update quarto report rendering pipeline

Makefile

@@ -19,11 +19,12 @@ data/processed/ground_truth.csv : analysis/preprocess_batch_run_result.py data/b
 
 # Build 'report/docs/index.html' by rendering the Jupyter notebooks using Quarto.
 report/docs/index.html : data/processed/ground_truth.csv
-	quarto render
+	quarto render --cache-refresh
+	awk '{gsub(/proposal/,"final_report"); print}' ./report/docs/index.html > tmp && mv tmp ./report/docs/index.html
 
 .PHONY : publish
 publish : data/processed/ground_truth.csv
-	quarto publish gh-pages
+	quarto publish gh-pages ./report
 
 # The 'clean' target is used to clean up generated files and directories.
 .PHONY : clean
@@ -35,4 +36,5 @@ clean :
 	rm -rf data/batch_run/batch_run_4o
 	rm -rf data/processed/ground_truth.csv
 	rm -rf data/processed/score_*csv
+	rm -rf report/.quarto/
 

README.md

@@ -63,15 +63,15 @@ Run `fixml --help` for more details.
 
 > [!IMPORTANT]
 > By default, this tool uses OpenAI's `gpt3.5-turbo` for evaluation. To run any
-command that requires calls to LLM (i.e. `fixml evaluate`, `fixml generate`),
-an environment variable `OPENAI_API_KEY` needs to be set. To do so, either use
+> command that requires calls to LLM (i.e. `fixml evaluate`, `fixml generate`),
+> an environment variable `OPENAI_API_KEY` needs to be set. To do so, either use
 `export` to set the variable in your current session, or create a `.env` file
-with a line `OPENAI_API_KEY={your-api-key}` saved in your working directory.
+> with a line `OPENAI_API_KEY={your-api-key}` saved in your working directory.
 
 > [!TIP]
 > Currently, only calls to OpenAI endpoints are supported. This tool is still in
-ongoing development and integrations with other service providers and locally
-hosted LLMs are planned.
+> ongoing development and integrations with other service providers and locally
+> hosted LLMs are planned.
 
 #### Test Evaluator
 
@@ -208,4 +208,7 @@ learning engineers, educators, practitioners, and hobbyists alike. Your
 contributions and feedback are invaluable in making this package a reliable
 resource for the community. 
 
-Special thanks to the University of British Columbia (UBC) and the University of Wisconsin-Madison for their support and resources. We extend our gratitude to Dr. Tiffany and Dr. Simon for their guidance and expertise, which have been instrumental in the development of this project.
+Special thanks to the University of British Columbia (UBC) and the University of
+Wisconsin-Madison for their support and resources. We extend our gratitude to
+Dr. Tiffany Timbers and Dr. Simon Goringfor their guidance and expertise, which
+have been instrumental in the development of this project.

_quarto.yml

@@ -1,10 +1,12 @@
 project:
   type: website
   render:
-    - "report/*qmd"
-  output-dir: report/docs
+    - report/final_report.qmd
+    - report/proposal.qmd
+  output-dir: "report/docs/"
 
 website:
+  title: "FixML - Checklists and LLM prompts for efficient and effective test creation in data analysis"
   sidebar:
     style: "docked"
     logo: "img/logo.png"

report/final_report.qmd

@@ -1,12 +1,11 @@
 ---
+title: "Final Report - Checklists and LLM prompts for efficient and effective test creation in data analysis"
 format:
   html:
     code-fold: true
 bibliography: references.bib
 ---
 
-# Final Report - Checklists and LLM prompts for efficient and effective test creation in data analysis
-
 by John Shiu, Orix Au Yeung, Tony Shum, Yingzi Jin
 
 ## Executive Summary

report/proposal.qmd

@@ -1,13 +1,12 @@
 ---
+title: "Proposal Report - Checklists and LLM prompts for efficient and effective test creation in data analysis"
 format:
   html:
     code-fold: true
 bibliography: references.bib
 jupyter: python3
 ---
 
-# Proposal Report - Checklists and LLM prompts for efficient and effective test creation in data analysis
-
 by John Shiu, Orix Au Yeung, Tony Shum, Yingzi Jin
 
 ## Executive Summary
@@ -36,7 +35,7 @@ We propose to develop testing suites diagnostic tools based on Large Language Mo
 
 Our solution offers an end-to-end application for evaluating and enhancing the robustness of users' ML systems.
 
-![Main components and workflow of the proposed system. The checklist would be written in [YAML](https://yaml.org/) to maximize readability for both humans and machines. We hope this will encourage researchers/users to read, understand and modify the checklist items, while keeping the checklist closely integrated with other components in our system.](../img/proposed_system_overview.png)
+![Main components and workflow of the proposed system. The checklist would be written in [YAML](https://yaml.org/) to maximize readability for both humans and machines. We hope this will encourage researchers/users to read, understand and modify the checklist items, while keeping the checklist closely integrated with other components in our system.](../img/proposed_system_overview.png){.lightbox}
 
 One big challenge in utilizing LLMs to reliably and consistently evaluate ML systems is their tendency to generate illogical and/or factually wrong information known as hallucination [@zhang2023sirens].