mirror of
https://github.com/thecyberlearn/quantum-ai-v2.git
synced 2026-08-18 18:32:59 +00:00
Fix Data Analyzer wallet balance persistence issue
Replace demo JavaScript implementation with real backend processing: - Remove fake analyzeData() function that only updated frontend - Add real AJAX submission to /agents/data-analyzer/process/ endpoint - Implement proper polling for results using request ID - Add displayResults() function to handle backend responses - Update wallet balance from server response to ensure persistence - Remove generateSampleAnalysis() demo function - Fix wallet balance to persist across page navigation This resolves the issue where wallet deduction would revert when navigating to other pages, as the frontend was using demo mode instead of actual database transactions. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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Claude
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@ -577,14 +577,31 @@
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return;
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}
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// Check user authentication
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{% if not user.is_authenticated %}
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window.location.href = "{% url 'authentication:login' %}";
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return;
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{% endif %}
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// Check wallet balance
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const balance = {{ user.wallet_balance|default:0 }};
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if (balance < 5.00) {
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showToast('Insufficient balance! You need 5.00 AED.', 'error');
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setTimeout(() => {
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window.location.href = "{% url 'core:wallet' %}";
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}, 2000);
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return;
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}
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const analysisType = document.querySelector('input[name="analysisType"]:checked').value;
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// Show processing status
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document.getElementById('processingStatus').style.display = 'block';
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document.getElementById('processButton').disabled = true;
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document.getElementById('processButton').innerHTML = '⏳ Processing...';
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document.getElementById('analysisResults').style.display = 'none';
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// Simulate analysis steps
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// Processing steps for user feedback
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const steps = [
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'Reading file structure...',
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'Extracting data patterns...',
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@ -600,142 +617,120 @@
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currentStep++;
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} else {
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clearInterval(stepInterval);
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completeAnalysis(analysisType);
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}
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}, 1000);
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// Submit form data to backend
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const formData = new FormData();
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formData.append('file', selectedFile);
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formData.append('analysis_type', analysisType);
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formData.append('csrfmiddlewaretoken', document.querySelector('[name=csrfmiddlewaretoken]').value);
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fetch('/agents/data-analyzer/process/', {
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method: 'POST',
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body: formData,
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headers: {
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'X-Requested-With': 'XMLHttpRequest'
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}
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})
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.then(response => response.json())
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.then(result => {
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clearInterval(stepInterval);
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if (result.success && result.request_id) {
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// Start polling for results
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pollForResults(result.request_id);
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} else {
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// Handle immediate response
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document.getElementById('processingStatus').style.display = 'none';
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document.getElementById('processButton').disabled = false;
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document.getElementById('processButton').innerHTML = '📊 Analyze Data (5.00 AED)';
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if (result.error) {
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showToast(`❌ ${result.error}`, 'error');
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} else {
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displayResults(result);
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}
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}
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})
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.catch(error => {
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clearInterval(stepInterval);
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console.error('Error:', error);
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document.getElementById('processingStatus').style.display = 'none';
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document.getElementById('processButton').disabled = false;
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document.getElementById('processButton').innerHTML = '📊 Analyze Data (5.00 AED)';
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showToast('❌ Network error - please try again', 'error');
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});
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}
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// Display analysis results
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function displayResults(result) {
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const resultsContainer = document.getElementById('analysisResults');
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const contentContainer = document.getElementById('analysisContent');
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if (result.success && result.status === 'completed') {
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// Use the analysis content from backend
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const content = result.report_text || result.analysis_results || result.insights_summary || 'Data analysis completed successfully!';
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contentContainer.textContent = content;
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resultsContainer.style.display = 'block';
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// Update wallet balance if provided
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if (result.wallet_balance !== undefined) {
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updateWalletBalance(result.wallet_balance);
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}
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showToast('✅ Data analysis completed and payment processed!', 'success');
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} else {
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showToast('❌ Failed to analyze data - no charge applied', 'error');
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}
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}
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// Poll for results
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function pollForResults(requestId) {
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let pollCount = 0;
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const maxPolls = 60; // 60 seconds maximum for data analysis
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const pollInterval = setInterval(() => {
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pollCount++;
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fetch(`/agents/data-analyzer/result/${requestId}/`)
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.then(response => response.json())
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.then(result => {
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if (result.status === 'completed' || result.status === 'failed') {
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clearInterval(pollInterval);
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document.getElementById('processingStatus').style.display = 'none';
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document.getElementById('processButton').disabled = false;
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document.getElementById('processButton').innerHTML = '📊 Analyze Data (5.00 AED)';
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displayResults(result);
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} else if (pollCount >= maxPolls) {
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clearInterval(pollInterval);
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document.getElementById('processingStatus').style.display = 'none';
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document.getElementById('processButton').disabled = false;
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document.getElementById('processButton').innerHTML = '📊 Analyze Data (5.00 AED)';
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showToast('❌ Processing timeout - please try again', 'error');
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}
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})
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.catch(error => {
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console.error('Error polling results:', error);
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if (pollCount >= maxPolls) {
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clearInterval(pollInterval);
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document.getElementById('processingStatus').style.display = 'none';
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document.getElementById('processButton').disabled = false;
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document.getElementById('processButton').innerHTML = '📊 Analyze Data (5.00 AED)';
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showToast('❌ Network error - please try again', 'error');
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}
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});
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}, 1000);
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}
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function completeAnalysis(analysisType) {
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// Generate sample analysis report
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const analysisReport = generateSampleAnalysis(analysisType);
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// Display results
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document.getElementById('analysisContent').textContent = analysisReport;
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document.getElementById('processingStatus').style.display = 'none';
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document.getElementById('analysisResults').style.display = 'block';
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// Reset button
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document.getElementById('processButton').disabled = false;
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document.getElementById('processButton').innerHTML = '📊 Analyze Data (5.00 AED)';
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// Update wallet balance (demo)
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const currentBalance = parseFloat('{{ user.wallet_balance|floatformat:2 }}');
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updateWalletBalance(currentBalance - 5.00);
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showToast('Data analysis complete! 5.00 AED used.', 'success');
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}
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function generateSampleAnalysis(type) {
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const fileName = selectedFile.name;
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const baseReport = `📊 Data Analysis Report - ${fileName}
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File Information:
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- Name: ${fileName}
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- Size: ${formatFileSize(selectedFile.size)}
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- Type: ${selectedFile.type || 'Unknown'}
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- Processed: ${new Date().toLocaleString()}
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`;
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if (type === 'summary') {
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return baseReport + `Summary Analysis Results:
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Key Findings:
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• Data contains 1,247 records across 8 columns
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• 94% data completeness rate
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• 3 potential outliers identified
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• Strong correlation (0.83) between variables A and B
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• Trending pattern shows 15% increase over time period
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Recommendations:
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• Clean missing data in columns 3 and 7
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• Investigate outlier values for data quality
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• Consider seasonal adjustments for trend analysis
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Generated by NetCop AI Data Analyzer Agent`;
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}
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if (type === 'detailed') {
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return baseReport + `Detailed Analysis Results:
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Data Quality Assessment:
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• Missing Values: 6% (73 records)
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• Duplicate Records: 2% (25 records)
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• Outliers Detected: 3 records beyond 3σ threshold
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• Data Types: 5 numeric, 2 categorical, 1 datetime
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Statistical Summary:
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• Mean: 45.67 ± 12.34
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• Median: 43.21
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• Mode: 42.00
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• Range: 15.5 - 89.3
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• Skewness: 0.23 (slight right skew)
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• Kurtosis: -0.45 (platykurtic distribution)
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Correlation Analysis:
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• Variable A ↔ Variable B: 0.83 (strong positive)
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• Variable C ↔ Variable D: -0.67 (moderate negative)
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• Variable E ↔ Variable F: 0.12 (weak positive)
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Trend Analysis:
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• Linear trend: y = 2.3x + 18.5 (R² = 0.76)
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• Seasonal component detected (quarterly pattern)
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• 15% overall growth trend identified
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Generated by NetCop AI Data Analyzer Agent`;
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}
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if (type === 'statistical') {
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return baseReport + `Advanced Statistical Analysis:
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Descriptive Statistics:
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• Count: 1,247 observations
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• Mean: 45.67 ± 12.34 (95% CI: 44.98 - 46.36)
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• Median: 43.21
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• Standard Deviation: 12.34
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• Variance: 152.48
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• Coefficient of Variation: 27.02%
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Distribution Analysis:
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• Normality Test (Shapiro-Wilk): W = 0.987, p = 0.043
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• Distribution appears approximately normal with slight skew
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• Outliers: 3 values > 3σ (flagged for review)
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Hypothesis Testing:
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• T-test vs. baseline: t = 3.45, p = 0.0006 (significant)
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• ANOVA across groups: F = 12.67, p < 0.001 (significant)
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• Chi-square test: χ² = 23.45, p = 0.012 (significant)
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Regression Analysis:
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• R-squared: 0.762 (76.2% variance explained)
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• Adjusted R-squared: 0.758
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• F-statistic: 234.67, p < 0.001
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• Durbin-Watson: 1.98 (no autocorrelation)
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Model Coefficients:
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• Intercept: 18.5 ± 2.1 (p < 0.001)
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• Slope: 2.3 ± 0.3 (p < 0.001)
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• Residual Standard Error: 4.67
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Time Series Analysis:
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• Trend: Increasing (slope = 0.023/month)
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• Seasonality: Quarterly pattern detected
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• Autocorrelation: Significant at lags 1, 4, 12
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• Forecast accuracy: MAPE = 8.3%
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Generated by NetCop AI Data Analyzer Agent`;
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}
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return baseReport + 'Analysis complete.';
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}
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function updateWalletBalance(newBalance) {
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document.getElementById('walletBalance').textContent = newBalance.toFixed(2) + ' AED';
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// Update header balance if exists
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const headerBalance = document.querySelector('[data-wallet-balance]');
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if (headerBalance) {
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headerBalance.textContent = `💰 ${newBalance.toFixed(2)} AED`;
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}
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// Update wallet balance display
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const balanceElements = document.querySelectorAll('[data-wallet-balance]');
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balanceElements.forEach(element => {
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element.textContent = `${newBalance.toFixed(2)} AED`;
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});
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window.currentWalletBalance = newBalance;
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}
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function copyAnalysisReport() {
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