build error - fix 1

.dockerignore

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+# Don't include these in Docker build
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.Python
+*.so
+*.egg-info/
+
+# Development files
+.git/
+.gitignore
+.gitattributes
+.env
+.env.*
+
+# Documentation and examples (not needed for runtime)
+API_CLIENT_EXAMPLES.py
+API_CURL_EXAMPLES.sh
+API_JAVASCRIPT_EXAMPLE.js
+API_KEYS_FOR_COLLEAGUE.md
+API_USAGE.md
+API_USAGE_UPDATED.md
+CHANGELOG_API_UPDATE.md
+FRONTEND_README.md
+frontend.html
+index_v2.html
+setup_instructions.md
+SETUP_MODEL_HUB.md
+
+# Unused files (pattern detection removed)
+patterbs.json
+confidence_scores.js
+metadata.js
+
+# Test files
+test_images/
+*.local.*
+
+# IDE files
+.vscode/
+.idea/
+*.swp
+*.swo
+.DS_Store
+

.gitattributes

@@ -1,35 +1,12 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
+*.gguf filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
 *.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text

API_CLIENT_EXAMPLES.py

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+"""
+A/B Test Predictor - API Client Examples
+==========================================
+
+This file shows how to send requests to the A/B Test Predictor API.
+"""
+
+# ============================================================================
+# Option 1: Gradio Python Client (Recommended)
+# ============================================================================
+
+from gradio_client import Client
+from PIL import Image
+import json
+
+# Initialize the client
+# For local deployment:
+client = Client("http://localhost:7860")
+
+# For Hugging Face Spaces deployment:
+# client = Client("your-username/ABTestPredictor")
+
+def predict_with_gradio_client(control_image_path, variant_image_path, 
+                                business_model, customer_type, conversion_type, 
+                                industry, page_type):
+    """
+    Send prediction request using Gradio Client
+    
+    Args:
+        control_image_path: Path to control image file
+        variant_image_path: Path to variant image file
+        business_model: One of ["E-Commerce", "Lead Generation", "Other*", "SaaS"]
+        customer_type: One of ["B2B", "B2C", "Both", "Other*"]
+        conversion_type: One of ["Direct Purchase", "High-Intent Lead Gen", 
+                                 "Info/Content Lead Gen", "Location Search", 
+                                 "Non-Profit/Community", "Other Conversion"]
+        industry: One of the 14 industry categories
+        page_type: One of ["Awareness & Discovery", "Consideration & Evaluation", 
+                           "Conversion", "Internal & Navigation", "Post-Conversion & Other"]
+    
+    Returns:
+        dict: Prediction results with confidence scores
+    """
+    result = client.predict(
+        control_image_path,      # Control image file path
+        variant_image_path,      # Variant image file path
+        business_model,          # Business Model dropdown
+        customer_type,           # Customer Type dropdown
+        conversion_type,         # Conversion Type dropdown
+        industry,                # Industry dropdown
+        page_type,               # Page Type dropdown
+        api_name="/predict_with_categorical_data"  # The function endpoint
+    )
+    
+    return result
+
+
+# Example usage
+if __name__ == "__main__":
+    # Example 1: Basic prediction
+    result = predict_with_gradio_client(
+        control_image_path="path/to/control_image.jpg",
+        variant_image_path="path/to/variant_image.jpg",
+        business_model="SaaS",
+        customer_type="B2B",
+        conversion_type="High-Intent Lead Gen",
+        industry="B2B Software & Tech",
+        page_type="Awareness & Discovery"
+    )
+    
+    print("Prediction Results:")
+    print(json.dumps(result, indent=2))
+    
+    # Access specific fields
+    win_probability = result['predictionResults']['probability']
+    confidence = result['predictionResults']['modelConfidence']
+    
+    print(f"\nWin Probability: {win_probability}")
+    print(f"Model Confidence: {confidence}%")
+
+
+# ============================================================================
+# Option 2: Direct HTTP POST Request (cURL equivalent in Python)
+# ============================================================================
+
+import requests
+import base64
+
+def predict_with_http_request(control_image_path, variant_image_path,
+                               business_model, customer_type, conversion_type,
+                               industry, page_type, api_url="http://localhost:7860"):
+    """
+    Send prediction request using direct HTTP POST
+    
+    Note: This requires converting images to base64 for Gradio's API format
+    """
+    
+    # Read and encode images
+    with open(control_image_path, "rb") as f:
+        control_b64 = base64.b64encode(f.read()).decode()
+    
+    with open(variant_image_path, "rb") as f:
+        variant_b64 = base64.b64encode(f.read()).decode()
+    
+    # Prepare the request payload (Gradio format)
+    payload = {
+        "data": [
+            f"data:image/jpeg;base64,{control_b64}",  # Control image
+            f"data:image/jpeg;base64,{variant_b64}",  # Variant image
+            business_model,
+            customer_type,
+            conversion_type,
+            industry,
+            page_type
+        ]
+    }
+    
+    # Send POST request to Gradio API
+    response = requests.post(
+        f"{api_url}/api/predict",
+        json=payload,
+        headers={"Content-Type": "application/json"}
+    )
+    
+    if response.status_code == 200:
+        return response.json()['data'][0]  # Gradio wraps response in 'data' array
+    else:
+        raise Exception(f"API request failed: {response.status_code} - {response.text}")
+
+
+# ============================================================================
+# Option 3: Using PIL Images (in-memory)
+# ============================================================================
+
+import numpy as np
+from PIL import Image
+
+def predict_with_pil_images(control_img, variant_img,
+                             business_model, customer_type, conversion_type,
+                             industry, page_type):
+    """
+    Send prediction with PIL Image objects (useful for programmatic image generation)
+    
+    Args:
+        control_img: PIL Image object
+        variant_img: PIL Image object
+    """
+    
+    # Convert PIL images to numpy arrays (Gradio expects numpy arrays)
+    control_array = np.array(control_img)
+    variant_array = np.array(variant_img)
+    
+    # Use the Gradio client
+    result = client.predict(
+        control_array,
+        variant_array,
+        business_model,
+        customer_type,
+        conversion_type,
+        industry,
+        page_type,
+        api_name="/predict_with_categorical_data"
+    )
+    
+    return result
+
+
+# Example with PIL
+if __name__ == "__main__":
+    # Load images using PIL
+    control_img = Image.open("control.jpg")
+    variant_img = Image.open("variant.jpg")
+    
+    result = predict_with_pil_images(
+        control_img=control_img,
+        variant_img=variant_img,
+        business_model="SaaS",
+        customer_type="B2B",
+        conversion_type="High-Intent Lead Gen",
+        industry="B2B Software & Tech",
+        page_type="Awareness & Discovery"
+    )
+
+
+# ============================================================================
+# Option 4: Batch Processing Multiple Tests
+# ============================================================================
+
+def batch_predict(test_cases, output_file="results.json"):
+    """
+    Process multiple A/B tests in batch
+    
+    Args:
+        test_cases: List of dicts with test parameters
+        output_file: Where to save results
+    
+    Example test_cases:
+        [
+            {
+                "control_image": "test1_control.jpg",
+                "variant_image": "test1_variant.jpg",
+                "business_model": "SaaS",
+                "customer_type": "B2B",
+                "conversion_type": "High-Intent Lead Gen",
+                "industry": "B2B Software & Tech",
+                "page_type": "Awareness & Discovery"
+            },
+            # ... more tests
+        ]
+    """
+    
+    results = []
+    
+    for i, test in enumerate(test_cases):
+        print(f"Processing test {i+1}/{len(test_cases)}...")
+        
+        try:
+            result = predict_with_gradio_client(
+                control_image_path=test["control_image"],
+                variant_image_path=test["variant_image"],
+                business_model=test["business_model"],
+                customer_type=test["customer_type"],
+                conversion_type=test["conversion_type"],
+                industry=test["industry"],
+                page_type=test["page_type"]
+            )
+            
+            results.append({
+                "test_id": i + 1,
+                "input": test,
+                "prediction": result
+            })
+            
+        except Exception as e:
+            print(f"Error processing test {i+1}: {e}")
+            results.append({
+                "test_id": i + 1,
+                "input": test,
+                "error": str(e)
+            })
+    
+    # Save results
+    with open(output_file, "w") as f:
+        json.dump(results, f, indent=2)
+    
+    print(f"\nBatch processing complete! Results saved to {output_file}")
+    return results
+
+
+# ============================================================================
+# Valid Category Values (for reference)
+# ============================================================================
+
+VALID_CATEGORIES = {
+    "business_model": [
+        "E-Commerce",
+        "Lead Generation",
+        "Other*",
+        "SaaS"
+    ],
+    
+    "customer_type": [
+        "B2B",
+        "B2C",
+        "Both",
+        "Other*"
+    ],
+    
+    "conversion_type": [
+        "Direct Purchase",
+        "High-Intent Lead Gen",
+        "Info/Content Lead Gen",
+        "Location Search",
+        "Non-Profit/Community",
+        "Other Conversion"
+    ],
+    
+    "industry": [
+        "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"
+    ],
+    
+    "page_type": [
+        "Awareness & Discovery",
+        "Consideration & Evaluation",
+        "Conversion",
+        "Internal & Navigation",
+        "Post-Conversion & Other"
+    ]
+}
+
+
+def validate_categories(business_model, customer_type, conversion_type, 
+                       industry, page_type):
+    """Validate that all categories are valid"""
+    
+    errors = []
+    
+    if business_model not in VALID_CATEGORIES["business_model"]:
+        errors.append(f"Invalid business_model: {business_model}")
+    
+    if customer_type not in VALID_CATEGORIES["customer_type"]:
+        errors.append(f"Invalid customer_type: {customer_type}")
+    
+    if conversion_type not in VALID_CATEGORIES["conversion_type"]:
+        errors.append(f"Invalid conversion_type: {conversion_type}")
+    
+    if industry not in VALID_CATEGORIES["industry"]:
+        errors.append(f"Invalid industry: {industry}")
+    
+    if page_type not in VALID_CATEGORIES["page_type"]:
+        errors.append(f"Invalid page_type: {page_type}")
+    
+    if errors:
+        raise ValueError("Category validation failed:\n" + "\n".join(errors))
+    
+    return True
+
+
+# ============================================================================
+# Error Handling Example
+# ============================================================================
+
+def safe_predict(control_image_path, variant_image_path,
+                business_model, customer_type, conversion_type,
+                industry, page_type):
+    """
+    Safe prediction with error handling and validation
+    """
+    
+    try:
+        # Validate categories first
+        validate_categories(business_model, customer_type, conversion_type,
+                          industry, page_type)
+        
+        # Make prediction
+        result = predict_with_gradio_client(
+            control_image_path=control_image_path,
+            variant_image_path=variant_image_path,
+            business_model=business_model,
+            customer_type=customer_type,
+            conversion_type=conversion_type,
+            industry=industry,
+            page_type=page_type
+        )
+        
+        return {
+            "success": True,
+            "result": result
+        }
+        
+    except ValueError as e:
+        return {
+            "success": False,
+            "error": "Validation Error",
+            "message": str(e)
+        }
+    
+    except Exception as e:
+        return {
+            "success": False,
+            "error": "API Error",
+            "message": str(e)
+        }
+

API_CURL_EXAMPLES.sh

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+#!/bin/bash
+
+# ============================================================================
+# A/B Test Predictor - cURL API Examples
+# ============================================================================
+
+# Configuration
+API_URL="http://localhost:7860"  # Change to your deployment URL
+# For Hugging Face Spaces: API_URL="https://your-username-abtestpredictor.hf.space"
+
+# ============================================================================
+# Example 1: Basic Prediction with Image Files
+# ============================================================================
+
+# Convert images to base64
+CONTROL_IMAGE_B64=$(base64 -i control_image.jpg)
+VARIANT_IMAGE_B64=$(base64 -i variant_image.jpg)
+
+# Send POST request to Gradio API
+curl -X POST "${API_URL}/api/predict" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "data": [
+      "data:image/jpeg;base64,'"${CONTROL_IMAGE_B64}"'",
+      "data:image/jpeg;base64,'"${VARIANT_IMAGE_B64}"'",
+      "SaaS",
+      "B2B",
+      "High-Intent Lead Gen",
+      "B2B Software & Tech",
+      "Awareness & Discovery"
+    ],
+    "fn_index": 0
+  }'
+
+# ============================================================================
+# Example 2: Using a Function to Send Requests
+# ============================================================================
+
+predict_abtest() {
+    local CONTROL_IMG=$1
+    local VARIANT_IMG=$2
+    local BUSINESS_MODEL=$3
+    local CUSTOMER_TYPE=$4
+    local CONVERSION_TYPE=$5
+    local INDUSTRY=$6
+    local PAGE_TYPE=$7
+    
+    # Encode images
+    local CONTROL_B64=$(base64 -i "$CONTROL_IMG")
+    local VARIANT_B64=$(base64 -i "$VARIANT_IMG")
+    
+    # Make API call
+    curl -X POST "${API_URL}/api/predict" \
+      -H "Content-Type: application/json" \
+      -d '{
+        "data": [
+          "data:image/jpeg;base64,'"${CONTROL_B64}"'",
+          "data:image/jpeg;base64,'"${VARIANT_B64}"'",
+          "'"${BUSINESS_MODEL}"'",
+          "'"${CUSTOMER_TYPE}"'",
+          "'"${CONVERSION_TYPE}"'",
+          "'"${INDUSTRY}"'",
+          "'"${PAGE_TYPE}"'"
+        ]
+      }' | jq .
+}
+
+# Usage
+predict_abtest \
+  "control.jpg" \
+  "variant.jpg" \
+  "SaaS" \
+  "B2B" \
+  "High-Intent Lead Gen" \
+  "B2B Software & Tech" \
+  "Awareness & Discovery"
+
+# ============================================================================
+# Example 3: Multiple Predictions in a Loop
+# ============================================================================
+
+# Read test cases from CSV
+while IFS=',' read -r control variant business customer conversion industry page
+do
+    echo "Processing: $control vs $variant"
+    
+    predict_abtest \
+      "$control" \
+      "$variant" \
+      "$business" \
+      "$customer" \
+      "$conversion" \
+      "$industry" \
+      "$page"
+    
+    sleep 1  # Rate limiting
+done < test_cases.csv
+
+# ============================================================================
+# Example 4: Save Results to File
+# ============================================================================
+
+predict_and_save() {
+    local OUTPUT_FILE=$1
+    
+    predict_abtest \
+      "control.jpg" \
+      "variant.jpg" \
+      "SaaS" \
+      "B2B" \
+      "High-Intent Lead Gen" \
+      "B2B Software & Tech" \
+      "Awareness & Discovery" > "$OUTPUT_FILE"
+    
+    echo "Results saved to $OUTPUT_FILE"
+}
+
+predict_and_save "prediction_result.json"
+
+# ============================================================================
+# Example 5: Parse and Extract Specific Fields
+# ============================================================================
+
+# Get just the win probability
+get_win_probability() {
+    predict_abtest "$@" | jq -r '.data[0].predictionResults.probability'
+}
+
+# Get model confidence
+get_confidence() {
+    predict_abtest "$@" | jq -r '.data[0].predictionResults.modelConfidence'
+}
+
+# Usage
+PROB=$(get_win_probability "control.jpg" "variant.jpg" "SaaS" "B2B" "High-Intent Lead Gen" "B2B Software & Tech" "Awareness & Discovery")
+CONF=$(get_confidence "control.jpg" "variant.jpg" "SaaS" "B2B" "High-Intent Lead Gen" "B2B Software & Tech" "Awareness & Discovery")
+
+echo "Win Probability: $PROB"
+echo "Model Confidence: $CONF%"
+
+# ============================================================================
+# Valid Category Values (for reference)
+# ============================================================================
+
+# Business Model options:
+# - "E-Commerce"
+# - "Lead Generation"
+# - "Other*"
+# - "SaaS"
+
+# Customer Type options:
+# - "B2B"
+# - "B2C"
+# - "Both"
+# - "Other*"
+
+# Conversion Type options:
+# - "Direct Purchase"
+# - "High-Intent Lead Gen"
+# - "Info/Content Lead Gen"
+# - "Location Search"
+# - "Non-Profit/Community"
+# - "Other Conversion"
+
+# Industry options:
+# - "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"
+
+# Page Type options:
+# - "Awareness & Discovery"
+# - "Consideration & Evaluation"
+# - "Conversion"
+# - "Internal & Navigation"
+# - "Post-Conversion & Other"
+

API_JAVASCRIPT_EXAMPLE.js

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+/**
+ * A/B Test Predictor - JavaScript/Node.js API Examples
+ */
+
+// ============================================================================
+// Option 1: Using Fetch API (Browser/Node.js)
+// ============================================================================
+
+async function predictABTest(controlImagePath, variantImagePath, categories) {
+    const apiUrl = 'http://localhost:7860/api/predict'; // Change to your deployment URL
+    
+    // Read and encode images to base64
+    const fs = require('fs').promises;
+    
+    const controlImage = await fs.readFile(controlImagePath);
+    const variantImage = await fs.readFile(variantImagePath);
+    
+    const controlB64 = `data:image/jpeg;base64,${controlImage.toString('base64')}`;
+    const variantB64 = `data:image/jpeg;base64,${variantImage.toString('base64')}`;
+    
+    // Prepare request payload
+    const payload = {
+        data: [
+            controlB64,
+            variantB64,
+            categories.businessModel,
+            categories.customerType,
+            categories.conversionType,
+            categories.industry,
+            categories.pageType
+        ]
+    };
+    
+    // Send POST request
+    const response = await fetch(apiUrl, {
+        method: 'POST',
+        headers: {
+            'Content-Type': 'application/json'
+        },
+        body: JSON.stringify(payload)
+    });
+    
+    if (!response.ok) {
+        throw new Error(`API request failed: ${response.status} ${response.statusText}`);
+    }
+    
+    const result = await response.json();
+    return result.data[0]; // Gradio wraps response in 'data' array
+}
+
+// Example usage
+(async () => {
+    try {
+        const result = await predictABTest(
+            'control.jpg',
+            'variant.jpg',
+            {
+                businessModel: 'SaaS',
+                customerType: 'B2B',
+                conversionType: 'High-Intent Lead Gen',
+                industry: 'B2B Software & Tech',
+                pageType: 'Awareness & Discovery'
+            }
+        );
+        
+        console.log('Prediction Results:');
+        console.log(JSON.stringify(result, null, 2));
+        
+        console.log('\nWin Probability:', result.predictionResults.probability);
+        console.log('Model Confidence:', result.predictionResults.modelConfidence + '%');
+        
+    } catch (error) {
+        console.error('Error:', error.message);
+    }
+})();
+
+
+// ============================================================================
+// Option 2: Using Axios (More Robust)
+// ============================================================================
+
+const axios = require('axios');
+const fs = require('fs').promises;
+
+class ABTestPredictorClient {
+    constructor(apiUrl = 'http://localhost:7860') {
+        this.apiUrl = apiUrl;
+        this.endpoint = `${apiUrl}/api/predict`;
+    }
+    
+    async encodeImage(imagePath) {
+        const imageBuffer = await fs.readFile(imagePath);
+        return `data:image/jpeg;base64,${imageBuffer.toString('base64')}`;
+    }
+    
+    async predict(controlImagePath, variantImagePath, categories) {
+        try {
+            // Encode images
+            const controlB64 = await this.encodeImage(controlImagePath);
+            const variantB64 = await this.encodeImage(variantImagePath);
+            
+            // Validate categories
+            this.validateCategories(categories);
+            
+            // Prepare payload
+            const payload = {
+                data: [
+                    controlB64,
+                    variantB64,
+                    categories.businessModel,
+                    categories.customerType,
+                    categories.conversionType,
+                    categories.industry,
+                    categories.pageType
+                ]
+            };
+            
+            // Make API call
+            const response = await axios.post(this.endpoint, payload, {
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                timeout: 30000 // 30 second timeout
+            });
+            
+            return {
+                success: true,
+                data: response.data.data[0]
+            };
+            
+        } catch (error) {
+            return {
+                success: false,
+                error: error.message,
+                details: error.response?.data
+            };
+        }
+    }
+    
+    validateCategories(categories) {
+        const validCategories = {
+            businessModel: ['E-Commerce', 'Lead Generation', 'Other*', 'SaaS'],
+            customerType: ['B2B', 'B2C', 'Both', 'Other*'],
+            conversionType: [
+                'Direct Purchase',
+                'High-Intent Lead Gen',
+                'Info/Content Lead Gen',
+                'Location Search',
+                'Non-Profit/Community',
+                'Other Conversion'
+            ],
+            industry: [
+                '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'
+            ],
+            pageType: [
+                'Awareness & Discovery',
+                'Consideration & Evaluation',
+                'Conversion',
+                'Internal & Navigation',
+                'Post-Conversion & Other'
+            ]
+        };
+        
+        // Validate each category
+        for (const [key, value] of Object.entries(categories)) {
+            if (!validCategories[key]?.includes(value)) {
+                throw new Error(`Invalid ${key}: ${value}`);
+            }
+        }
+        
+        return true;
+    }
+    
+    async batchPredict(testCases) {
+        const results = [];
+        
+        for (let i = 0; i < testCases.length; i++) {
+            console.log(`Processing test ${i + 1}/${testCases.length}...`);
+            
+            const testCase = testCases[i];
+            const result = await this.predict(
+                testCase.controlImage,
+                testCase.variantImage,
+                testCase.categories
+            );
+            
+            results.push({
+                testId: i + 1,
+                input: testCase,
+                result: result
+            });
+            
+            // Rate limiting
+            await new Promise(resolve => setTimeout(resolve, 1000));
+        }
+        
+        return results;
+    }
+}
+
+// Example usage
+(async () => {
+    const client = new ABTestPredictorClient('http://localhost:7860');
+    
+    // Single prediction
+    const result = await client.predict(
+        'control.jpg',
+        'variant.jpg',
+        {
+            businessModel: 'SaaS',
+            customerType: 'B2B',
+            conversionType: 'High-Intent Lead Gen',
+            industry: 'B2B Software & Tech',
+            pageType: 'Awareness & Discovery'
+        }
+    );
+    
+    if (result.success) {
+        console.log('Prediction successful!');
+        console.log('Win Probability:', result.data.predictionResults.probability);
+        console.log('Confidence:', result.data.predictionResults.modelConfidence + '%');
+    } else {
+        console.error('Prediction failed:', result.error);
+    }
+    
+    // Batch predictions
+    const testCases = [
+        {
+            controlImage: 'test1_control.jpg',
+            variantImage: 'test1_variant.jpg',
+            categories: {
+                businessModel: 'SaaS',
+                customerType: 'B2B',
+                conversionType: 'High-Intent Lead Gen',
+                industry: 'B2B Software & Tech',
+                pageType: 'Awareness & Discovery'
+            }
+        },
+        // Add more test cases...
+    ];
+    
+    const batchResults = await client.batchPredict(testCases);
+    console.log('Batch results:', JSON.stringify(batchResults, null, 2));
+})();
+
+
+// ============================================================================
+// Option 3: Browser Example (Using File Input)
+// ============================================================================
+
+// HTML:
+// <input type="file" id="controlImage" accept="image/*">
+// <input type="file" id="variantImage" accept="image/*">
+// <button onclick="predictFromBrowser()">Predict</button>
+// <div id="results"></div>
+
+async function predictFromBrowser() {
+    const controlFile = document.getElementById('controlImage').files[0];
+    const variantFile = document.getElementById('variantImage').files[0];
+    
+    if (!controlFile || !variantFile) {
+        alert('Please select both images');
+        return;
+    }
+    
+    // Convert files to base64
+    const controlB64 = await fileToBase64(controlFile);
+    const variantB64 = await fileToBase64(variantFile);
+    
+    // Prepare payload
+    const payload = {
+        data: [
+            controlB64,
+            variantB64,
+            'SaaS',
+            'B2B',
+            'High-Intent Lead Gen',
+            'B2B Software & Tech',
+            'Awareness & Discovery'
+        ]
+    };
+    
+    try {
+        const response = await fetch('http://localhost:7860/api/predict', {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json'
+            },
+            body: JSON.stringify(payload)
+        });
+        
+        const result = await response.json();
+        displayResults(result.data[0]);
+        
+    } catch (error) {
+        alert('Error: ' + error.message);
+    }
+}
+
+function fileToBase64(file) {
+    return new Promise((resolve, reject) => {
+        const reader = new FileReader();
+        reader.onload = () => resolve(reader.result);
+        reader.onerror = reject;
+        reader.readAsDataURL(file);
+    });
+}
+
+function displayResults(data) {
+    const resultsDiv = document.getElementById('results');
+    resultsDiv.innerHTML = `
+        <h3>Prediction Results</h3>
+        <p>Win Probability: ${data.predictionResults.probability}</p>
+        <p>Model Confidence: ${data.predictionResults.modelConfidence}%</p>
+        <p>Training Samples: ${data.predictionResults.trainingDataSamples}</p>
+        <p>Total Predictions: ${data.predictionResults.totalPredictions}</p>
+    `;
+}
+
+
+// ============================================================================
+// Option 4: Express.js Server Example
+// ============================================================================
+
+const express = require('express');
+const multer = require('multer');
+const upload = multer({ dest: 'uploads/' });
+
+const app = express();
+const client = new ABTestPredictorClient('http://localhost:7860');
+
+app.post('/predict', upload.fields([
+    { name: 'control', maxCount: 1 },
+    { name: 'variant', maxCount: 1 }
+]), async (req, res) => {
+    try {
+        const controlPath = req.files['control'][0].path;
+        const variantPath = req.files['variant'][0].path;
+        
+        const categories = {
+            businessModel: req.body.businessModel,
+            customerType: req.body.customerType,
+            conversionType: req.body.conversionType,
+            industry: req.body.industry,
+            pageType: req.body.pageType
+        };
+        
+        const result = await client.predict(controlPath, variantPath, categories);
+        
+        // Clean up uploaded files
+        const fs = require('fs');
+        fs.unlinkSync(controlPath);
+        fs.unlinkSync(variantPath);
+        
+        res.json(result);
+        
+    } catch (error) {
+        res.status(500).json({
+            success: false,
+            error: error.message
+        });
+    }
+});
+
+app.listen(3000, () => {
+    console.log('Proxy server running on port 3000');
+});
+

DEPLOYMENT_FIX.md

@@ -0,0 +1,164 @@
+# Hugging Face Spaces Deployment Fix
+
+## Problem
+Build was failing with exit code 1 due to cache issues and potentially large/unnecessary files being uploaded.
+
+## Solutions Applied
+
+### 1. Created `.dockerignore`
+This file prevents unnecessary files from being included in the Docker build:
+- Documentation files (API examples, setup guides, etc.)
+- Development files (.git, .env, etc.)
+- Unused files from previous version (patterbs.json, metadata.js, confidence_scores.js)
+- IDE and test files
+
+### 2. Created `.gitattributes`
+Ensures large model files are properly tracked with Git LFS:
+- *.pth files (your model)
+- Other common model formats
+
+### 3. Files That Can Be Deleted (No Longer Needed)
+
+These files are from the old AI API version and are no longer used:
+```bash
+# Can be safely deleted
+rm patterbs.json          # 269KB - Pattern descriptions (no longer used)
+rm confidence_scores.js   # 15KB - JS version of confidence data
+rm metadata.js            # 14KB - JS version of metadata
+rm frontend.html          # Old frontend (if not using)
+rm index_v2.html          # Old frontend (if not using)
+rm API_USAGE.md           # Old API docs (replaced by API_USAGE_UPDATED.md)
+```
+
+### 4. Essential Files for Deployment
+
+Keep these files:
+```
+✅ app.py                              # Main application
+✅ requirements.txt                    # Python dependencies
+✅ packages.txt                        # System dependencies (tesseract-ocr)
+✅ README.md                           # Project documentation
+✅ confidence_scores.json              # Confidence data (NEEDED)
+✅ model/multimodal_gated_model_2.7_GGG.pth      # Model weights
+✅ model/multimodal_cat_mappings_GGG.json        # Category mappings
+✅ .gitattributes                      # Git LFS configuration
+✅ .dockerignore                       # Docker build optimization
+```
+
+### 5. Deployment Steps
+
+1. **Clean up unnecessary files:**
+```bash
+cd /Users/nitish/Spiralyze/HuggingFace/Spaces/ABTestPredictor
+
+# Optional: Delete unused files
+rm patterbs.json confidence_scores.js metadata.js frontend.html index_v2.html API_USAGE.md
+```
+
+2. **Verify Git LFS is tracking the model:**
+```bash
+git lfs ls-files
+# Should show: model/multimodal_gated_model_2.7_GGG.pth
+```
+
+3. **Commit and push changes:**
+```bash
+git add .
+git commit -m "Fix: Remove AI API dependencies and optimize for deployment"
+git push
+```
+
+4. **If model file isn't tracked by LFS:**
+```bash
+# Track it
+git lfs track "*.pth"
+git add .gitattributes
+git add model/multimodal_gated_model_2.7_GGG.pth
+git commit -m "Track model file with Git LFS"
+git push
+```
+
+### 6. Hugging Face Spaces Settings
+
+Make sure your Space is configured correctly:
+- **SDK**: Gradio
+- **SDK Version**: 4.44.0 (as specified in README.md)
+- **Hardware**: GPU (T4, A10G, or better recommended)
+- **No environment variables needed** (we removed API keys)
+
+### 7. Common Build Issues & Fixes
+
+#### Issue: "File too large"
+**Solution**: Use Git LFS for files > 10MB
+```bash
+git lfs track "*.pth"
+git add .gitattributes
+git commit -m "Add Git LFS tracking"
+```
+
+#### Issue: "Package installation failed"
+**Solution**: Check requirements.txt for conflicts
+```bash
+# Test locally first
+pip install -r requirements.txt
+```
+
+#### Issue: "Tesseract not found"
+**Solution**: Ensure packages.txt contains `tesseract-ocr`
+
+#### Issue: "Model file not found"
+**Solution**: Verify model files are in the correct location:
+- `model/multimodal_gated_model_2.7_GGG.pth`
+- `model/multimodal_cat_mappings_GGG.json`
+
+### 8. Testing Deployment
+
+Once deployed, test the API:
+
+**Using Gradio Interface:**
+1. Go to your Space URL
+2. Upload test images
+3. Select categories
+4. Click "Predict"
+
+**Using Python Client:**
+```python
+from gradio_client import Client
+
+client = Client("your-username/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)
+```
+
+### 9. Monitoring
+
+Check build logs in Hugging Face Spaces:
+- Go to your Space
+- Click "Logs" tab
+- Look for any errors during build or runtime
+
+### 10. Rollback Plan
+
+If issues persist:
+1. Check previous working commit: `git log`
+2. Revert: `git revert <commit-hash>`
+3. Or reset: `git reset --hard <working-commit-hash>`
+4. Force push: `git push --force` (⚠️ only if needed)
+
+## Expected Build Time
+- First build: 5-10 minutes (downloading model, installing packages)
+- Subsequent builds: 2-3 minutes (cached layers)
+
+## Success Indicators
+✅ Build completes without errors
+✅ Space status shows "Running"
+✅ Gradio interface loads correctly
+✅ Can make predictions successfully
+✅ Response includes confidence scores
+

QUICK_FIX_SUMMARY.md

@@ -0,0 +1,134 @@
+# Quick Fix for Build Error - Summary
+
+## What Was the Problem?
+The Hugging Face Spaces build was failing because:
+1. Node.js dependency for loading metadata.js (no longer needed)
+2. Unnecessary large files (patterbs.json - 269KB) being uploaded
+3. No `.dockerignore` to exclude documentation files
+4. Git LFS might not be properly configured
+
+## What Was Fixed?
+
+### 1. ✅ Simplified Category Mapping Loading
+**Changed**: Removed Node.js dependency for loading metadata.js  
+**File**: `app.py` lines 172-184  
+**Result**: Now creates default mappings directly in Python
+
+### 2. ✅ Created `.dockerignore`
+**Purpose**: Excludes unnecessary files from Docker build:
+- Documentation files (API examples, guides)
+- Unused files (patterbs.json, metadata.js, confidence_scores.js)
+- Development files (.git, .env, IDEs)
+
+### 3. ✅ Created `.gitattributes`
+**Purpose**: Ensures model files are properly tracked with Git LFS
+
+## Next Steps
+
+### Step 1: Clean Up (Optional but Recommended)
+```bash
+cd /Users/nitish/Spiralyze/HuggingFace/Spaces/ABTestPredictor
+
+# Delete files that are no longer needed
+rm patterbs.json           # Pattern descriptions (no longer used)
+rm confidence_scores.js    # JS version (not needed, we use .json)
+rm metadata.js            # JS version (not needed)
+rm frontend.html          # Old frontend (if using Gradio only)
+rm index_v2.html          # Old frontend (if using Gradio only)
+```
+
+### Step 2: Commit and Push
+```bash
+# Add all changes
+git add .
+
+# Commit
+git commit -m "Fix: Remove Node.js dependency and optimize for Hugging Face Spaces deployment"
+
+# Push to Hugging Face Spaces
+git push
+```
+
+### Step 3: Verify Model LFS Tracking
+```bash
+# Check if model is tracked by Git LFS
+git lfs ls-files
+
+# Should show: model/multimodal_gated_model_2.7_GGG.pth
+```
+
+If the model ISN'T shown, track it:
+```bash
+git lfs track "model/*.pth"
+git add .gitattributes
+git add model/multimodal_gated_model_2.7_GGG.pth
+git commit -m "Add Git LFS tracking for model file"
+git push
+```
+
+## Files That MUST Exist for Deployment
+
+```
+✅ app.py                                        # Main application
+✅ requirements.txt                              # Python dependencies
+✅ packages.txt                                  # System dependencies
+✅ README.md                                     # Documentation
+✅ confidence_scores.json                        # Confidence data
+✅ model/multimodal_gated_model_2.7_GGG.pth     # Model (tracked by LFS)
+✅ model/multimodal_cat_mappings_GGG.json       # Category mappings
+✅ .gitattributes                                # LFS configuration
+✅ .dockerignore                                 # Build optimization
+```
+
+## Expected Result After Fix
+
+- ✅ Build should complete successfully in 5-10 minutes
+- ✅ No Node.js errors
+- ✅ Smaller Docker image (excluded unnecessary files)
+- ✅ Space status: "Running"
+- ✅ API accessible and functional
+
+## Test After Deployment
+
+```python
+from gradio_client import Client
+
+client = Client("your-username/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)
+```
+
+## If Build Still Fails
+
+1. Check logs in Hugging Face Spaces
+2. Verify all required files are present
+3. Check that model file is tracked by Git LFS: `git lfs ls-files`
+4. Ensure packages.txt contains `tesseract-ocr`
+5. Review DEPLOYMENT_FIX.md for detailed troubleshooting
+
+## Changes Made to Files
+
+### app.py
+- Removed Node.js subprocess calls
+- Simplified category mapping creation
+- No external dependencies
+
+### New Files
+- `.dockerignore` - Excludes unnecessary files from build
+- `.gitattributes` - Ensures proper Git LFS tracking
+- `DEPLOYMENT_FIX.md` - Detailed troubleshooting guide
+- `QUICK_FIX_SUMMARY.md` - This file
+
+## Key Improvements
+
+🚀 **Faster builds**: Excluded documentation reduces build time  
+🔧 **Simpler deployment**: No Node.js dependency  
+📦 **Smaller images**: Only essential files included  
+✅ **Better reliability**: Standard Python-only approach
+

app.py

@@ -170,47 +170,18 @@ if not os.path.exists(MODEL_DIR):
     os.makedirs(MODEL_DIR)
 
 if not os.path.exists(CAT_MAPPINGS_SAVE_PATH):
-    print(f"⚠️ GGG Category mappings not found. Loading from metadata.js...")
-    # Import the real metadata from metadata.js
-    import subprocess
-    import sys
-    
-    try:
-        # Use Node.js to extract the categoryMappings from metadata.js
-        result = subprocess.run([
-            'node', '-e', 
-            'const meta = require("./metadata.js"); console.log(JSON.stringify(meta.categoryMappings));'
-        ], capture_output=True, text=True, cwd='.')
-        
-        if result.returncode == 0:
-            category_mappings_from_js = json.loads(result.stdout.strip())
-            print(f"✅ Successfully loaded category mappings from metadata.js for GGG model")
-            with open(CAT_MAPPINGS_SAVE_PATH, 'w') as f:
-                json.dump(category_mappings_from_js, f, indent=2)
-        else:
-            print(f"⚠️ Failed to load from metadata.js: {result.stderr}")
-            print("Creating GGG-compatible dummy mappings as fallback...")
-            dummy_mappings = {
-                "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"]}
-            }
-            with open(CAT_MAPPINGS_SAVE_PATH, 'w') as f:
-                json.dump(dummy_mappings, f, indent=2)
-    except Exception as e:
-        print(f"⚠️ Error loading metadata.js: {e}")
-        print("Creating GGG-compatible dummy mappings as fallback...")
-        dummy_mappings = {
-            "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"]}
-        }
-        with open(CAT_MAPPINGS_SAVE_PATH, 'w') as f:
-            json.dump(dummy_mappings, f, indent=2)
+    print(f"⚠️ GGG Category mappings not found. Creating default mappings...")
+    # Create the standard category mappings expected by the model
+    default_mappings = {
+        "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"]}
+    }
+    with open(CAT_MAPPINGS_SAVE_PATH, 'w') as f:
+        json.dump(default_mappings, f, indent=2)
+    print(f"✅ Created default category mappings at {CAT_MAPPINGS_SAVE_PATH}")
 
 with open(CAT_MAPPINGS_SAVE_PATH, 'r') as f:
     category_mappings = json.load(f)