Uploading the DockerFile, app.R, and README.md

Dockerfile

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+FROM rocker/shiny:4.5.2
+
+# System dependencies for querychat/duckdb
+RUN apt-get update && apt-get install -y \
+    libcurl4-openssl-dev \
+    libssl-dev \
+    libxml2-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# Install remotes for version-pinned installation
+RUN R -e "install.packages('remotes', repos='https://cloud.r-project.org')"
+
+# Install CRAN packages with pinned versions
+RUN R -e "remotes::install_version('shiny', version = '1.12.1', repos = 'https://cloud.r-project.org')"
+RUN R -e "remotes::install_version('bslib', version = '0.10.0', repos = 'https://cloud.r-project.org')"
+RUN R -e "remotes::install_version('DT', version = '0.34.0', repos = 'https://cloud.r-project.org')"
+RUN R -e "remotes::install_version('dplyr', version = '1.2.0', repos = 'https://cloud.r-project.org')"
+# duckdb - install from duckdb r-universe with pinned version
+RUN R -e "remotes::install_version('duckdb', version = '1.4.4', repos = c('https://duckdb.r-universe.dev', 'https://cloud.r-project.org'))"
+
+# Install r-universe packages with pinned versions (querychat and dependencies)
+RUN R -e "remotes::install_version('ellmer', version = '0.4.0', repos = c('https://posit-dev.r-universe.dev', 'https://cloud.r-project.org'))"
+RUN R -e "remotes::install_version('shinychat', version = '0.3.0', repos = c('https://posit-dev.r-universe.dev', 'https://cloud.r-project.org'))"
+RUN R -e "remotes::install_version('querychat', version = '0.2.0', repos = c('https://posit-dev.r-universe.dev', 'https://cloud.r-project.org'))"
+
+# Copy application code
+COPY app.R app.R
+COPY data/ data/
+COPY www/ www/
+
+# Verify critical packages are available
+RUN R -e "stopifnot(requireNamespace('shiny', quietly = TRUE))" \
+    && R -e "stopifnot(requireNamespace('querychat', quietly = TRUE))"
+
+EXPOSE 7860
+
+CMD ["R", "--quiet", "-e", "shiny::runApp('/app', host='0.0.0.0', port=7860)"]

README.md

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----
-title: Querychat Demo
-emoji: 🐠
-colorFrom: green
-colorTo: red
-sdk: docker
-pinned: false
-license: mit
-short_description: A short demo for using querychat for Miami Students
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+sdk: docker
+app_port: 7860
+title: ISA 401 OEWS Jobs Explorer
+emoji: 📊
+colorFrom: yellow
+colorTo: yellow
+pinned: false
+license: mit
+short_description: Explore U.S. occupational employment and wage data
+---
+
+# ISA 401 OEWS Jobs Explorer
+
+**Your AI-Powered Assistant for Exploring Occupational Employment and Wage Statistics**
+
+[![Live App](https://img.shields.io/badge/Live_App-Hugging_Face-yellow)](https://huggingface.co/spaces/fmegahed/oews_explorer)
+
+Explore the U.S. Bureau of Labor Statistics' Occupational Employment and Wage Statistics (OEWS) dataset using natural language queries.
+
+---
+
+## What is this app?
+
+This Shiny application uses AI-powered natural language processing to help you explore the OEWS dataset. Instead of writing SQL queries, simply ask questions in plain English and get instant results.
+
+**Example queries:**
+- "What are the top 10 highest paying occupations in Ohio?"
+- "Show me employment by industry for software developers"
+- "What is the median wage for nurses nationally?"
+- "Compare wages between California and Texas for data scientists"
+
+---
+
+## Dataset Information
+
+**Dataset:** Occupational Employment and Wage Statistics (OEWS) Survey (May 2024 Estimates)
+**Publisher:** U.S. Bureau of Labor Statistics (BLS), Department of Labor
+**Website:** https://www.bls.gov/oes/
+
+The OEWS program produces employment and wage estimates annually for over 800 occupations. These estimates are available for the nation as a whole, for individual states, and for metropolitan and nonmetropolitan areas.
+
+### Key Fields
+
+| Field | Description |
+|-------|-------------|
+| `occ_title` | Occupation title |
+| `area_title` | Geographic area name |
+| `tot_emp` | Total employment |
+| `a_mean` | Mean annual wage |
+| `a_median` | Median annual wage |
+| `h_mean` | Mean hourly wage |
+
+---
+
+## Features
+
+- **Natural Language Queries**: Ask questions in plain English
+- **SQL Transparency**: See the generated SQL for each query
+- **Interactive Data Table**: Sort, filter, and export results
+- **Miami University Theming**: Branded for ISA 401 course use
+
+---
+
+## Running Locally
+
+**With R:**
+```r
+# Install dependencies
+renv::restore()
+
+# Run the app
+shiny::runApp('.', host = '0.0.0.0', port = 7860)
+```
+
+**With Docker:**
+```bash
+# Build the image
+docker build -t oews-explorer .
+
+# Run with OpenAI API key
+docker run --rm -p 7860:7860 -e OPENAI_API_KEY=$OPENAI_API_KEY oews-explorer
+```
+
+---
+
+## Required Environment Variable
+
+This app requires an OpenAI API key to function:
+
+```bash
+export OPENAI_API_KEY="your-api-key-here"
+```
+
+On Hugging Face Spaces, set this as a secret in your Space settings.
+
+---
+
+## Technology Stack
+
+- **[Shiny](https://shiny.posit.co/)** - Web application framework for R
+- **[querychat](https://github.com/posit-dev/querychat)** - Natural language data querying
+- **[bslib](https://rstudio.github.io/bslib/)** - Bootstrap theming for Shiny
+- **[DT](https://rstudio.github.io/DT/)** - Interactive data tables
+
+---
+
+## Course Information
+
+This application was developed for **ISA 401** at **Miami University** to help students explore and understand labor market data using modern AI-powered tools.
+
+---
+
+## Data Source
+
+Bureau of Labor Statistics, U.S. Department of Labor. *Occupational Employment and Wage Statistics (OEWS), May 2024 Estimates.*
+https://www.bls.gov/oes/

app.R

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+# ISA 401 OEWS Jobs Explorer
+# A Shiny app for exploring U.S. Occupational Employment and Wage Statistics
+
+library(shiny)
+library(bslib)
+library(querychat)
+library(DT)
+library(dplyr)
+library(duckdb)
+
+# Load preprocessed data
+oews <- readRDS("data/oews.rds")
+
+# Step 1: Create QueryChat object
+client = ellmer::chat_openai(
+  model = "gpt-5-mini-2025-08-07",
+  credentials = function(){return(Sys.getenv("OPENAI_API_KEY"))}
+)
+
+qc = querychat::querychat(
+  oews,
+  client = client,
+  greeting = "Welcome to Our ISA 401 Assistant for Understanding the OEWS Dataset",
+  extra_instructions = "data/extra_instructions.md",
+  data_description = 'data/data_desc.md'
+)
+
+## Based on the documentation:
+# https://posit-dev.github.io/querychat/r/articles/build.html#programmatic-filtering
+
+# Step 2: Add UI component
+ui <- bslib::page_sidebar(
+  sidebar = qc$sidebar(),
+  bslib::card(
+    bslib::card_header("Data Table"),
+    DT::DTOutput("table")
+  ),
+  bslib::card(
+    fill = FALSE,
+    bslib::card_header("SQL Query"),
+    shiny::verbatimTextOutput("sql")
+  )
+)
+
+# Step 3: Use reactive values in server
+server <- function(input, output, session) {
+  qc_vals <- qc$server()
+  
+  output$table <- DT::renderDT({
+    DT::datatable(qc_vals$df(), fillContainer = TRUE)
+  })
+  
+  output$sql <- shiny::renderText({
+    qc_vals$sql() %||% "SELECT * FROM oews"
+  })
+}
+
+shiny::shinyApp(ui, server)