy": 0.6,
-    "count": 15,
-    "training_data_count": 135,
-    "correct_predictions": 9,
-    "actual_wins": 6,
-    "predicted_wins": 2
-  },
-  "Automotive & Transportation|Consideration & Evaluation": {
-    "accuracy": 0.667,
-    "count": 6,
-    "training_data_count": 54,
-    "correct_predictions": 4,
-    "actual_wins": 2,
-    "predicted_wins": 2
-  },
-  "Automotive & Transportation|Conversion": {
-    "accuracy": 1.0,
-    "count": 3,
-    "training_data_count": 27,
-    "correct_predictions": 3,
-    "actual_wins": 0,
-    "predicted_wins": 0
-  },
-  "Automotive & Transportation|Internal & Navigation": {
-    "accuracy": 0.571,
-    "count": 7,
-    "training_data_count": 63,
-    "correct_predictions": 4,
-    "actual_wins": 3,
-    "predicted_wins": 2
-  },
-  "B2B Services|Awareness & Discovery": {
-    "accuracy": 0.698,
-    "count": 483,
-    "training_data_count": 4347,
-    "correct_predictions": 337,
-    "actual_wins": 186,
-    "predicted_wins": 178
-  },
-  "B2B Services|Consideration & Evaluation": {
-    "accuracy": 0.657,
-    "count": 175,
-    "training_data_count": 1575,
-    "correct_predictions": 115,
-    "actual_wins": 82,
-    "predicted_wins": 78
-  },
-  "B2B Services|Conversion": {
-    "accuracy": 0.604,
-    "count": 53,
-    "training_data_count": 477,
-    "correct_predictions": 32,
-    "actual_wins": 26,
-    "predicted_wins": 23
-  },
-  "B2B Services|Internal & Navigation": {
-    "accuracy": 0.719,
-    "count": 139,
-    "training_data_count": 1251,
-    "correct_predictions": 100,
-    "actual_wins": 58,
-    "predicted_wins": 43
-  },
-  "B2B Services|Post-Conversion & Other": {
-    "accuracy": 0.571,
-    "count": 14,
-    "training_data_count": 126,
-    "correct_predictions": 8,
-    "actual_wins": 4,
-    "predicted_wins": 8
-  },
-  "B2B Software & Tech|Awareness & Discovery": {
-    "accuracy": 0.661,
-    "count": 1626,
-    "training_data_count": 14634,
-    "correct_predictions": 1074,
-    "actual_wins": 667,
-    "predicted_wins": 625
-  },
-  "B2B Software & Tech|Consideration & Evaluation": {
-    "accuracy": 0.617,
-    "count": 1046,
-    "training_data_count": 9414,
-    "correct_predictions": 645,
-    "actual_wins": 432,
-    "predicted_wins": 397
-  },
-  "B2B Software & Tech|Conversion": {
-    "accuracy": 0.647,
-    "count": 184,
-    "training_data_count": 1656,
-    "correct_predictions": 119,
-    "actual_wins": 71,
-    "predicted_wins": 74
-  },
-  "B2B Software & Tech|Internal & Navigation": {
-    "accuracy": 0.715,
-    "count": 376,
-    "training_data_count": 3384,
-    "correct_predictions": 269,
-    "actual_wins": 138,
-    "predicted_wins": 117
-  },
-  "B2B Software & Tech|Post-Conversion & Other": {
-    "accuracy": 0.78,
-    "count": 41,
-    "training_data_count": 369,
-    "correct_predictions": 32,
-    "actual_wins": 15,
-    "predicted_wins": 18
-  },
-  "Consumer Services|Awareness & Discovery": {
-    "accuracy": 0.723,
-    "count": 238,
-    "training_data_count": 2142,
-    "correct_predictions": 172,
-    "actual_wins": 97,
-    "predicted_wins": 85
-  },
-  "Consumer Services|Consideration & Evaluation": {
-    "accuracy": 0.592,
-    "count": 103,
-    "training_data_count": 927,
-    "correct_predictions": 61,
-    "actual_wins": 49,
-    "predicted_wins": 41
-  },
-  "Consumer Services|Conversion": {
-    "accuracy": 0.643,
-    "count": 42,
-    "training_data_count": 378,
-    "correct_predictions": 27,
-    "actual_wins": 12,
-    "predicted_wins": 13
-  },
-  "Consumer Services|Internal & Navigation": {
-    "accuracy": 0.607,
-    "count": 56,
-    "training_data_count": 504,
-    "correct_predictions": 34,
-    "actual_wins": 32,
-    "predicted_wins": 22
-  },
-  "Consumer Services|Post-Conversion & Other": {
-    "accuracy": 0.5,
-    "count": 2,
-    "training_data_count": 18,
-    "correct_predictions": 1,
-    "actual_wins": 1,
-    "predicted_wins": 0
-  },
-  "Consumer Software & Apps|Awareness & Discovery": {
-    "accuracy": 0.682,
-    "count": 22,
-    "training_data_count": 198,
-    "correct_predictions": 15,
-    "actual_wins": 5,
-    "predicted_wins": 8
-  },
-  "Consumer Software & Apps|Consideration & Evaluation": {
-    "accuracy": 0.9,
-    "count": 10,
-    "training_data_count": 90,
-    "correct_predictions": 9,
-    "actual_wins": 6,
-    "predicted_wins": 5
-  },
-  "Consumer Software & Apps|Conversion": {
-    "accuracy": 0.667,
-    "count": 15,
-    "training_data_count": 135,
-    "correct_predictions": 10,
-    "actual_wins": 5,
-    "predicted_wins": 6
-  },
-  "Consumer Software & Apps|Internal & Navigation": {
-    "accuracy": 0.2,
-    "count": 5,
-    "training_data_count": 45,
-    "correct_predictions": 1,
-    "actual_wins": 3,
-    "predicted_wins": 3
-  },
-  "Consumer Software & Apps|Post-Conversion & Other": {
-    "accuracy": 0.0,
-    "count": 1,
-    "training_data_count": 9,
-    "correct_predictions": 0,
-    "actual_wins": 1,
-    "predicted_wins": 0
-  },
-  "Education|Awareness & Discovery": {
-    "accuracy": 0.589,
-    "count": 409,
-    "training_data_count": 3681,
-    "correct_predictions": 241,
-    "actual_wins": 180,
-    "predicted_wins": 170
-  },
-  "Education|Consideration & Evaluation": {
-    "accuracy": 0.645,
-    "count": 183,
-    "training_data_count": 1647,
-    "correct_predictions": 118,
-    "actual_wins": 72,
-    "predicted_wins": 77
-  },
-  "Education|Conversion": {
-    "accuracy": 0.605,
-    "count": 43,
-    "training_data_count": 387,
-    "correct_predictions": 26,
-    "actual_wins": 16,
-    "predicted_wins": 17
-  },
-  "Education|Internal & Navigation": {
-    "accuracy": 0.661,
-    "count": 177,
-    "training_data_count": 1593,
-    "correct_predictions": 117,
-    "actual_wins": 70,
-    "predicted_wins": 62
-  },
-  "Education|Post-Conversion & Other": {
-    "accuracy": 0.308,
-    "count": 13,
-    "training_data_count": 117,
-    "correct_predictions": 4,
-    "actual_wins": 9,
-    "predicted_wins": 8
-  },
-  "Finance, Insurance & Real Estate|Awareness & Discovery": {
-    "accuracy": 0.662,
-    "count": 417,
-    "training_data_count": 3753,
-    "correct_predictions": 276,
-    "actual_wins": 172,
-    "predicted_wins": 147
-  },
-  "Finance, Insurance & Real Estate|Consideration & Evaluation": {
-    "accuracy": 0.596,
-    "count": 193,
-    "training_data_count": 1737,
-    "correct_predictions": 115,
-    "actual_wins": 82,
-    "predicted_wins": 78
-  },
-  "Finance, Insurance & Real Estate|Conversion": {
-    "accuracy": 0.615,
-    "count": 52,
-    "training_data_count": 468,
-    "correct_predictions": 32,
-    "actual_wins": 26,
-    "predicted_wins": 22
-  },
-  "Finance, Insurance & Real Estate|Internal & Navigation": {
-    "accuracy": 0.678,
-    "count": 177,
-    "training_data_count": 1593,
-    "correct_predictions": 120,
-    "actual_wins": 65,
-    "predicted_wins": 60
-  },
-  "Finance, Insurance & Real Estate|Post-Conversion & Other": {
-    "accuracy": 0.545,
-    "count": 22,
-    "training_data_count": 198,
-    "correct_predictions": 12,
-    "actual_wins": 13,
-    "predicted_wins": 7
-  },
-  "Food, Hospitality & Travel|Awareness & Discovery": {
-    "accuracy": 0.676,
-    "count": 293,
-    "training_data_count": 2637,
-    "correct_predictions": 198,
-    "actual_wins": 141,
-    "predicted_wins": 136
-  },
-  "Food, Hospitality & Travel|Consideration & Evaluation": {
-    "accuracy": 0.642,
-    "count": 159,
-    "training_data_count": 1431,
-    "correct_predictions": 102,
-    "actual_wins": 70,
-    "predicted_wins": 59
-  },
-  "Food, Hospitality & Travel|Conversion": {
-    "accuracy": 0.6,
-    "count": 60,
-    "training_data_count": 540,
-    "correct_predictions": 36,
-    "actual_wins": 31,
-    "predicted_wins": 27
-  },
-  "Food, Hospitality & Travel|Internal & Navigation": {
-    "accuracy": 0.63,
-    "count": 73,
-    "training_data_count": 657,
-    "correct_predictions": 46,
-    "actual_wins": 32,
-    "predicted_wins": 27
-  },
-  "Food, Hospitality & Travel|Post-Conversion & Other": {
-    "accuracy": 0.286,
-    "count": 7,
-    "training_data_count": 63,
-    "correct_predictions": 2,
-    "actual_wins": 2,
-    "predicted_wins": 5
-  },
-  "Health & Wellness|Awareness & Discovery": {
-    "accuracy": 0.631,
-    "count": 643,
-    "training_data_count": 5787,
-    "correct_predictions": 406,
-    "actual_wins": 262,
-    "predicted_wins": 249
-  },
-  "Health & Wellness|Consideration & Evaluation": {
-    "accuracy": 0.663,
-    "count": 389,
-    "training_data_count": 3501,
-    "correct_predictions": 258,
-    "actual_wins": 175,
-    "predicted_wins": 156
-  },
-  "Health & Wellness|Conversion": {
-    "accuracy": 0.632,
-    "count": 106,
-    "training_data_count": 954,
-    "correct_predictions": 67,
-    "actual_wins": 42,
-    "predicted_wins": 53
-  },
-  "Health & Wellness|Internal & Navigation": {
-    "accuracy": 0.654,
-    "count": 156,
-    "training_data_count": 1404,
-    "correct_predictions": 102,
-    "actual_wins": 72,
-    "predicted_wins": 58
-  },
-  "Health & Wellness|Post-Conversion & Other": {
-    "accuracy": 0.667,
-    "count": 12,
-    "training_data_count": 108,
-    "correct_predictions": 8,
-    "actual_wins": 5,
-    "predicted_wins": 3
-  },
-  "Industrial & Manufacturing|Awareness & Discovery": {
-    "accuracy": 0.573,
-    "count": 171,
-    "training_data_count": 1539,
-    "correct_predictions": 98,
-    "actual_wins": 75,
-    "predicted_wins": 82
-  },
-  "Industrial & Manufacturing|Consideration & Evaluation": {
-    "accuracy": 0.677,
-    "count": 93,
-    "training_data_count": 837,
-    "correct_predictions": 63,
-    "actual_wins": 34,
-    "predicted_wins": 32
-  },
-  "Industrial & Manufacturing|Conversion": {
-    "accuracy": 0.778,
-    "count": 18,
-    "training_data_count": 162,
-    "correct_predictions": 14,
-    "actual_wins": 6,
-    "predicted_wins": 10
-  },
-  "Industrial & Manufacturing|Internal & Navigation": {
-    "accuracy": 0.776,
-    "count": 67,
-    "training_data_count": 603,
-    "correct_predictions": 52,
-    "actual_wins": 25,
-    "predicted_wins": 28
-  },
-  "Industrial & Manufacturing|Post-Conversion & Other": {
-    "accuracy": 0.833,
-    "count": 6,
-    "training_data_count": 54,
-    "correct_predictions": 5,
-    "actual_wins": 4,
-    "predicted_wins": 5
-  },
-  "Media & Entertainment|Awareness & Discovery": {
-    "accuracy": 0.701,
-    "count": 251,
-    "training_data_count": 2259,
-    "correct_predictions": 176,
-    "actual_wins": 99,
-    "predicted_wins": 106
-  },
-  "Media & Entertainment|Consideration & Evaluation": {
-    "accuracy": 0.663,
-    "count": 95,
-    "training_data_count": 855,
-    "correct_predictions": 63,
-    "actual_wins": 28,
-    "predicted_wins": 32
-  },
-  "Media & Entertainment|Conversion": {
-    "accuracy": 0.792,
-    "count": 24,
-    "training_data_count": 216,
-    "correct_predictions": 19,
-    "actual_wins": 10,
-    "predicted_wins": 11
-  },
-  "Media & Entertainment|Internal & Navigation": {
-    "accuracy": 0.676,
-    "count": 68,
-    "training_data_count": 612,
-    "correct_predictions": 46,
-    "actual_wins": 24,
-    "predicted_wins": 18
-  },
-  "Media & Entertainment|Post-Conversion & Other": {
-    "accuracy": 0.6,
-    "count": 5,
-    "training_data_count": 45,
-    "correct_predictions": 3,
-    "actual_wins": 1,
-    "predicted_wins": 1
-  },
-  "Non-Profit & Government|Awareness & Discovery": {
-    "accuracy": 0.692,
-    "count": 107,
-    "training_data_count": 963,
-    "correct_predictions": 74,
-    "actual_wins": 36,
-    "predicted_wins": 37
-  },
-  "Non-Profit & Government|Consideration & Evaluation": {
-    "accuracy": 0.531,
-    "count": 32,
-    "training_data_count": 288,
-    "correct_predictions": 17,
-    "actual_wins": 12,
-    "predicted_wins": 11
-  },
-  "Non-Profit & Government|Conversion": {
-    "accuracy": 0.707,
-    "count": 92,
-    "training_data_count": 828,
-    "correct_predictions": 65,
-    "actual_wins": 29,
-    "predicted_wins": 22
-  },
-  "Non-Profit & Government|Internal & Navigation": {
-    "accuracy": 0.64,
-    "count": 50,
-    "training_data_count": 450,
-    "correct_predictions": 32,
-    "actual_wins": 23,
-    "predicted_wins": 21
-  },
-  "Non-Profit & Government|Post-Conversion & Other": {
-    "accuracy": 0.909,
-    "count": 11,
-    "training_data_count": 99,
-    "correct_predictions": 10,
-    "actual_wins": 4,
-    "predicted_wins": 3
-  },
-  "Other|Awareness & Discovery": {
-    "accuracy": 0.755,
-    "count": 53,
-    "training_data_count": 477,
-    "correct_predictions": 40,
-    "actual_wins": 24,
-    "predicted_wins": 21
-  },
-  "Other|Consideration & Evaluation": {
-    "accuracy": 0.385,
-    "count": 26,
-    "training_data_count": 234,
-    "correct_predictions": 10,
-    "actual_wins": 13,
-    "predicted_wins": 9
-  },
-  "Other|Conversion": {
-    "accuracy": 0.75,
-    "count": 4,
-    "training_data_count": 36,
-    "correct_predictions": 3,
-    "actual_wins": 2,
-    "predicted_wins": 3
-  },
-  "Other|Internal & Navigation": {
-    "accuracy": 0.4,
-    "count": 10,
-    "training_data_count": 90,
-    "correct_predictions": 4,
-    "actual_wins": 5,
-    "predicted_wins": 1
-  },
-  "Other|Post-Conversion & Other": {
-    "accuracy": 1.0,
-    "count": 1,
-    "training_data_count": 9,
-    "correct_predictions": 1,
-    "actual_wins": 1,
-    "predicted_wins": 1
-  },
-  "Retail & E-commerce|Awareness & Discovery": {
-    "accuracy": 0.645,
-    "count": 619,
-    "training_data_count": 5571,
-    "correct_predictions": 399,
-    "actual_wins": 309,
-    "predicted_wins": 239
-  },
-  "Retail & E-commerce|Consideration & Evaluation": {
-    "accuracy": 0.638,
-    "count": 718,
-    "training_data_count": 6462,
-    "correct_predictions": 458,
-    "actual_wins": 345,
-    "predicted_wins": 309
-  },
-  "Retail & E-commerce|Conversion": {
-    "accuracy": 0.661,
-    "count": 112,
-    "training_data_count": 1008,
-    "correct_predictions": 74,
-    "actual_wins": 58,
-    "predicted_wins": 60
-  },
-  "Retail & E-commerce|Internal & Navigation": {
-    "accuracy": 0.636,
-    "count": 154,
-    "training_data_count": 1386,
-    "correct_predictions": 98,
-    "actual_wins": 67,
-    "predicted_wins": 59
-  },
-  "Retail & E-commerce|Post-Conversion & Other": {
-    "accuracy": 1.0,
-    "count": 5,
-    "training_data_count": 45,
-    "correct_predictions": 5,
-    "actual_wins": 2,
-    "predicted_wins": 2
-  }
-};
-
-// Helper function to get confidence data for Industry + Page Type combination
-function getConfidenceScore(industry, pageType) {
-    const key = industry + '|' + pageType;
-    return confidenceMapping[key] || {
-        accuracy: 0.5,  // Default fallback
-        count: 0,
-        training_data_count: 0,
-        correct_predictions: 0,
-        actual_wins: 0,
-        predicted_wins: 0
-    };
-}
-
-// Export for use in other files
-if (typeof module !== 'undefined' && module.exports) {
-    module.exports = {
-        confidenceMapping,
-        getConfidenceScore
-    };
-}

frontend.html

@@ -1,576 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>A/B Test Predictor - Local Frontend</title>
-    <style>
-        * {
-            margin: 0;
-            padding: 0;
-            box-sizing: border-box;
-        }
-
-        body {
-            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif;
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            min-height: 100vh;
-            padding: 20px;
-            display: flex;
-            justify-content: center;
-            align-items: center;
-        }
-
-        .container {
-            background: white;
-            border-radius: 20px;
-            box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3);
-            max-width: 1200px;
-            width: 100%;
-            padding: 40px;
-        }
-
-        h1 {
-            text-align: center;
-            color: #333;
-            margin-bottom: 10px;
-            font-size: 2.5em;
-        }
-
-        .subtitle {
-            text-align: center;
-            color: #666;
-            margin-bottom: 40px;
-            font-size: 1.1em;
-        }
-
-        .upload-section {
-            display: grid;
-            grid-template-columns: 1fr 1fr;
-            gap: 30px;
-            margin-bottom: 30px;
-        }
-
-        .upload-box {
-            border: 3px dashed #667eea;
-            border-radius: 15px;
-            padding: 30px;
-            text-align: center;
-            transition: all 0.3s ease;
-            background: #f8f9ff;
-            cursor: pointer;
-            position: relative;
-            overflow: hidden;
-        }
-
-        .upload-box:hover {
-            border-color: #764ba2;
-            background: #f0f1ff;
-            transform: translateY(-5px);
-            box-shadow: 0 10px 20px rgba(102, 126, 234, 0.2);
-        }
-
-        .upload-box.has-image {
-            border-color: #10b981;
-            background: #f0fdf4;
-        }
-
-        .upload-box h3 {
-            color: #667eea;
-            margin-bottom: 15px;
-            font-size: 1.5em;
-        }
-
-        .upload-icon {
-            font-size: 3em;
-            margin-bottom: 10px;
-            color: #667eea;
-        }
-
-        .upload-text {
-            color: #666;
-            font-size: 0.95em;
-            margin-bottom: 15px;
-        }
-
-        .file-input {
-            display: none;
-        }
-
-        .preview-image {
-            max-width: 100%;
-            max-height: 250px;
-            border-radius: 10px;
-            margin-top: 15px;
-            display: none;
-            box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1);
-        }
-
-        .preview-image.show {
-            display: block;
-        }
-
-        .file-name {
-            color: #10b981;
-            font-weight: 600;
-            margin-top: 10px;
-            font-size: 0.9em;
-        }
-
-        .predict-button {
-            width: 100%;
-            padding: 18px;
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            color: white;
-            border: none;
-            border-radius: 12px;
-            font-size: 1.2em;
-            font-weight: 600;
-            cursor: pointer;
-            transition: all 0.3s ease;
-            margin-bottom: 30px;
-            box-shadow: 0 5px 15px rgba(102, 126, 234, 0.3);
-        }
-
-        .predict-button:hover:not(:disabled) {
-            transform: translateY(-2px);
-            box-shadow: 0 8px 20px rgba(102, 126, 234, 0.4);
-        }
-
-        .predict-button:disabled {
-            background: #ccc;
-            cursor: not-allowed;
-            box-shadow: none;
-        }
-
-        .loading {
-            display: none;
-            text-align: center;
-            margin: 30px 0;
-        }
-
-        .loading.show {
-            display: block;
-        }
-
-        .spinner {
-            border: 4px solid #f3f3f3;
-            border-top: 4px solid #667eea;
-            border-radius: 50%;
-            width: 50px;
-            height: 50px;
-            animation: spin 1s linear infinite;
-            margin: 0 auto 15px;
-        }
-
-        @keyframes spin {
-            0% { transform: rotate(0deg); }
-            100% { transform: rotate(360deg); }
-        }
-
-        .loading-text {
-            color: #667eea;
-            font-weight: 600;
-            margin-bottom: 10px;
-        }
-
-        .progress {
-            color: #666;
-            font-size: 0.9em;
-        }
-
-        .results {
-            display: none;
-            background: #f8f9ff;
-            border-radius: 15px;
-            padding: 30px;
-            margin-top: 30px;
-        }
-
-        .results.show {
-            display: block;
-            animation: fadeIn 0.5s ease;
-        }
-
-        @keyframes fadeIn {
-            from { opacity: 0; transform: translateY(20px); }
-            to { opacity: 1; transform: translateY(0); }
-        }
-
-        .results h2 {
-            color: #333;
-            margin-bottom: 20px;
-            font-size: 1.8em;
-        }
-
-        .json-display {
-            background: #1e293b;
-            color: #e2e8f0;
-            padding: 20px;
-            border-radius: 10px;
-            font-family: 'Courier New', monospace;
-            font-size: 0.9em;
-            overflow-x: auto;
-            white-space: pre-wrap;
-            word-wrap: break-word;
-            line-height: 1.6;
-            box-shadow: inset 0 2px 10px rgba(0, 0, 0, 0.2);
-        }
-
-        .error {
-            background: #fee;
-            border: 2px solid #fcc;
-            border-radius: 10px;
-            padding: 20px;
-            margin-top: 20px;
-            color: #c33;
-            display: none;
-        }
-
-        .error.show {
-            display: block;
-        }
-
-        .error h3 {
-            margin-bottom: 10px;
-        }
-
-        @media (max-width: 768px) {
-            .upload-section {
-                grid-template-columns: 1fr;
-            }
-
-            h1 {
-                font-size: 1.8em;
-            }
-
-            .container {
-                padding: 20px;
-            }
-        }
-    </style>
-</head>
-<body>
-    <div class="container">
-        <h1>🚀 A/B Test Predictor</h1>
-        <p class="subtitle">Upload your control and variant images to get AI-powered predictions</p>
-
-        <div class="upload-section">
-            <div class="upload-box" id="controlBox" onclick="document.getElementById('controlInput').click()">
-                <div class="upload-icon">📊</div>
-                <h3>Control Image</h3>
-                <p class="upload-text">Click to upload or drag & drop</p>
-                <input type="file" id="controlInput" class="file-input" accept="image/*">
-                <img id="controlPreview" class="preview-image" alt="Control preview">
-                <div id="controlFileName" class="file-name"></div>
-            </div>
-
-            <div class="upload-box" id="variantBox" onclick="document.getElementById('variantInput').click()">
-                <div class="upload-icon">📈</div>
-                <h3>Variant Image</h3>
-                <p class="upload-text">Click to upload or drag & drop</p>
-                <input type="file" id="variantInput" class="file-input" accept="image/*">
-                <img id="variantPreview" class="preview-image" alt="Variant preview">
-                <div id="variantFileName" class="file-name"></div>
-            </div>
-        </div>
-
-        <button id="predictButton" class="predict-button" disabled>
-            🤖 Analyze & Predict Winner
-        </button>
-
-        <div id="loading" class="loading">
-            <div class="spinner"></div>
-            <p class="loading-text">🧠 AI is analyzing your images...</p>
-            <p class="progress" id="progressText">Uploading files...</p>
-        </div>
-
-        <div id="error" class="error">
-            <h3>❌ Error</h3>
-            <p id="errorMessage"></p>
-        </div>
-
-        <div id="results" class="results">
-            <h2>📊 Prediction Results</h2>
-            <div id="jsonDisplay" class="json-display"></div>
-        </div>
-    </div>
-
-    <script>
-        const API_URL = 'https://nitish-spz-abtestpredictorv2.hf.space';
-        
-        let controlFile = null;
-        let variantFile = null;
-
-        // Setup file input handlers
-        const controlInput = document.getElementById('controlInput');
-        const variantInput = document.getElementById('variantInput');
-        const controlBox = document.getElementById('controlBox');
-        const variantBox = document.getElementById('variantBox');
-        const controlPreview = document.getElementById('controlPreview');
-        const variantPreview = document.getElementById('variantPreview');
-        const controlFileName = document.getElementById('controlFileName');
-        const variantFileName = document.getElementById('variantFileName');
-        const predictButton = document.getElementById('predictButton');
-        const progressText = document.getElementById('progressText');
-
-        // Handle file selection
-        controlInput.addEventListener('change', (e) => handleFileSelect(e, 'control'));
-        variantInput.addEventListener('change', (e) => handleFileSelect(e, 'variant'));
-
-        // Drag and drop handlers
-        [controlBox, variantBox].forEach(box => {
-            box.addEventListener('dragover', (e) => {
-                e.preventDefault();
-                box.style.borderColor = '#764ba2';
-            });
-
-            box.addEventListener('dragleave', (e) => {
-                e.preventDefault();
-                box.style.borderColor = '#667eea';
-            });
-
-            box.addEventListener('drop', (e) => {
-                e.preventDefault();
-                e.stopPropagation();
-                box.style.borderColor = '#667eea';
-                
-                const type = box.id === 'controlBox' ? 'control' : 'variant';
-                const files = e.dataTransfer.files;
-                
-                if (files.length > 0 && files[0].type.startsWith('image/')) {
-                    const input = type === 'control' ? controlInput : variantInput;
-                    input.files = files;
-                    handleFileSelect({ target: input }, type);
-                }
-            });
-        });
-
-        function handleFileSelect(event, type) {
-            const file = event.target.files[0];
-            if (!file) return;
-
-            if (type === 'control') {
-                controlFile = file;
-                displayPreview(file, controlPreview, controlFileName, controlBox);
-            } else {
-                variantFile = file;
-                displayPreview(file, variantPreview, variantFileName, variantBox);
-            }
-
-            // Enable predict button if both files are selected
-            if (controlFile && variantFile) {
-                predictButton.disabled = false;
-            }
-        }
-
-        function displayPreview(file, previewImg, fileNameDiv, box) {
-            const reader = new FileReader();
-            reader.onload = (e) => {
-                previewImg.src = e.target.result;
-                previewImg.classList.add('show');
-                fileNameDiv.textContent = `✓ ${file.name}`;
-                box.classList.add('has-image');
-            };
-            reader.readAsDataURL(file);
-        }
-
-        // Handle prediction
-        predictButton.addEventListener('click', async () => {
-            if (!controlFile || !variantFile) {
-                showError('Please upload both control and variant images');
-                return;
-            }
-
-            // Hide previous results/errors
-            document.getElementById('results').classList.remove('show');
-            document.getElementById('error').classList.remove('show');
-            
-            // Show loading
-            document.getElementById('loading').classList.add('show');
-            predictButton.disabled = true;
-
-            try {
-                console.log('📤 Step 1: Uploading files to Gradio...');
-                progressText.textContent = 'Uploading files...';
-
-                // Upload files to Gradio
-                const controlPath = await uploadFileToGradio(controlFile);
-                const variantPath = await uploadFileToGradio(variantFile);
-                
-                console.log(`✅ Files uploaded: ${controlPath}, ${variantPath}`);
-                
-                // Create FileData objects
-                const controlFileData = {
-                    path: controlPath,
-                    url: `${API_URL}/file=${controlPath}`,
-                    orig_name: controlFile.name,
-                    size: controlFile.size,
-                    mime_type: controlFile.type,
-                    meta: { _type: "gradio.FileData" }
-                };
-
-                const variantFileData = {
-                    path: variantPath,
-                    url: `${API_URL}/file=${variantPath}`,
-                    orig_name: variantFile.name,
-                    size: variantFile.size,
-                    mime_type: variantFile.type,
-                    meta: { _type: "gradio.FileData" }
-                };
-
-                console.log('📤 Step 2: Sending prediction request...');
-                progressText.textContent = 'AI is analyzing images (20-40s)...';
-
-                // Make API call
-                const response = await fetch(`${API_URL}/call/predict_with_auto_categorization`, {
-                    method: 'POST',
-                    headers: { 'Content-Type': 'application/json' },
-                    body: JSON.stringify({
-                        data: [controlFileData, variantFileData]
-                    })
-                });
-
-                if (!response.ok) {
-                    throw new Error(`API error: ${response.status}`);
-                }
-
-                const result = await response.json();
-                console.log('✅ Request queued:', result);
-
-                if (!result.event_id) {
-                    throw new Error('No event_id received');
-                }
-
-                // Poll for results
-                progressText.textContent = 'Waiting for AI prediction...';
-                const finalResult = await pollForResult(result.event_id);
-                
-                console.log('✅ Final result:', finalResult);
-                displayResults(finalResult);
-
-            } catch (error) {
-                console.error('❌ Error:', error);
-                showError(`Failed to get prediction: ${error.message}`);
-            } finally {
-                document.getElementById('loading').classList.remove('show');
-                predictButton.disabled = false;
-            }
-        });
-
-        async function uploadFileToGradio(file) {
-            const formData = new FormData();
-            formData.append('files', file);
-            
-            const response = await fetch(`${API_URL}/upload`, {
-                method: 'POST',
-                body: formData
-            });
-            
-            if (!response.ok) {
-                throw new Error(`Upload failed: ${response.status}`);
-            }
-            
-            const result = await response.json();
-            
-            if (Array.isArray(result) && result.length > 0) {
-                return result[0];
-            }
-            throw new Error('Upload failed - no path returned');
-        }
-
-        async function pollForResult(eventId) {
-            const maxAttempts = 50;  // 50 attempts x 2 seconds = 100 seconds max
-            const pollInterval = 2000;
-            
-            for (let attempt = 0; attempt < maxAttempts; attempt++) {
-                progressText.textContent = `Polling for results (${attempt + 1}/${maxAttempts})...`;
-                console.log(`📊 Poll attempt ${attempt + 1}/${maxAttempts}`);
-                
-                try {
-                    const response = await fetch(`${API_URL}/call/predict_with_auto_categorization/${eventId}`);
-                    
-                    if (!response.ok) {
-                        await sleep(pollInterval);
-                        continue;
-                    }
-                    
-                    const text = await response.text();
-                    
-                    if (!text || text.trim() === '') {
-                        await sleep(pollInterval);
-                        continue;
-                    }
-                    
-                    // Parse SSE format
-                    const lines = text.split('\n');
-                    for (let i = 0; i < lines.length; i++) {
-                        if (lines[i].trim() === 'event: complete') {
-                            // Found complete event, get the data
-                            for (let j = i + 1; j < lines.length; j++) {
-                                const dataLine = lines[j].trim();
-                                if (dataLine.startsWith('data: ')) {
-                                    const jsonData = dataLine.substring(6);
-                                    const result = JSON.parse(jsonData);
-                                    return result;
-                                }
-                            }
-                        }
-                    }
-                    
-                    // Check for any data line that looks like a result
-                    for (const line of lines) {
-                        if (line.startsWith('data: ')) {
-                            try {
-                                const jsonData = line.substring(6);
-                                const parsed = JSON.parse(jsonData);
-                                
-                                if (parsed && typeof parsed === 'object' && !parsed.progress) {
-                                    // Looks like a result
-                                    if (parsed['🎯 Prediction Results'] || Array.isArray(parsed)) {
-                                        return parsed;
-                                    }
-                                }
-                            } catch (e) {
-                                // Not valid JSON, continue
-                            }
-                        }
-                    }
-                    
-                } catch (error) {
-                    console.error('Poll error:', error);
-                }
-                
-                await sleep(pollInterval);
-            }
-            
-            throw new Error('Polling timeout - no result after 100 seconds');
-        }
-
-        function sleep(ms) {
-            return new Promise(resolve => setTimeout(resolve, ms));
-        }
-
-        function displayResults(data) {
-            const resultsDiv = document.getElementById('results');
-            const jsonDisplay = document.getElementById('jsonDisplay');
-            
-            // Pretty print JSON
-            jsonDisplay.textContent = JSON.stringify(data, null, 2);
-            
-            resultsDiv.classList.add('show');
-        }
-
-        function showError(message) {
-            const errorDiv = document.getElementById('error');
-            const errorMessage = document.getElementById('errorMessage');
-            
-            errorMessage.textContent = message;
-            errorDiv.classList.add('show');
-        }
-
-        console.log('✅ Frontend loaded and ready!');
-    </script>
-</body>
-</html>
-

index_v2.html

@@ -1,437 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>A/B Test Predictor - Simple Version</title>
-    <style>
-        * {
-            margin: 0;
-            padding: 0;
-            box-sizing: border-box;
-        }
-
-        body {
-            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif;
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            min-height: 100vh;
-            padding: 20px;
-            display: flex;
-            justify-content: center;
-            align-items: center;
-        }
-
-        .container {
-            background: white;
-            border-radius: 20px;
-            box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3);
-            max-width: 1200px;
-            width: 100%;
-            padding: 40px;
-        }
-
-        h1 {
-            text-align: center;
-            color: #333;
-            margin-bottom: 10px;
-            font-size: 2.5em;
-        }
-
-        .subtitle {
-            text-align: center;
-            color: #666;
-            margin-bottom: 40px;
-            font-size: 1.1em;
-        }
-
-        .upload-section {
-            display: grid;
-            grid-template-columns: 1fr 1fr;
-            gap: 30px;
-            margin-bottom: 30px;
-        }
-
-        .upload-box {
-            border: 3px dashed #667eea;
-            border-radius: 15px;
-            padding: 30px;
-            text-align: center;
-            transition: all 0.3s ease;
-            background: #f8f9ff;
-            cursor: pointer;
-            position: relative;
-            overflow: hidden;
-        }
-
-        .upload-box:hover {
-            border-color: #764ba2;
-            background: #f0f1ff;
-            transform: translateY(-5px);
-            box-shadow: 0 10px 20px rgba(102, 126, 234, 0.2);
-        }
-
-        .upload-box.has-image {
-            border-color: #10b981;
-            background: #f0fdf4;
-        }
-
-        .upload-box h3 {
-            color: #667eea;
-            margin-bottom: 15px;
-            font-size: 1.5em;
-        }
-
-        .upload-icon {
-            font-size: 3em;
-            margin-bottom: 10px;
-            color: #667eea;
-        }
-
-        .upload-text {
-            color: #666;
-            font-size: 0.95em;
-            margin-bottom: 15px;
-        }
-
-        .file-input {
-            display: none;
-        }
-
-        .preview-image {
-            max-width: 100%;
-            max-height: 250px;
-            border-radius: 10px;
-            margin-top: 15px;
-            display: none;
-            box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1);
-        }
-
-        .preview-image.show {
-            display: block;
-        }
-
-        .file-name {
-            color: #10b981;
-            font-weight: 600;
-            margin-top: 10px;
-            font-size: 0.9em;
-        }
-
-        .predict-button {
-            width: 100%;
-            padding: 18px;
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            color: white;
-            border: none;
-            border-radius: 12px;
-            font-size: 1.2em;
-            font-weight: 600;
-            cursor: pointer;
-            transition: all 0.3s ease;
-            margin-bottom: 30px;
-            box-shadow: 0 5px 15px rgba(102, 126, 234, 0.3);
-        }
-
-        .predict-button:hover:not(:disabled) {
-            transform: translateY(-2px);
-            box-shadow: 0 8px 20px rgba(102, 126, 234, 0.4);
-        }
-
-        .predict-button:disabled {
-            background: #ccc;
-            cursor: not-allowed;
-            box-shadow: none;
-        }
-
-        .loading {
-            display: none;
-            text-align: center;
-            margin: 30px 0;
-        }
-
-        .loading.show {
-            display: block;
-        }
-
-        .spinner {
-            border: 4px solid #f3f3f3;
-            border-top: 4px solid #667eea;
-            border-radius: 50%;
-            width: 50px;
-            height: 50px;
-            animation: spin 1s linear infinite;
-            margin: 0 auto 15px;
-        }
-
-        @keyframes spin {
-            0% { transform: rotate(0deg); }
-            100% { transform: rotate(360deg); }
-        }
-
-        .loading-text {
-            color: #667eea;
-            font-weight: 600;
-        }
-
-        .results {
-            display: none;
-            background: #f8f9ff;
-            border-radius: 15px;
-            padding: 30px;
-            margin-top: 30px;
-        }
-
-        .results.show {
-            display: block;
-            animation: fadeIn 0.5s ease;
-        }
-
-        @keyframes fadeIn {
-            from { opacity: 0; transform: translateY(20px); }
-            to { opacity: 1; transform: translateY(0); }
-        }
-
-        .results h2 {
-            color: #333;
-            margin-bottom: 20px;
-            font-size: 1.8em;
-        }
-
-        .json-display {
-            background: #1e293b;
-            color: #e2e8f0;
-            padding: 20px;
-            border-radius: 10px;
-            font-family: 'Courier New', monospace;
-            font-size: 0.9em;
-            overflow-x: auto;
-            white-space: pre-wrap;
-            word-wrap: break-word;
-            line-height: 1.6;
-            box-shadow: inset 0 2px 10px rgba(0, 0, 0, 0.2);
-        }
-
-        .error {
-            background: #fee;
-            border: 2px solid #fcc;
-            border-radius: 10px;
-            padding: 20px;
-            margin-top: 20px;
-            color: #c33;
-            display: none;
-        }
-
-        .error.show {
-            display: block;
-        }
-
-        .error h3 {
-            margin-bottom: 10px;
-        }
-
-        @media (max-width: 768px) {
-            .upload-section {
-                grid-template-columns: 1fr;
-            }
-
-            h1 {
-                font-size: 1.8em;
-            }
-
-            .container {
-                padding: 20px;
-            }
-        }
-    </style>
-</head>
-<body>
-    <div class="container">
-        <h1>🚀 A/B Test Predictor</h1>
-        <p class="subtitle">Upload your control and variant images to get AI-powered predictions</p>
-
-        <div class="upload-section">
-            <div class="upload-box" id="controlBox" onclick="document.getElementById('controlInput').click()">
-                <div class="upload-icon">📊</div>
-                <h3>Control Image</h3>
-                <p class="upload-text">Click to upload or drag & drop</p>
-                <input type="file" id="controlInput" class="file-input" accept="image/*">
-                <img id="controlPreview" class="preview-image" alt="Control preview">
-                <div id="controlFileName" class="file-name"></div>
-            </div>
-
-            <div class="upload-box" id="variantBox" onclick="document.getElementById('variantInput').click()">
-                <div class="upload-icon">📈</div>
-                <h3>Variant Image</h3>
-                <p class="upload-text">Click to upload or drag & drop</p>
-                <input type="file" id="variantInput" class="file-input" accept="image/*">
-                <img id="variantPreview" class="preview-image" alt="Variant preview">
-                <div id="variantFileName" class="file-name"></div>
-            </div>
-        </div>
-
-        <button id="predictButton" class="predict-button" disabled>
-            🤖 Analyze & Predict Winner
-        </button>
-
-        <div id="loading" class="loading">
-            <div class="spinner"></div>
-            <p class="loading-text">🧠 AI is analyzing your images... This may take 20-40 seconds</p>
-        </div>
-
-        <div id="error" class="error">
-            <h3>❌ Error</h3>
-            <p id="errorMessage"></p>
-        </div>
-
-        <div id="results" class="results">
-            <h2>📊 Prediction Results</h2>
-            <div id="jsonDisplay" class="json-display"></div>
-        </div>
-    </div>
-
-    <!-- Import Gradio Client Library -->
-    <script type="module">
-        import { Client } from "https://cdn.jsdelivr.net/npm/@gradio/client@1.3.0/dist/index.min.js";
-
-        let controlFile = null;
-        let variantFile = null;
-
-        // Setup file input handlers
-        const controlInput = document.getElementById('controlInput');
-        const variantInput = document.getElementById('variantInput');
-        const controlBox = document.getElementById('controlBox');
-        const variantBox = document.getElementById('variantBox');
-        const controlPreview = document.getElementById('controlPreview');
-        const variantPreview = document.getElementById('variantPreview');
-        const controlFileName = document.getElementById('controlFileName');
-        const variantFileName = document.getElementById('variantFileName');
-        const predictButton = document.getElementById('predictButton');
-
-        // Handle file selection
-        controlInput.addEventListener('change', (e) => handleFileSelect(e, 'control'));
-        variantInput.addEventListener('change', (e) => handleFileSelect(e, 'variant'));
-
-        // Drag and drop handlers
-        [controlBox, variantBox].forEach(box => {
-            box.addEventListener('dragover', (e) => {
-                e.preventDefault();
-                box.style.borderColor = '#764ba2';
-            });
-
-            box.addEventListener('dragleave', (e) => {
-                e.preventDefault();
-                box.style.borderColor = '#667eea';
-            });
-
-            box.addEventListener('drop', (e) => {
-                e.preventDefault();
-                e.stopPropagation();
-                box.style.borderColor = '#667eea';
-                
-                const type = box.id === 'controlBox' ? 'control' : 'variant';
-                const files = e.dataTransfer.files;
-                
-                if (files.length > 0 && files[0].type.startsWith('image/')) {
-                    const input = type === 'control' ? controlInput : variantInput;
-                    input.files = files;
-                    handleFileSelect({ target: input }, type);
-                }
-            });
-        });
-
-        function handleFileSelect(event, type) {
-            const file = event.target.files[0];
-            if (!file) return;
-
-            if (type === 'control') {
-                controlFile = file;
-                displayPreview(file, controlPreview, controlFileName, controlBox);
-            } else {
-                variantFile = file;
-                displayPreview(file, variantPreview, variantFileName, variantBox);
-            }
-
-            // Enable predict button if both files are selected
-            if (controlFile && variantFile) {
-                predictButton.disabled = false;
-            }
-        }
-
-        function displayPreview(file, previewImg, fileNameDiv, box) {
-            const reader = new FileReader();
-            reader.onload = (e) => {
-                previewImg.src = e.target.result;
-                previewImg.classList.add('show');
-                fileNameDiv.textContent = `✓ ${file.name}`;
-                box.classList.add('has-image');
-            };
-            reader.readAsDataURL(file);
-        }
-
-        // Handle prediction using Gradio Client
-        predictButton.addEventListener('click', async () => {
-            if (!controlFile || !variantFile) {
-                showError('Please upload both control and variant images');
-                return;
-            }
-
-            // Hide previous results/errors
-            document.getElementById('results').classList.remove('show');
-            document.getElementById('error').classList.remove('show');
-            
-            // Show loading
-            document.getElementById('loading').classList.add('show');
-            predictButton.disabled = true;
-
-            try {
-                console.log('🔗 Connecting to Gradio client...');
-                
-                // Connect to the Hugging Face Space
-                const client = await Client.connect("nitish-spz/ABTestPredictorV2");
-                
-                console.log('✅ Connected! Uploading files and making prediction...');
-                
-                // Make prediction using the official Gradio client
-                // It automatically handles file uploads
-                const result = await client.predict("/predict_with_auto_categorization", {
-                    control_image: controlFile,
-                    variant_image: variantFile,
-                });
-
-                console.log('✅ Result received:', result);
-
-                // Display results
-                displayResults(result.data);
-
-            } catch (error) {
-                console.error('❌ Error:', error);
-                showError(`Failed to get prediction: ${error.message}`);
-            } finally {
-                document.getElementById('loading').classList.remove('show');
-                predictButton.disabled = false;
-            }
-        });
-
-        function displayResults(data) {
-            const resultsDiv = document.getElementById('results');
-            const jsonDisplay = document.getElementById('jsonDisplay');
-            
-            // Pretty print JSON
-            jsonDisplay.textContent = JSON.stringify(data, null, 2);
-            
-            resultsDiv.classList.add('show');
-        }
-
-        function showError(message) {
-            const errorDiv = document.getElementById('error');
-            const errorMessage = document.getElementById('errorMessage');
-            
-            errorMessage.textContent = message;
-            errorDiv.classList.add('show');
-        }
-
-        console.log('✅ Gradio Client loaded and ready!');
-    </script>
-</body>
-</html>
-

metadata.js

@@ -1,604 +0,0 @@
-/**
- * This file contains the metadata used for filtering in the search application.
- * It is auto-generated by a Python script. Do not edit this file manually.
- */
-
-const pattern = [
-"Access Wall",
- "Accordion",
- "Add / Change Delay",
- "Add / Change Emphasis",
- "Add Background Image",
- "Add CTA",
- "Add Details",
- "Amazon A/B Testing",
- "Anchoring",
- "Animated CTA",
- "Animation",
- "Annotate UI",
- "Attention Director",
- "Audio Recording",
- "Autofill",
- "Autoplay Video",
- "Autoplay Video Without Sound",
- "Baseline - Other",
- "Baseline Hero",
- "Before and After",
- "Benefit Button",
- "Bestseller / Featured Products",
- "Big Claim Headline",
- "Bigger Product Images",
- "Blog / Content Summary",
- "Blurred Content",
- "Bold-face Copy",
- "Bottom Banner to Top Banner",
- "Bottom Corner Pop-up",
- "Breadcrumbs",
- "Bullet Points",
- "Bullet Points - Change Inclusions",
- "Bullet Points - Emphasize Inclusions",
- "Bullet Points - Inclusions",
- "Bulletize Copy",
- "Button",
- "Button Positioning",
- "Button to Radio Button",
- "Buttonized CTA",
- "Buttons - Image",
- "CTA Activation",
- "CTA Color Change",
- "CTA Copy Change",
- "CTA Positioning",
- "CTA Section",
- "CTA Size",
- "Calculator",
- "Calendar",
- "Calendar Flexibility Button",
- "Cart Contents Section",
- "Case Studies",
- "Change / Add Link to Product Pages",
- "Change / Add Product/Plan Name",
- "Change Advertisements / Ads",
- "Change Attention Director",
- "Change Background Image",
- "Change CTA Link",
- "Change CTA Shape",
- "Change CTA to Hyperlink",
- "Change Checkbox/Tickbox to Buttons/Dropdown",
- "Change Copy Order",
- "Change Copy on Image / Video Overlay",
- "Change Default Currency",
- "Change Default Filter/Sort View",
- "Change Displayed / Listed Product / Category",
- "Change Donation Amount",
- "Change Error Message",
- "Change FAQs",
- "Change Features",
- "Change Field Type",
- "Change Font",
- "Change Font Color",
- "Change Form Field Label",
- "Change Form Fields",
- "Change GIF / Video / Animation",
- "Change Hero Image",
- "Change How It Works",
- "Change Hyperlink Copy",
- "Change Icon",
- "Change Icon to Copy",
- "Change Image",
- "Change Image / Video Size",
- "Change Image Order",
- "Change Inclusions",
- "Change Integrations",
- "Change Link to Internal Pages",
- "Change Listing Cards Layout / Design",
- "Change Logo",
- "Change Logos in Social Proof",
- "Change Matrix Layout / Design",
- "Change Menu",
- "Change Modal",
- "Change Payment Options",
- "Change Price",
- "Change Pricing Card Layout / Design",
- "Change Product Image",
- "Change Progress Timeline",
- "Change Promotion",
- "Change Qualifying Questions",
- "Change Real People",
- "Change Recommended Tags",
- "Change Resources / Articles",
- "Change Reviews Summary",
- "Change Section",
- "Change Section Order",
- "Change Selection Buttons to Field",
- "Change Statistics",
- "Change Subscription / Trial / Promotion Duration",
- "Change Tag Design/Copy",
- "Change Testimonial",
- "Change Trust Badges",
- "Change USPs*",
- "Change Upsell Product",
- "Change Video Thumbnail Image",
- "Chatbot",
- "Checkbox / Tickbox",
- "Clearbit Form",
- "Clickable Icon / Image",
- "Clickable Section",
- "Color Change",
- "Company Info",
- "Comparison Matrix",
- "Competitor Pricing",
- "Concise Headline",
- "Condensed List",
- "Contact Number",
- "Contact Us Section",
- "Content to Tabs",
- "Copy - Add",
- "Copy Center-Aligned",
- "Copy Change",
- "Copy Positioning",
- "Countdown Timer",
- "Cross Sell",
- "Crossed-out Features",
- "Currency Font Size",
- "Currency to Percentage Savings",
- "Database Number",
- "Decrease Number of Slides",
- "Demo/Trial Duration",
- "Disclaimer",
- "Double Column Form",
- "Download Method",
- "Dropdown",
- "Dual CTA",
- "Dummy Category Name",
- "Dummy Form Field",
- "Dynamic/Animated Headline",
- "Easing Product/Plan Selection",
- "Email Plus CTA",
- "Email Plus CTA in Nav",
- "Email Subscription",
- "Emphasize / Highlight Urgency",
- "Emphasize Buttons",
- "Emphasize CTA",
- "Emphasize Cross Sell",
- "Emphasize Form",
- "Emphasize Guarantee",
- "Emphasize Inclusions",
- "Emphasize Interface Image",
- "Emphasize Options",
- "Emphasize Payment Options",
- "Emphasize Pricing / Price",
- "Emphasize Search Bar",
- "Emphasize Social Proof",
- "Emphasize Testimonials",
- "Exit Modal",
- "Explainer Microcopy",
- "External Ad",
- "FAQs",
- "Fear of Missing Out",
- "Features",
- "Features - Additional",
- "Features - Bento-Style",
- "Features - Z-Style",
- "Features Accordion",
- "Features Grid",
- "Filters",
- "Filters - Change Options Order",
- "Filters - Emphasize",
- "Filters - Options",
- "Filters - Redesign",
- "Filters - Show More Options",
- "Flourishes / Small Design Details",
- "Font Size",
- "Footer Navigation",
- "Form - Add",
- "Form Activation",
- "Form Center-Aligned",
- "Form Color Change",
- "Form Field Border Change",
- "Form Field Label",
- "Form Help",
- "Form Over Partner Logos",
- "Form Over Pricing",
- "Form Over Resource",
- "Form Over UI",
- "Form Over UI - Animated",
- "Form Over UI - Other",
- "Form Over UI With Copy",
- "Form Over UI With Integrations",
- "Form Over UI With Reviews Summary",
- "Form Over UI With Social Proof",
- "Form Over UI With Testimonial",
- "Form Over UI in Hero",
- "Form Positioning",
- "Form Redesign",
- "Form in Modal",
- "Form on the Left",
- "Free Shipping",
- "Free Shipping - Emphasize",
- "Full-width Modal",
- "G2/Forrester/Gartner Alignment Chart",
- "GDPR Cookie Modal",
- "GDPR Cookie Modal - Change",
- "Gender Segmentation",
- "Geo-specific Personalization",
- "Guarantee",
- "Hamburger Button",
- "Headline",
- "Headline Center-Aligned",
- "Headline Copy Change",
- "Hero Bar Navigation",
- "Hero Center-Aligned",
- "Hero Layout",
- "Hero Redesign",
- "Hero Size",
- "Hero Tiles",
- "Hide Price in Form",
- "Highlight Words",
- "How It Works",
- "How It Works - Emphasize",
- "Hyperlink",
- "Icon Label",
- "Icons",
- "Image",
- "Image/Video to Background",
- "Impact Chart",
- "Inclusions",
- "Inclusions - Additional",
- "Increase Number of Related Articles/Case Studies",
- "Increase Trust",
- "Industry Types",
- "Infinite Scroll",
- "Inline Form Field Copy",
- "Inline Form Field Copy Change",
- "Inline Link Nudge",
- "Inline Validation",
- "Insertion",
- "Insertion Redesign",
- "Instant Copy",
- "Integrations",
- "Interactive Hero",
- "Interactive Modal",
- "Interactive Product Section",
- "Interactive Tour",
- "Interest Rates",
- "Interface in Background",
- "Left Form Over UI",
- "Link to External Sites",
- "Link to Internal Page",
- "Link to Product Listing",
- "Live Chat",
- "Live Trends",
- "Local Languages",
- "Localization Option",
- "Locked Hero",
- "Login Method",
- "Login to Signup",
- "Logo",
- "Logo Positioning",
- "Logo Size",
- "Long Page",
- "Longform Baseline",
- "Map",
- "Media Mentions Social Proof",
- "Meet the People",
- "Menu",
- "Mini Case Studies",
- "Mini Donation",
- "Mini Reviews",
- "Mini Triage in Nav",
- "Minimize Price Font Size",
- "Mixed Media",
- "Modal",
- "Modify Breadcrumbs",
- "Modify Search Bar",
- "More Detailed Reviews",
- "More Expensive First",
- "More Product Info",
- "Move Bestsellers/Featured Products Up",
- "Move Image Up",
- "Multi-step Forms",
- "Multiple Hero Images",
- "Natural Language Forms",
- "Navigation Bar",
- "Navigation Bar Redesign",
- "New Edition",
- "Number of Purchases",
- "Number of Slots / Stocks Progress Bar",
- "Online / Active Now Status",
- "Open in a New Tab",
- "Optimized Copy",
- "Optimized Copy - Form",
- "Optimized Copy - Hero",
- "Other",
- "Out of Stock Products",
- "Page Layout",
- "Pain Point",
- "Partner Logos",
- "Payment First",
- "Payment Options",
- "People Like Me With Problems Like Mine",
- "Personal Headline",
- "Personalized OS",
- "Postcode / Domain / Other Plus CTA",
- "Pre-expanded Chatbot",
- "Pre-expanded Dropdown",
- "Pre-expanded Tooltip",
- "Pre-expanded/Condensed FAQs",
- "Pre-filled Text Box",
- "Pre-selected Options",
- "Price Per Month",
- "Price Per Unit",
- "Pricing Cards",
- "Pricing Cards & Features Combined",
- "Pricing Cards - Change / Add Emphasized Plan",
- "Pricing Cards - Change Inclusions",
- "Pricing Cards - Emphasize Inclusions",
- "Pricing Cards - Inclusions",
- "Pricing Defaulted to Annual",
- "Pricing Defaulted to Subscription",
- "Pricing Toggle",
- "Product Comparison",
- "Product Customization",
- "Product Image",
- "Product Organization",
- "Product Subscription",
- "Product/Bundle",
- "Progress Bar",
- "Progress Timeline",
- "Promotion",
- "Promotion - Discount Offer Banner",
- "Promotion - Emphasize",
- "Promotions - Trial Offer",
- "Pros and Cons",
- "Prozac",
- "Prozac - Others",
- "Prozac Copy Change",
- "Purchase History",
- "Push Copy Up",
- "Push Form Up",
- "Push Form Up - Hero",
- "Push Guarantee Up",
- "Push Pricing Up",
- "QR Code",
- "Qualifying Questions",
- "Quantitative Headline",
- "Quantity Selector",
- "Quote Slider",
- "Radical Redesign",
- "Radical Redesign - Basecamp Pricing",
- "Real People",
- "Rearranged Form Content",
- "Recently Viewed / Purchased",
- "Recommended (Non-Product)",
- "Recommended Tag",
- "Redesign Footer",
- "Redesign Modal",
- "Redesign Review Ribbon",
- "Redesign Section",
- "Redesign Triage",
- "Reduce Choices",
- "Reduce Distractors",
- "Reduce Form Length / Remove Form Fields",
- "Reduce Lower Interest Content",
- "Reduce Steps",
- "Reduced Spacing",
- "Referral Section",
- "Related Articles",
- "Related Products",
- "Remove Advertisements / Ads",
- "Remove Detail",
- "Remove Escapes",
- "Remove Hero",
- "Remove Overages",
- "Remove Section",
- "Remove Slider",
- "Remove Terms & Conditions",
- "Remove Top Banner",
- "Remove Video",
- "Replace Animation With Video",
- "Replace Icon With Image",
- "Replace Image With Animation",
- "Replace Image With Interface",
- "Replace Image with Video",
- "Resource Section",
- "Returning Users",
- "Review Ribbon",
- "Reviewer Name",
- "Reviews Section",
- "Reviews Summary",
- "Reviews Summary Positioning",
- "Rewording",
- "Savings",
- "Savings - Emphasize",
- "Scarcity",
- "Scroll Down Animation",
- "Search Bar / Button",
- "Search Section",
- "Search to Dropdown",
- "Section Banner",
- "Shipping Information",
- "Shorten FAQs",
- "Shorten Product Comparison",
- "Shortened Forms",
- "Shortform Baseline",
- "Show / Change to Required / Mandatory Field",
- "Show Annual Price",
- "Show Daily Price",
- "Show Interface",
- "Show Price",
- "Show Starting Price",
- "Show Weekly Price",
- "Side Navigation",
- "Signup / Registration Wall",
- "Signup to Demo",
- "Simplify Design",
- "Single Annual-Monthly Toggle in Pricing",
- "Single CTA in Pricing",
- "Single Select to Multiple Select Option / Choice",
- "Single Sign-On (SSO)",
- "Slider Positioning",
- "Slider Thumbnail",
- "Sliding Form",
- "Slight Icon Change",
- "Social Count",
- "Social Media Links",
- "Social Proof",
- "Social Proof Copy Change",
- "Social Proof in Hero",
- "Specific Benefit*",
- "Specific Contact",
- "Specific Guarantee",
- "Specific Headline",
- "Split Screen",
- "Split Screen with Sticky Form",
- "Statistics",
- "Statistics - Emphasize",
- "Step Numbers",
- "Sticky - Other",
- "Sticky Banner",
- "Sticky CTA",
- "Sticky Footer",
- "Sticky Form",
- "Sticky Navigation Bar",
- "Sticky Positioning",
- "Sticky Redesign",
- "Sticky Reviews",
- "Sticky Search Bar",
- "Sticky Side Navigation",
- "Sticky Subnav",
- "Store/Service Locations",
- "Stories-style Video",
- "Strikethrough Pricing",
- "Subhead",
- "Subhead Copy Change",
- "Subscription/Plan Time/Duration",
- "Superhead",
- "Superhead Copy Change",
- "Suppress Promo",
- "Suppressed Content",
- "Suppressed Copy",
- "Table of Contents",
- "Tags",
- "Tease Pricing",
- "Tease Savings",
- "Tease Video / Content",
- "Terms of Service",
- "Testimonial Positioning",
- "Testimonials",
- "Testimonials With Stars",
- "Text Field/Dropdown to Button",
- "Text Field/Dropdown to Slider",
- "Text on the Left",
- "The Science / Method Behind",
- "Time to Completion*",
- "Time to First Ah-ha / Completion",
- "Tooltip",
- "Tooltip - Change",
- "Tooltip Positioning",
- "Top Banner Color Change",
- "Top Banner Copy Change",
- "Triage",
- "Trust Badges",
- "Trust Badges - Emphasize",
- "Trust Badges in Hero",
- "USPs - Logos / Icons / Images",
- "Unique Selling Points (USPs)*",
- "Upsell Membership",
- "Upsell to Demo/Trial",
- "Urgency",
- "Use Case",
- "Video in Hero",
- "Wall of Testimonials",
- "Warranty",
- "Widen Webpage",
-];
-
-const industries = [
-    "Automotive & Transportation",
-    "B2B Services",
-    "B2B Software & Tech",
-    "Consumer Services",
-    "Consumer Software & Apps",
-    "Education",
-    "Finance, Insurance & Real Estate",
-    "Food, Hospitality & Travel",
-    "Health & Wellness",
-    "Industrial & Manufacturing",
-    "Media & Entertainment",
-    "Non-Profit & Government",
-    "Other",
-    "Retail & E-commerce"
-];
-
-const customerTypes = [
-    "B2B",
-    "B2C", 
-    "Both",
-    "Other*"
-];
-
-const pageTypes = [
-    "Awareness & Discovery",
-    "Consideration & Evaluation", 
-    "Conversion",
-    "Internal & Navigation",
-    "Post-Conversion & Other"
-];
-
-const results = [
-    "Loser",
-    "Neutral",
-    "Winner"
-];
-
-const businessModels = [
-    "E-Commerce",
-    "Lead Generation",
-    "Other*",
-    "SaaS"
-];
-
-const conversionTypes = [
-    "Direct Purchase",
-    "High-Intent Lead Gen", 
-    "Info/Content Lead Gen",
-    "Location Search",
-    "Non-Profit/Community",
-    "Other Conversion"
-];
-
-// Create category mappings in the format expected by app.py
-const categoryMappings = {
-    "Business Model": {
-        "num_categories": businessModels.length,
-        "categories": businessModels
-    },
-    "Customer Type": {
-        "num_categories": customerTypes.length,
-        "categories": customerTypes
-    },
-    "grouped_conversion_type": {
-        "num_categories": conversionTypes.length,
-        "categories": conversionTypes
-    },
-    "grouped_industry": {
-        "num_categories": industries.length,
-        "categories": industries
-    },
-    "grouped_page_type": {
-        "num_categories": pageTypes.length,
-        "categories": pageTypes
-    }
-};
-
-// Export the arrays and mappings so they can be imported into other files.
-module.exports = {
-    industries,
-    customerTypes,
-    pageTypes,
-    results,
-    businessModels,
-    conversionTypes,
-    pattern,
-    categoryMappings,
-};

model/multimodal_cat_mappings_GGG.json

@@ -1,60 +0,0 @@
-{
-    "Business Model": {
-        "num_categories": 4,
-        "categories": [
-            "E-Commerce",
-            "Lead Generation",
-            "Other*",
-            "SaaS"
-        ]
-    },
-    "Customer Type": {
-        "num_categories": 4,
-        "categories": [
-            "B2B",
-            "B2C",
-            "Both",
-            "Other*"
-        ]
-    },
-    "grouped_conversion_type": {
-        "num_categories": 6,
-        "categories": [
-            "Direct Purchase",
-            "High-Intent Lead Gen",
-            "Info/Content Lead Gen",
-            "Location Search",
-            "Non-Profit/Community",
-            "Other Conversion"
-        ]
-    },
-    "grouped_industry": {
-        "num_categories": 14,
-        "categories": [
-            "Automotive & Transportation",
-            "B2B Services",
-            "B2B Software & Tech",
-            "Consumer Services",
-            "Consumer Software & Apps",
-            "Education",
-            "Finance, Insurance & Real Estate",
-            "Food, Hospitality & Travel",
-            "Health & Wellness",
-            "Industrial & Manufacturing",
-            "Media & Entertainment",
-            "Non-Profit & Government",
-            "Other",
-            "Retail & E-commerce"
-        ]
-    },
-    "grouped_page_type": {
-        "num_categories": 5,
-        "categories": [
-            "Awareness & Discovery",
-            "Consideration & Evaluation",
-            "Conversion",
-            "Internal & Navigation",
-            "Post-Conversion & Other"
-        ]
-    }
-}

model/multimodal_gated_model_2.7_GGG.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f174db8f8c95eed67deea8e3b3073d41f98699f64f0f10112532ef5a21c0c5d9
-size 788806087