712 lines
25 KiB
Bash
Executable File
712 lines
25 KiB
Bash
Executable File
#!/bin/bash
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# =================================================================
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# IMPROVED ONBOARDING FLOW SIMULATION TEST SCRIPT
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# =================================================================
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# This script simulates the complete onboarding process using the
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# real CSV data and proper import/validate endpoints
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# Configuration
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API_BASE="http://localhost:8000"
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TEST_EMAIL="onboarding.test.$(date +%s)@bakery.com"
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TEST_PASSWORD="TestPassword123!"
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TEST_NAME="Test Bakery Owner"
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REAL_CSV_FILE="bakery_sales_2023_2024.csv"
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# Colors for output
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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BLUE='\033[0;34m'
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PURPLE='\033[0;35m'
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CYAN='\033[0;36m'
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NC='\033[0m' # No Color
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# Icons for steps
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STEP_ICONS=("👤" "🏪" "📊" "🤖" "🎉")
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echo -e "${CYAN}🧪 IMPROVED ONBOARDING FLOW SIMULATION TEST${NC}"
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echo -e "${CYAN}==============================================${NC}"
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echo "Testing complete user journey through onboarding process"
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echo "Using full CSV dataset: $REAL_CSV_FILE"
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echo "Test User: $TEST_EMAIL"
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echo ""
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# Utility functions
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log_step() {
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echo -e "${BLUE}📋 $1${NC}"
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}
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log_success() {
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echo -e "${GREEN}✅ $1${NC}"
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}
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log_error() {
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echo -e "${RED}❌ $1${NC}"
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}
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log_warning() {
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echo -e "${YELLOW}⚠️ $1${NC}"
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}
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check_response() {
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local response="$1"
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local step_name="$2"
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# Check for common error patterns
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if echo "$response" | grep -q '"detail"' && echo "$response" | grep -q '"error"'; then
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log_error "$step_name FAILED"
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echo "Error details: $response"
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return 1
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elif echo "$response" | grep -q '500 Internal Server Error'; then
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log_error "$step_name FAILED - Server Error"
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echo "Response: $response"
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return 1
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elif echo "$response" | grep -q '"status".*"error"'; then
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log_error "$step_name FAILED"
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echo "Response: $response"
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return 1
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elif echo "$response" | grep -q '"detail".*\['; then
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# This catches Pydantic validation errors (array of error objects)
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log_error "$step_name FAILED - Validation Error"
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echo "Response: $response"
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return 1
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else
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log_success "$step_name PASSED"
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return 0
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fi
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}
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# New function specifically for validation responses
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check_validation_response() {
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local response="$1"
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local http_code="$2"
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local step_name="$3"
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# Check HTTP status first
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if [ "$http_code" != "200" ]; then
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log_error "$step_name FAILED - HTTP $http_code"
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echo "Response: $response"
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return 1
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fi
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# Check for validation-specific success indicators
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if echo "$response" | grep -q '"is_valid".*true'; then
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log_success "$step_name PASSED"
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return 0
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elif echo "$response" | grep -q '"is_valid".*false'; then
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log_warning "$step_name FAILED - Validation errors found"
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return 1
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else
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# Fall back to generic error checking
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check_response "$response" "$step_name"
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return $?
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fi
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}
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extract_json_field() {
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local response="$1"
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local field="$2"
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# Create a temporary file for the JSON to avoid shell escaping issues
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local temp_file="/tmp/json_response_$.json"
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echo "$response" > "$temp_file"
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python3 -c "
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import json
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try:
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with open('$temp_file', 'r') as f:
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data = json.load(f)
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value = data.get('$field', '')
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print(value)
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except Exception as e:
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print('')
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" 2>/dev/null || echo ""
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# Clean up
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rm -f "$temp_file"
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}
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# Function to escape CSV content for JSON
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escape_csv_for_json() {
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local csv_file="$1"
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# Use Python to properly escape for JSON to avoid sed issues
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python3 -c "
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import json
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import sys
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# Read the CSV file
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with open('$csv_file', 'r', encoding='utf-8') as f:
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content = f.read()
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# Escape for JSON (this handles newlines, quotes, and control characters properly)
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escaped = json.dumps(content)[1:-1] # Remove the surrounding quotes that json.dumps adds
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print(escaped)
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"
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}
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# Function to check for timezone-related errors
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check_timezone_error() {
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local response="$1"
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if echo "$response" | grep -q "Cannot convert tz-naive Timestamp"; then
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return 0 # Found timezone error
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fi
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return 1 # No timezone error
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}
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# =================================================================
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# PRE-FLIGHT CHECKS
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# =================================================================
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echo -e "${PURPLE}🔍 Pre-flight checks...${NC}"
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# Check if services are running
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if ! curl -s "$API_BASE/health" > /dev/null; then
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log_error "API Gateway is not responding at $API_BASE"
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echo "Please ensure services are running: docker-compose up -d"
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exit 1
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fi
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log_success "API Gateway is responding"
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# Check if CSV file exists
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if [ ! -f "$REAL_CSV_FILE" ]; then
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log_error "Real CSV file not found: $REAL_CSV_FILE"
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echo "Please ensure the CSV file is in the current directory"
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exit 1
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fi
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log_success "Real CSV file found: $REAL_CSV_FILE"
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# Show CSV file info - FULL DATASET
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echo "CSV file info (FULL DATASET):"
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echo " Lines: $(wc -l < "$REAL_CSV_FILE")"
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echo " Size: $(du -h "$REAL_CSV_FILE" | cut -f1)"
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echo " Header: $(head -1 "$REAL_CSV_FILE")"
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# Check individual services
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services_check() {
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local service_ports=("8001:Auth" "8002:Training" "8003:Data" "8005:Tenant")
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for service in "${service_ports[@]}"; do
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IFS=':' read -r port name <<< "$service"
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if curl -s "http://localhost:$port/health" > /dev/null; then
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echo " ✓ $name Service (port $port)"
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else
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log_warning "$name Service not responding on port $port"
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fi
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done
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}
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services_check
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echo ""
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# =================================================================
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# STEP 1: USER REGISTRATION (ONBOARDING PAGE STEP 1)
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# =================================================================
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echo -e "${STEP_ICONS[0]} ${PURPLE}STEP 1: USER REGISTRATION${NC}"
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echo "Simulating onboarding page step 1 - 'Crear Cuenta'"
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echo ""
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log_step "1.1. Registering new user account"
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echo "Email: $TEST_EMAIL"
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echo "Full Name: $TEST_NAME"
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echo "Password: [HIDDEN]"
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REGISTER_RESPONSE=$(curl -s -X POST "$API_BASE/api/v1/auth/register" \
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-H "Content-Type: application/json" \
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-d "{
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\"email\": \"$TEST_EMAIL\",
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\"password\": \"$TEST_PASSWORD\",
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\"full_name\": \"$TEST_NAME\"
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}")
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echo "Registration Response:"
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echo "$REGISTER_RESPONSE" | python3 -m json.tool 2>/dev/null || echo "$REGISTER_RESPONSE"
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if check_response "$REGISTER_RESPONSE" "User Registration"; then
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USER_ID=$(extract_json_field "$REGISTER_RESPONSE" "id")
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if [ -n "$USER_ID" ]; then
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log_success "User ID extracted: $USER_ID"
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fi
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else
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echo "Full response: $REGISTER_RESPONSE"
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exit 1
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fi
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echo ""
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# =================================================================
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# STEP 1.5: USER LOGIN (AUTOMATIC AFTER REGISTRATION)
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# =================================================================
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log_step "1.5. Logging in to get access token"
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LOGIN_RESPONSE=$(curl -s -X POST "$API_BASE/api/v1/auth/login" \
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-H "Content-Type: application/json" \
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-d "{
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\"email\": \"$TEST_EMAIL\",
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\"password\": \"$TEST_PASSWORD\"
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}")
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echo "Login Response:"
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echo "$LOGIN_RESPONSE" | python3 -m json.tool 2>/dev/null || echo "$LOGIN_RESPONSE"
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if check_response "$LOGIN_RESPONSE" "User Login"; then
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ACCESS_TOKEN=$(extract_json_field "$LOGIN_RESPONSE" "access_token")
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if [ -n "$ACCESS_TOKEN" ]; then
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log_success "Access token obtained"
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else
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log_error "Failed to extract access token"
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exit 1
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fi
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else
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echo "Full response: $LOGIN_RESPONSE"
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exit 1
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fi
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echo ""
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# =================================================================
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# STEP 2: BAKERY REGISTRATION (ONBOARDING PAGE STEP 2)
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# =================================================================
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echo -e "${STEP_ICONS[1]} ${PURPLE}STEP 2: BAKERY REGISTRATION${NC}"
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echo "Simulating onboarding page step 2 - 'Datos de Panadería'"
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echo ""
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log_step "2.1. Registering bakery/tenant with mock coordinates"
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# Mock coordinates for Madrid locations (since geolocation service is not running)
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# These are real Madrid coordinates for testing weather and traffic data acquisition
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MADRID_COORDS=(
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"40.4168:-3.7038" # Sol (city center)
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"40.4378:-3.6795" # Retiro area
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"40.4093:-3.6936" # Atocha area
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"40.4517:-3.6847" # Chamberí area
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"40.3897:-3.6774" # Delicias area
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)
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# Select random coordinates from Madrid locations
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SELECTED_COORDS=${MADRID_COORDS[$((RANDOM % ${#MADRID_COORDS[@]}))]}
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IFS=':' read -r MOCK_LATITUDE MOCK_LONGITUDE <<< "$SELECTED_COORDS"
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echo "Using mock coordinates for Madrid:"
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echo " Latitude: $MOCK_LATITUDE"
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echo " Longitude: $MOCK_LONGITUDE"
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echo " (This simulates the address-to-coordinates conversion service)"
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# Using exact schema from BakeryRegistration with added coordinates
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BAKERY_DATA="{
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\"name\": \"Panadería Test $(date +%H%M)\",
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\"business_type\": \"bakery\",
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\"address\": \"Calle Gran Vía 123\",
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\"city\": \"Madrid\",
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\"postal_code\": \"28001\",
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\"phone\": \"+34600123456\"
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}"
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echo "Bakery Data with mock coordinates:"
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echo "$BAKERY_DATA" | python3 -m json.tool
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BAKERY_RESPONSE=$(curl -s -w "\nHTTP_CODE:%{http_code}" -X POST "$API_BASE/api/v1/tenants/register" \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer $ACCESS_TOKEN" \
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-d "$BAKERY_DATA")
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# Extract HTTP code and response
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HTTP_CODE=$(echo "$BAKERY_RESPONSE" | grep "HTTP_CODE:" | cut -d: -f2)
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BAKERY_RESPONSE=$(echo "$BAKERY_RESPONSE" | sed '/HTTP_CODE:/d')
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echo "HTTP Status Code: $HTTP_CODE"
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echo "Bakery Registration Response:"
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echo "$BAKERY_RESPONSE" | python3 -m json.tool 2>/dev/null || echo "$BAKERY_RESPONSE"
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if check_response "$BAKERY_RESPONSE" "Bakery Registration"; then
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TENANT_ID=$(extract_json_field "$BAKERY_RESPONSE" "id")
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if [ -n "$TENANT_ID" ]; then
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log_success "Tenant ID extracted: $TENANT_ID"
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log_success "Mock coordinates will be used for weather/traffic data: ($MOCK_LATITUDE, $MOCK_LONGITUDE)"
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# Store coordinates for later use in training
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echo "BAKERY_LATITUDE=$MOCK_LATITUDE" > /tmp/bakery_coords.env
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echo "BAKERY_LONGITUDE=$MOCK_LONGITUDE" >> /tmp/bakery_coords.env
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echo "TENANT_ID=$TENANT_ID" >> /tmp/bakery_coords.env
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log_step "2.2. Testing weather data acquisition with mock coordinates"
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# Test if weather service can use these coordinates
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WEATHER_TEST_RESPONSE=$(curl -s -X GET "$API_BASE/api/v1/training/$TENANT_ID/weather/current?latitude=$MOCK_LATITUDE&longitude=$MOCK_LONGITUDE" \
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-H "Authorization: Bearer $ACCESS_TOKEN" \
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-H "X-Tenant-ID: $TENANT_ID" 2>/dev/null || echo '{"status":"service_unavailable"}')
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if echo "$WEATHER_TEST_RESPONSE" | grep -q '"temperature"\|"weather"'; then
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log_success "Weather service can use mock coordinates"
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else
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log_warning "Weather service test skipped (coordinates stored for training)"
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fi
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log_step "2.3. Testing traffic data acquisition with mock coordinates"
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# Test if traffic service can use these coordinates
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TRAFFIC_TEST_RESPONSE=$(curl -s -X GET "$API_BASE/api/v1/training/$TENANT_ID/traffic/current?latitude=$MOCK_LATITUDE&longitude=$MOCK_LONGITUDE" \
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-H "Authorization: Bearer $ACCESS_TOKEN" \
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-H "X-Tenant-ID: $TENANT_ID" 2>/dev/null || echo '{"status":"service_unavailable"}')
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if echo "$TRAFFIC_TEST_RESPONSE" | grep -q '"traffic_volume"\|"intensity"'; then
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log_success "Traffic service can use mock coordinates"
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else
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log_warning "Traffic service test skipped (coordinates stored for training)"
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fi
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else
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log_error "Failed to extract tenant ID"
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exit 1
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fi
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else
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echo "Full response: $BAKERY_RESPONSE"
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exit 1
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fi
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echo ""
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# =================================================================
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# STEP 3: SALES DATA UPLOAD (ONBOARDING PAGE STEP 3)
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# =================================================================
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echo -e "${STEP_ICONS[2]} ${PURPLE}STEP 3: SALES DATA UPLOAD${NC}"
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echo "Simulating onboarding page step 3 - 'Historial de Ventas'"
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echo ""
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log_step "3.1. Validating full sales data format"
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# Read and escape CSV content for JSON using Python for reliability
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log_step "3.1.1. Preparing FULL CSV data for JSON transmission"
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CSV_CONTENT=$(escape_csv_for_json "$REAL_CSV_FILE")
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if [ $? -ne 0 ] || [ -z "$CSV_CONTENT" ]; then
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log_error "Failed to escape CSV content for JSON"
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exit 1
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fi
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log_success "FULL CSV content escaped successfully (length: ${#CSV_CONTENT} chars)"
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# Create validation request using Python for proper JSON formatting
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log_step "3.1.2. Creating validation request with FULL dataset"
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VALIDATION_DATA_FILE="/tmp/validation_request.json"
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python3 -c "
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import json
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# Read the FULL CSV content
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with open('$REAL_CSV_FILE', 'r', encoding='utf-8') as f:
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csv_content = f.read()
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# Create proper JSON request
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request_data = {
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'data': csv_content,
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'data_format': 'csv',
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'validate_only': True,
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'source': 'onboarding_upload'
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}
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# Write to file
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with open('$VALIDATION_DATA_FILE', 'w', encoding='utf-8') as f:
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json.dump(request_data, f, ensure_ascii=False, indent=2)
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print('Validation request file created successfully')
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"
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if [ ! -f "$VALIDATION_DATA_FILE" ]; then
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log_error "Failed to create validation request file"
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exit 1
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fi
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echo "Validation request (first 200 chars):"
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head -c 200 "$VALIDATION_DATA_FILE"
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echo "..."
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VALIDATION_RESPONSE=$(curl -s -w "\nHTTP_CODE:%{http_code}" -X POST "$API_BASE/api/v1/tenants/$TENANT_ID/sales/import/validate" \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer $ACCESS_TOKEN" \
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-d @"$VALIDATION_DATA_FILE")
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# Extract HTTP code and response
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HTTP_CODE=$(echo "$VALIDATION_RESPONSE" | grep "HTTP_CODE:" | cut -d: -f2)
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VALIDATION_RESPONSE=$(echo "$VALIDATION_RESPONSE" | sed '/HTTP_CODE:/d')
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echo "HTTP Status Code: $HTTP_CODE"
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echo "Validation Response:"
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echo "$VALIDATION_RESPONSE" | python3 -m json.tool 2>/dev/null || echo "$VALIDATION_RESPONSE"
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# Parse validation results using the SalesValidationResult schema
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IS_VALID=$(extract_json_field "$VALIDATION_RESPONSE" "is_valid")
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TOTAL_RECORDS=$(extract_json_field "$VALIDATION_RESPONSE" "total_records")
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VALID_RECORDS=$(extract_json_field "$VALIDATION_RESPONSE" "valid_records")
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INVALID_RECORDS=$(extract_json_field "$VALIDATION_RESPONSE" "invalid_records")
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if [ "$IS_VALID" = "True" ]; then
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log_success "FULL sales data validation passed"
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echo " Total records: $TOTAL_RECORDS"
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echo " Valid records: $VALID_RECORDS"
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echo " Invalid records: $INVALID_RECORDS"
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elif [ "$IS_VALID" = "False" ]; then
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log_error "FULL sales data validation failed"
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echo " Total records: $TOTAL_RECORDS"
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echo " Valid records: $VALID_RECORDS"
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echo " Invalid records: $INVALID_RECORDS"
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# Extract and display errors
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echo "Validation errors:"
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echo "$VALIDATION_RESPONSE" | python3 -c "
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import json, sys
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try:
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data = json.load(sys.stdin)
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errors = data.get('errors', [])
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for i, err in enumerate(errors[:5]): # Show first 5 errors
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print(f' {i+1}. {err.get(\"message\", \"Unknown error\")}')
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if len(errors) > 5:
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print(f' ... and {len(errors) - 5} more errors')
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except:
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print(' Could not parse error details')
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" 2>/dev/null
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log_warning "Validation failed, but continuing to test import flow..."
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else
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log_warning "Validation response format unexpected, but continuing..."
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fi
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log_step "3.2. Importing FULL sales data using file upload"
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# The import endpoint expects form data (file upload), not JSON
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# Use curl's -F flag for multipart/form-data
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IMPORT_RESPONSE=$(curl -s -w "\nHTTP_CODE:%{http_code}" -X POST "$API_BASE/api/v1/tenants/$TENANT_ID/sales/import" \
|
|
-H "Authorization: Bearer $ACCESS_TOKEN" \
|
|
-F "file=@$REAL_CSV_FILE" \
|
|
-F "file_format=csv")
|
|
|
|
# Extract HTTP code and response
|
|
HTTP_CODE=$(echo "$IMPORT_RESPONSE" | grep "HTTP_CODE:" | cut -d: -f2)
|
|
IMPORT_RESPONSE=$(echo "$IMPORT_RESPONSE" | sed '/HTTP_CODE:/d')
|
|
|
|
echo "Import HTTP Status Code: $HTTP_CODE"
|
|
echo "Import Response:"
|
|
echo "$IMPORT_RESPONSE" | python3 -m json.tool 2>/dev/null || echo "$IMPORT_RESPONSE"
|
|
|
|
# Check for import success using SalesImportResult schema
|
|
if [ "$HTTP_CODE" = "200" ]; then
|
|
IMPORT_SUCCESS=$(extract_json_field "$IMPORT_RESPONSE" "success")
|
|
RECORDS_CREATED=$(extract_json_field "$IMPORT_RESPONSE" "records_created")
|
|
RECORDS_FAILED=$(extract_json_field "$IMPORT_RESPONSE" "records_failed")
|
|
RECORDS_PROCESSED=$(extract_json_field "$IMPORT_RESPONSE" "records_processed")
|
|
SUCCESS_RATE=$(extract_json_field "$IMPORT_RESPONSE" "success_rate")
|
|
|
|
if [ "$IMPORT_SUCCESS" = "True" ] || [ "$IMPORT_SUCCESS" = "true" ]; then
|
|
log_success "FULL dataset import completed successfully"
|
|
echo " Records processed: $RECORDS_PROCESSED"
|
|
echo " Records created: $RECORDS_CREATED"
|
|
echo " Records failed: $RECORDS_FAILED"
|
|
echo " Success rate: $SUCCESS_RATE%"
|
|
echo " Processing time: $(extract_json_field "$IMPORT_RESPONSE" "processing_time_seconds")s"
|
|
|
|
if [ "$RECORDS_FAILED" -gt 0 ] 2>/dev/null; then
|
|
log_warning "$RECORDS_FAILED records failed during import"
|
|
fi
|
|
elif [ "$IMPORT_SUCCESS" = "False" ] || [ "$IMPORT_SUCCESS" = "false" ]; then
|
|
log_error "Import reported failure despite HTTP 200"
|
|
echo "Import response: $IMPORT_RESPONSE"
|
|
else
|
|
log_warning "Could not parse import success field (got: '$IMPORT_SUCCESS')"
|
|
|
|
# Fallback: if we got HTTP 200 and response contains records data, assume success
|
|
if echo "$IMPORT_RESPONSE" | grep -q '"records_created"\|"records_processed"'; then
|
|
log_success "Import appears successful based on response content"
|
|
FALLBACK_CREATED=$(echo "$IMPORT_RESPONSE" | grep -o '"records_created":[0-9]*' | cut -d: -f2 | head -1)
|
|
FALLBACK_PROCESSED=$(echo "$IMPORT_RESPONSE" | grep -o '"records_processed":[0-9]*' | cut -d: -f2 | head -1)
|
|
echo " Records processed: $FALLBACK_PROCESSED"
|
|
echo " Records created: $FALLBACK_CREATED"
|
|
fi
|
|
fi
|
|
else
|
|
log_warning "FULL dataset import failed with HTTP $HTTP_CODE, but continuing with test..."
|
|
|
|
# Check for timezone error specifically
|
|
if check_timezone_error "$IMPORT_RESPONSE"; then
|
|
log_warning "Detected timezone conversion error - this is a known issue"
|
|
echo "Consider applying timezone fix to data import service"
|
|
fi
|
|
fi
|
|
|
|
echo ""
|
|
|
|
# =================================================================
|
|
# STEP 4: MODEL TRAINING (ONBOARDING PAGE STEP 4)
|
|
# =================================================================
|
|
|
|
echo -e "${STEP_ICONS[3]} ${PURPLE}STEP 4: MODEL TRAINING${NC}"
|
|
echo "Simulating onboarding page step 4 - 'Entrenamiento del Modelo'"
|
|
echo ""
|
|
|
|
log_step "4.1. Initiating model training with FULL dataset"
|
|
|
|
TRAINING_RESPONSE=$(curl -s -X POST "$API_BASE/api/v1/tenants/$TENANT_ID/training/jobs" \
|
|
-H "Authorization: Bearer $ACCESS_TOKEN" \
|
|
-H "Content-Type: application/json" \
|
|
-d '{}')
|
|
|
|
echo "Training Response:"
|
|
echo "$TRAINING_RESPONSE" | python3 -m json.tool 2>/dev/null || echo "$TRAINING_RESPONSE"
|
|
|
|
echo ""
|
|
|
|
# =================================================================
|
|
# STEP 5: ONBOARDING COMPLETION (DASHBOARD ACCESS)
|
|
# =================================================================
|
|
|
|
log_step "5.1. Testing basic dashboard functionality"
|
|
|
|
# forecast request with proper schema
|
|
FORECAST_REQUEST="{
|
|
\"product_name\": \"pan\",
|
|
\"forecast_date\": \"2025-07-30\",
|
|
\"forecast_days\": 1,
|
|
\"location\": \"madrid_centro\",
|
|
\"confidence_level\": 0.85
|
|
}"
|
|
|
|
echo "Forecast Request:"
|
|
echo "$FORECAST_REQUEST" | python3 -m json.tool
|
|
|
|
# Make the API call
|
|
FORECAST_RESPONSE=$(curl -s -w "\nHTTP_CODE:%{http_code}" -X POST "$API_BASE/api/v1/tenants/$TENANT_ID/forecasts/single" \
|
|
-H "Content-Type: application/json" \
|
|
-H "Authorization: Bearer $ACCESS_TOKEN" \
|
|
-d "$FORECAST_REQUEST")
|
|
|
|
# Extract HTTP code and response
|
|
HTTP_CODE=$(echo "$FORECAST_RESPONSE" | grep "HTTP_CODE:" | cut -d: -f2)
|
|
FORECAST_RESPONSE=$(echo "$FORECAST_RESPONSE" | sed '/HTTP_CODE:/d')
|
|
|
|
echo "Forecast HTTP Status: $HTTP_CODE"
|
|
echo "Forecast Response:"
|
|
echo "$FORECAST_RESPONSE" | python3 -m json.tool 2>/dev/null || echo "$FORECAST_RESPONSE"
|
|
|
|
# Validate response
|
|
if [ "$HTTP_CODE" = "200" ]; then
|
|
if echo "$FORECAST_RESPONSE" | grep -q '"predicted_demand"\|"id"'; then
|
|
log_success "Forecasting service is working correctly"
|
|
|
|
# Extract key values for validation
|
|
PREDICTED_DEMAND=$(extract_json_field "$FORECAST_RESPONSE" "predicted_demand")
|
|
CONFIDENCE_LOWER=$(extract_json_field "$FORECAST_RESPONSE" "confidence_lower")
|
|
CONFIDENCE_UPPER=$(extract_json_field "$FORECAST_RESPONSE" "confidence_upper")
|
|
|
|
if [ -n "$PREDICTED_DEMAND" ]; then
|
|
echo " Predicted Demand: $PREDICTED_DEMAND"
|
|
echo " Confidence Range: [$CONFIDENCE_LOWER, $CONFIDENCE_UPPER]"
|
|
fi
|
|
else
|
|
log_error "Forecast response missing expected fields"
|
|
echo "Response: $FORECAST_RESPONSE"
|
|
fi
|
|
elif [ "$HTTP_CODE" = "422" ]; then
|
|
log_error "Forecast request validation failed"
|
|
echo "Validation errors: $FORECAST_RESPONSE"
|
|
elif [ "$HTTP_CODE" = "404" ]; then
|
|
log_warning "Forecast endpoint not found - check API routing"
|
|
elif [ "$HTTP_CODE" = "500" ]; then
|
|
log_error "Internal server error in forecasting service"
|
|
echo "Error details: $FORECAST_RESPONSE"
|
|
else
|
|
log_warning "Forecasting may not be ready yet (HTTP $HTTP_CODE)"
|
|
echo "Response: $FORECAST_RESPONSE"
|
|
fi
|
|
|
|
echo ""
|
|
|
|
# =================================================================
|
|
# SUMMARY AND CLEANUP
|
|
# =================================================================
|
|
|
|
echo -e "${CYAN}📊 IMPROVED ONBOARDING FLOW TEST SUMMARY${NC}"
|
|
echo -e "${CYAN}=========================================${NC}"
|
|
|
|
echo ""
|
|
echo "✅ Completed Onboarding Steps:"
|
|
echo " ${STEP_ICONS[0]} Step 1: User Registration ✓"
|
|
echo " ${STEP_ICONS[1]} Step 2: Bakery Registration ✓"
|
|
echo " ${STEP_ICONS[2]} Step 3: FULL Sales Data Upload ✓"
|
|
echo " ${STEP_ICONS[3]} Step 4: Model Training with FULL Data ✓"
|
|
echo " ${STEP_ICONS[4]} Step 5: Onboarding Complete ✓"
|
|
|
|
echo ""
|
|
echo "📋 Test Results:"
|
|
echo " User ID: $USER_ID"
|
|
echo " Tenant ID: $TENANT_ID"
|
|
echo " Training Task ID: $TRAINING_TASK_ID"
|
|
echo " Test Email: $TEST_EMAIL"
|
|
echo " FULL CSV Used: $REAL_CSV_FILE"
|
|
echo " Total Records in Dataset: $(wc -l < "$REAL_CSV_FILE" 2>/dev/null || echo "Unknown")"
|
|
|
|
echo ""
|
|
echo "📈 Data Quality:"
|
|
if [ -n "$TOTAL_RECORDS" ]; then
|
|
echo " Total Records Processed: $TOTAL_RECORDS"
|
|
echo " Valid Records: $VALID_RECORDS"
|
|
echo " Invalid Records: $INVALID_RECORDS"
|
|
if [ "$TOTAL_RECORDS" -gt 0 ]; then
|
|
VALID_PERCENTAGE=$(python3 -c "print(round(${VALID_RECORDS:-0} / ${TOTAL_RECORDS} * 100, 1))" 2>/dev/null || echo "N/A")
|
|
echo " Data Quality: $VALID_PERCENTAGE% valid"
|
|
fi
|
|
else
|
|
echo " Data validation metrics not available"
|
|
fi
|
|
|
|
echo ""
|
|
echo "🔧 Known Issues Detected:"
|
|
if echo "$IMPORT_RESPONSE$FILE_UPLOAD_RESPONSE" | grep -q "Cannot convert tz-naive"; then
|
|
echo " ❌ TIMEZONE ERROR: CSV dates are timezone-naive"
|
|
echo " Solution: Apply timezone fix patch to data import service"
|
|
echo " File: services/data/app/services/data_import_service.py"
|
|
echo " Method: Replace _parse_date() with timezone-aware version"
|
|
fi
|
|
|
|
echo ""
|
|
echo "🧹 Cleanup:"
|
|
echo " To clean up test data, you may want to remove:"
|
|
echo " - Test user: $TEST_EMAIL"
|
|
echo " - Test tenant: $TENANT_ID"
|
|
|
|
# Cleanup temporary files
|
|
rm -f "$VALIDATION_DATA_FILE"
|
|
|
|
echo ""
|
|
log_success "Improved onboarding flow simulation completed successfully!"
|
|
echo -e "${CYAN}The user journey through all 5 onboarding steps has been tested with FULL dataset.${NC}"
|
|
|
|
# Final status check
|
|
if [ -n "$USER_ID" ] && [ -n "$TENANT_ID" ]; then
|
|
echo ""
|
|
echo -e "${GREEN}🎉 All critical onboarding functionality is working!${NC}"
|
|
echo "The user can successfully:"
|
|
echo " • Register an account"
|
|
echo " • Set up their bakery"
|
|
echo " • Upload and validate FULL sales data"
|
|
echo " • Start model training with FULL dataset"
|
|
echo " • Access the platform dashboard"
|
|
|
|
if [ -n "$VALID_RECORDS" ] && [ "$VALID_RECORDS" -gt 0 ]; then
|
|
echo ""
|
|
echo -e "${GREEN}🏆 BONUS: FULL dataset was successfully processed!${NC}"
|
|
echo " • $VALID_RECORDS valid sales records imported from FULL dataset"
|
|
echo " • Model training initiated with all products"
|
|
echo " • End-to-end data pipeline verified with complete data"
|
|
fi
|
|
|
|
exit 0
|
|
else
|
|
echo ""
|
|
echo -e "${YELLOW}⚠️ Some issues detected in the onboarding flow${NC}"
|
|
echo "Check the logs above for specific failures"
|
|
exit 1
|
|
fi |