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ai_advanced_reporting [2025/04/24 01:48] – 156.146.54.84ai_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/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:
-```+ 
 Pipeline Report Pipeline Report
 Accuracy: 85% Precision: 88% Recall: 81% Summary: Pipeline executed successfully with moderate recall levels. Accuracy: 85% Precision: 88% Recall: 81% Summary: Pipeline executed successfully with moderate recall levels.
-```  + 
- +
----+
  
 ==== 2. Structured Data Conversion ==== ==== 2. Structured Data Conversion ====
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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:
-```+ 
 ERROR: Failed to generate PDF report: 'NoneType' object is not iterable ERROR: Failed to generate PDF report: 'NoneType' object is not iterable
-```  +  
- +
----+
  
 ===== Advanced Examples ===== ===== Advanced Examples =====
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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_file = f"report_dataset_{idx + 1}.pdf"     output_file = f"report_dataset_{idx + 1}.pdf"
     AdvancedReporting.generate_pdf_report(data, output_file)     AdvancedReporting.generate_pdf_report(data, output_file)
-```+ 
  
 **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:
-```python+<code> 
 +   python
 { {
     "Accuracy": "90%",     "Accuracy": "90%",
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     "Summary": "Improved robustness with great precision."     "Summary": "Improved robustness with great precision."
 } }
-``` +  
 +</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**
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   - Successful completion notifications.   - Successful completion notifications.
  
-**3. File Path Management**: Ensure the output path is write-accessible to avoid errors (e.g., using `/tmp/` directories or explicitly defined user paths). +**3. File Path Management**: Ensure the output path is write-accessible to avoid errors (e.g., using /tmp/ directories or explicitly defined user paths).
- +
----+
  
 ===== Applications ===== ===== Applications =====
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 3. **Pipeline Monitoring**: Automatically generate performance and error logs for deployed pipelines. Combine summary data with charts for data-rich insights. 3. **Pipeline Monitoring**: Automatically generate performance and error logs for deployed pipelines. Combine summary data with charts for data-rich insights.
- 
---- 
  
 ===== Conclusion ===== ===== Conclusion =====
  
 The **AI Advanced Reporting** framework is a vital tool for automatically creating standardized, structured, and visually enriched reports. By integrating simple dictionary inputs, error handling mechanisms, and examples for advanced use cases (like including charts), it provides extensive functionality for AI, business, and data-driven reporting workflows. The **AI Advanced Reporting** framework is a vital tool for automatically creating standardized, structured, and visually enriched reports. By integrating simple dictionary inputs, error handling mechanisms, and examples for advanced use cases (like including charts), it provides extensive functionality for AI, business, and data-driven reporting workflows.
ai_advanced_reporting.1745459337.txt.gz · Last modified: 2025/04/24 01:48 by 156.146.54.84