API_CURL_EXAMPLES.sh

#!/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"