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ai_advanced_reporting [2025/05/23 15:29] – eagleeyenebulaai_advanced_reporting [2025/06/18 18:55] (current) – [AI Advanced Reporting] eagleeyenebula
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 ====== AI Advanced Reporting ====== ====== AI Advanced Reporting ======
-* **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:+[[https://autobotsolutions.com/aurora/wiki/doku.php?id=ai_infinite_consciousnessdoku.php?id=ai_advanced_reporting|Wiki]]: [[https://autobotsolutions.com/god/templates/ai_advanced_reporting.html|Framework]]: [[https://github.com/AutoBotSolutions/Aurora/blob/Aurora/ai_advanced_reporting.py|GitHub]]: [[https://autobotsolutions.com/g-o-d-framework/advanced-reporting-streamlined-pdf-report-generation-for-ai-insights/|Article]]: 
 The **AI Advanced Reporting** framework is a Python-based utility designed to generate structured and visually formatted reports, such as **PDF documents**, summarizing the performance and insights from AI pipelines. The framework allows users to efficiently communicate and present key metrics, insights, and analysis results. The **AI Advanced Reporting** framework is a Python-based utility designed to generate structured and visually formatted reports, such as **PDF documents**, summarizing the performance and insights from AI pipelines. The framework allows users to efficiently communicate and present key metrics, insights, and analysis results.
 +
 +{{youtube>bU1QmhivD7w?large}}
 +
  
 ===== Overview ===== ===== Overview =====
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 === Example === === Example ===
-   python+<code> 
 +python
 data = { data = {
     "Accuracy": "85%",     "Accuracy": "85%",
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 AdvancedReporting.generate_pdf_report(data, output_path) AdvancedReporting.generate_pdf_report(data, output_path)
-``` 
  
 +</code>
 **Output**: **Output**:
 A PDF file named **pipeline_summary.pdf** is generated at the specified location, containing the following content: A PDF file named **pipeline_summary.pdf** is generated at the specified location, containing the following content:
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 === Example Data === === Example Data ===
-   python+<code> 
 +python
 data = { data = {
     "Model": "Random Forest",     "Model": "Random Forest",
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 AdvancedReporting.generate_pdf_report(data, output_path) AdvancedReporting.generate_pdf_report(data, output_path)
-```+</code>
  
 **Notes**: **Notes**:
-- For nested dictionaries (e.g., parameters above), first flatten the data before passing it into the generate_pdf_report()` method.+- For nested dictionaries (e.g., parameters above), first flatten the data before passing it into the generate_pdf_report() method.
  
 ==== 3. Error Handling ==== ==== 3. Error Handling ====
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 **Error Scenario**: **Error Scenario**:
 If an invalid dictionary is passed: If an invalid dictionary is passed:
-   python+<code> 
 +python
 invalid_data = None invalid_data = None
 AdvancedReporting.generate_pdf_report(invalid_data, "invalid_report.pdf") AdvancedReporting.generate_pdf_report(invalid_data, "invalid_report.pdf")
-``` 
  
 +</code>
 The system logs the following error: The system logs the following error:
    
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 **Example with Charts**: **Example with Charts**:
-   python+<code> 
 +python
 import matplotlib.pyplot as plt import matplotlib.pyplot as plt
 from io import BytesIO from io import BytesIO
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 data_with_chart = AdvancedReporting.generate_pdf_report(performance_metrics, "report_with_charts.pdf") data_with_chart = AdvancedReporting.generate_pdf_report(performance_metrics, "report_with_charts.pdf")
 +</code>
 ==== 2. Batch Reporting ==== ==== 2. Batch Reporting ====
  
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 **Batch Example:** **Batch Example:**
-   python+<code> 
 +python
 datasets = [ datasets = [
     {"Dataset": "Dataset A", "Accuracy": "89%", "Precision": "85%", "Recall": "81%"},     {"Dataset": "Dataset A", "Accuracy": "89%", "Precision": "85%", "Recall": "81%"},
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 **Output**: **Output**:
 Three separate PDFs (_report_dataset_1.pdf_, _report_dataset_2.pdf_, and _report_dataset_3.pdf_) will be created, summarizing the performance of each dataset. Three separate PDFs (_report_dataset_1.pdf_, _report_dataset_2.pdf_, and _report_dataset_3.pdf_) will be created, summarizing the performance of each dataset.
 +</code>
 ===== Best Practices ===== ===== Best Practices =====
  
 **1. Organizing Data**: Always provide structured data in dictionary form to maintain compatibility with the  generate_pdf_report() method. **1. Organizing Data**: Always provide structured data in dictionary form to maintain compatibility with the  generate_pdf_report() method.
 - Example: - Example:
 +<code>
    python    python
 { {
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 } }
    
 +</code>
 **2. Logging Usage**: Use a logging mechanism to track generation processes. Logs automatically register: **2. Logging Usage**: Use a logging mechanism to track generation processes. Logs automatically register:
   - **Start of Generation**   - **Start of Generation**
ai_advanced_reporting.1748014152.txt.gz · Last modified: 2025/05/23 15:29 by eagleeyenebula