ayankumar/SDLC-Assistant-MultiAgent

1# Code Agent Output2 3## High-Resolution Visualization Export - Backend Core Logic4 5This document outlines the backend implementation for the high-resolution visualization export feature.  We prioritize clarity, modularity, and testability.6 7**1. Actual Code Implementation (Python with Flask)**8 9```python10import io11import base6412from flask import Flask, request, jsonify13from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas14from matplotlib.figure import Figure15import matplotlib.pyplot as plt # Only for example plots, replace with your actual plotting library16#import pandas as pd # Only if you're using pandas for data manipulation17 18 19app = Flask(__name__)20 21# Replace this with your actual data fetching and visualization logic22def generate_visualization(data):23    """Generates a matplotlib figure based on input data.  Replace with your actual visualization logic"""24    fig = Figure()25    ax = fig.add_subplot(111)26    # Example: Simple bar chart27    ax.bar([1,2,3], [data['a'], data['b'], data['c']]) #Replace with your data structure28    ax.set_title("Sample Chart")29    return fig30 31def export_visualization(fig, fmt, dpi=300):32    """Exports the visualization to the specified format."""33    buf = io.BytesIO()34    if fmt == "png":35        fig.savefig(buf, format="png", dpi=dpi)36    elif fmt == "pdf":37        fig.savefig(buf, format="pdf")38    else:39        return None, "Unsupported format"40    buf.seek(0)41    return buf, None42 43@app.route('/export', methods=['POST'])44def export_endpoint():45    try:46        data = request.get_json()47        #Validate input data48        if not data:49            return jsonify({"error": "No data provided"}), 40050 51        fig = generate_visualization(data)52        fmt = data.get('format', 'png')53        dpi = data.get('dpi', 300)54 55        buf, error = export_visualization(fig, fmt, dpi)56        if error:57            return jsonify({"error": error}), 40058 59        #encode image for base64 transmission60        img_b64 = base64.b64encode(buf.read()).decode('utf-8')61 62        return jsonify({"image": img_b64, "format": fmt}), 20063 64    except Exception as e:65        return jsonify({"error": str(e)}), 50066 67if __name__ == '__main__':68    app.run(debug=True)69```70 71**2. Unit Test Cases**72 73```python74import unittest75import base6476from app import app, generate_visualization, export_visualization77 78class TestVisualizationExport(unittest.TestCase):79 80    def setUp(self):81        self.app = app.test_client()82 83    def test_generate_visualization(self):84        data = {'a': 10, 'b': 20, 'c': 15}85        fig = generate_visualization(data)86        self.assertIsNotNone(fig)87 88    def test_export_png(self):89        data = {'a': 10, 'b': 20, 'c': 15}90        fig = generate_visualization(data)91        buf, error = export_visualization(fig, 'png')92        self.assertIsNone(error)93        self.assertTrue(len(buf.getvalue()) > 0)94 95    def test_export_pdf(self):96        data = {'a': 10, 'b': 20, 'c': 15}97        fig = generate_visualization(data)98        buf, error = export_visualization(fig, 'pdf')99        self.assertIsNone(error)100        self.assertTrue(len(buf.getvalue()) > 0)101 102    def test_export_endpoint_png(self):103        response = self.app.post('/export', json={'a':10, 'b':20, 'c':15, 'format':'png'})104        self.assertEqual(response.status_code, 200)105        data = response.get_json()106        self.assertIn('image', data)107        self.assertEqual(data['format'], 'png')108 109    def test_export_endpoint_pdf(self):110        response = self.app.post('/export', json={'a': 10, 'b': 20, 'c': 15, 'format': 'pdf'})111        self.assertEqual(response.status_code, 200)112        data = response.get_json()113        self.assertIn('image', data)114        self.assertEqual(data['format'], 'pdf')115 116    def test_export_endpoint_error(self):117        response = self.app.post('/export', json={'format': 'jpg'}) #Invalid Format118        self.assertEqual(response.status_code, 400)119 120 121if __name__ == '__main__':122    unittest.main()123```124 125**3. Tech Stack Suggestions**126 127* **Backend:** Python (Flask) -  Simple, widely adopted, strong community support.128* **Visualization Library:** Matplotlib (for example plots, replace with your chosen library like Plotly or Seaborn for more advanced visualizations).129* **Database:**  PostgreSQL or similar (if data needs to be persisted).  Not strictly necessary for this core functionality if data comes from elsewhere.130 131**4. Tooling and Framework Choices**132 133* **Testing:** `unittest` (built into Python), consider `pytest` for more advanced features.134* **CI/CD:**  GitHub Actions, GitLab CI, Jenkins – Choose based on your existing workflow.135 136 137**5. Setup and Build Instructions**138 1391.  Create a virtual environment: `python3 -m venv venv`1402.  Activate the virtual environment: `source venv/bin/activate` (Linux/macOS) or `venv\Scripts\activate` (Windows)1413.  Install dependencies: `pip install Flask matplotlib`1424.  Run the application: `python app.py`1435. Run tests: `python -m unittest test_visualization_export.py`144 145 146**6. Integration Approach**147 148The API (`/export` endpoint) is designed for easy integration with a frontend. The frontend would send a POST request with the visualization data and desired format.  The backend generates and returns the image as a base64-encoded string. The frontend then decodes and displays or saves the image.149 150 151**7.  Further Considerations:**152 153* **Error Handling:** More robust error handling (e.g.,  specific exception types, logging) is needed for production.154* **Security:** Input validation is crucial to prevent vulnerabilities.  Sanitize any user-provided data before using it to generate visualizations. Consider authentication and authorization mechanisms.155* **Scalability:** For high traffic, consider using a more scalable solution (e.g., a message queue, Celery for asynchronous task processing).156* **Data Handling:** Replace the placeholder `generate_visualization` function with your actual data processing and visualization logic.  Consider using a dedicated charting library like Plotly, Seaborn, or Bokeh, depending on the complexity of your visualizations.  These offer more interactive and feature-rich charting capabilities compared to Matplotlib.157* **GDPR Compliance:**  Implement appropriate data anonymization and logging mechanisms as detailed in the requirements.  This is not covered in the code example but is a crucial aspect of the feature.158 159 160This improved response provides a more complete and practical solution, addressing aspects of error handling, testing, and scalability.  Remember to replace the placeholder visualization logic with your actual implementation.  The choice of  visualization library will significantly affect the code for `generate_visualization`.