Endpoint

Overview Features

The overview endpoint gives you a bird's-eye view of your plagiarism check results, perfect for dashboards, reports, and quick analysis. It focuses on peer-to-peer comparisons between your uploaded submissions.

What You'll Get

• Summary Dashboard Data: Ready-to-use data for charts and visualizations
• Submission List: All uploaded files with their highest similarity scores
• Peer Comparisons: Detailed comparison matrix between submissions
• Chart Data: Pre-formatted data for bar charts and graphs
• Dashboard Link: Direct URL to view results in Codequiry dashboard

Request Parameters

Simple request requiring only your completed check ID to retrieve the overview data.

Request Examples

                curl -X POST \
  'https://codequiry.com/api/v1/check/overview?check_id=2810' \
  -H 'Accept: application/json' \
  -H 'apikey:  YOUR_API_KEY_HERE '
                

                    

                        JSON Format
                    
                
            

                curl -X POST \
  'https://codequiry.com/api/v1/check/overviewCSV?check_id=2810' \
  -H 'Accept: text/csv' \
  -H 'apikey:  YOUR_API_KEY_HERE '
                

                    

                        CSV Format
                    
                
            

JSON Response

        {
    "overviewURL": "https://dashboard.codequiry.com/course/api/assignment/2810/overview",
    "submissions": [
        {
            "id": 14587,
            "filename": "student1_assignment",
            "status_id": 4,
            "created_at": "2024-01-15 14:35:22",
            "updated_at": "2024-01-15 14:45:18",
            "result1": "12.50",
            "result2": "8.75",
            "result3": "15.20",
            "total_result": "85.45",
            "submissionresults": [
                {
                    "id": 463587,
                    "submission_id": 14587,
                    "submission_id_compared": 14588,
                    "score": "23.80",
                    "created_at": null,
                    "updated_at": null
                },
                {
                    "id": 463588,
                    "submission_id": 14587,
                    "submission_id_compared": 14589,
                    "score": "85.45",
                    "created_at": null,
                    "updated_at": null
                }
            ]
        },
        {
            "id": 14588,
            "filename": "student2_assignment",
            "status_id": 4,
            "created_at": "2024-01-15 14:35:22",
            "updated_at": "2024-01-15 14:45:18",
            "result1": "5.25",
            "result2": "3.80",
            "result3": "7.10",
            "total_result": "23.80",
            "submissionresults": [
                {
                    "id": 463589,
                    "submission_id": 14588,
                    "submission_id_compared": 14589,
                    "score": "12.30",
                    "created_at": null,
                    "updated_at": null
                }
            ]
        }
    ],
    "bardata": [
        {
            "submission": "student1_assignment",
            "score": "85.45"
        },
        {
            "submission": "student2_assignment",
            "score": "23.80"
        }
    ]
}
        

            

                JSON Response
            
        
    

Response Data Structure

Main Response Fields

Submission Object Fields

Understanding Similarity Scores

Data Visualization Examples

Using the bardata for Charts

The bardata array is perfectly formatted for creating visualizations:

                // Extract labels and data from bardata
const labels = data.bardata.map(item => item.submission);
const scores = data.bardata.map(item => parseFloat(item.score));

// Create bar chart
new Chart(ctx, {
    type: 'bar',
    data: {
        labels: labels,
        datasets: [{
            label: 'Similarity Score (%)',
            data: scores,
            backgroundColor: scores.map(score =>
                score > 70 ? '#dc3545' :
                score > 30 ? '#ffc107' : '#28a745'
            )
        }]
    }
});
                

                    

                        Chart.js
                    
                
            

                import matplotlib.pyplot as plt

# Extract data from API response
submissions = [item['submission'] for item in bardata]
scores = [float(item['score']) for item in bardata]

# Create color-coded bar chart
colors = ['red' if score > 70 else 'orange' if score > 30 else 'green' for score in scores]

plt.figure(figsize=(10, 6))
plt.bar(submissions, scores, color=colors)
plt.ylabel('Similarity Score (%)')
plt.title('Plagiarism Detection Results')
plt.xticks(rotation=45)
plt.show()
                

                    

                        Python
                    
                
            

Data Analysis Examples

The JSON response provides rich data that can be processed for various analytical purposes:

JavaScript Analysis Example

        // Analyze overview data for insights
function analyzeResults(overview) {
    const submissions = overview.submissions;

    // Calculate risk distribution
    const riskDistribution = {
        high: submissions.filter(s => parseFloat(s.total_result) > 70).length,
        medium: submissions.filter(s => parseFloat(s.total_result) > 30 && parseFloat(s.total_result) <= 70).length,
        low: submissions.filter(s => parseFloat(s.total_result) <= 30).length
    };

    // Find most similar pair
    let maxSimilarity = 0;
    let similarPair = null;

    overview.bardata.forEach(comparison => {
        if (comparison.similarity > maxSimilarity) {
            maxSimilarity = comparison.similarity;
            similarPair = comparison;
        }
    });

    return { riskDistribution, similarPair, maxSimilarity };
}
        

            

                Analysis Function
            
        
    

Error Responses & Special Cases

Check Not Completed (422 Unprocessable Entity)

        {
    "error": "The check has not been completed."
}
        

            

                Error Response
            
        
    

Special Score Values

Best Practices

Next Steps

With overview data in hand, dive deeper into specific plagiarism cases: