Sync from GitHub via hub-sync

.dockerignore

@@ -0,0 +1,73 @@
+# Python cache and bytecode
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+
+# Virtual environments / local packages
+env/
+venv/
+.venv/
+__pypackages__/
+
+# Installer / build logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Test / coverage artifacts
+.tox
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.log
+.mypy_cache
+.pytest_cache
+.hypothesis
+
+# Packaging / build artifacts
+dist/
+build/
+*.egg-info/
+.eggs/
+*.whl
+
+# Environment files (local secrets)
+.env
+.env.*
+!.env.example
+
+# Git data
+.git
+
+# Local tooling / OS cruft
+.python-version
+.DS_Store
+Thumbs.db
+.idea/
+.vscode/
+
+# Notebook checkpoints
+.ipynb_checkpoints/
+
+# Frontend dependency caches (if present)
+node_modules/
+
+# Project-specific cruft
+_brand.yml
+
+# Local cache / output files
+data/*
+!data/.gitkeep
+!data/daioe_*_v*.csv
+!data/scb_months_lvl1.parquet
+
+# remove extra files 
+.gitattributes
+.github
+.gitignore
+.ruff_cache
+.dockerignore

.python-version

@@ -0,0 +1 @@
+3.14

Dockerfile

@@ -0,0 +1,41 @@
+# ------------------------------- Builder Stage ------------------------------ #
+FROM python:3.14-bookworm AS builder
+
+RUN apt-get update && apt-get install --no-install-recommends -y \
+    build-essential curl ca-certificates \
+  && rm -rf /var/lib/apt/lists/*
+
+# Install uv
+RUN curl -fsSL https://astral.sh/uv/install.sh -o /install.sh \
+  && chmod 755 /install.sh \
+  && /install.sh \
+  && rm -f /install.sh
+
+ENV PATH="/root/.local/bin:${PATH}"
+ENV UV_PROJECT_ENVIRONMENT=/app/.venv
+
+WORKDIR /app
+
+# Install deps from lockfile (cache uv downloads for faster rebuilds).
+# This is a flat Shiny app, so only install dependencies, not a package.
+COPY pyproject.toml uv.lock ./
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv sync --frozen --no-dev --no-install-project
+
+
+## ------------------------------ Production Stage ---------------------------- ##
+FROM python:3.14-slim-bookworm AS production
+
+WORKDIR /app
+
+# Environment set-up
+COPY --from=builder /app/.venv /app/.venv
+ENV PATH="/app/.venv/bin:$PATH"
+
+# Copy only what the app needs at runtime
+COPY app.py ./app.py
+COPY data ./data
+
+# Requirement for deployment at hf
+EXPOSE 7860
+CMD ["shiny", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,10 +1 @@
----
-title: App Months
-emoji: 👀
-colorFrom: green
-colorTo: red
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# AI Econ DAIOE Months V2

app.py

@@ -0,0 +1,172 @@
+from pathlib import Path
+
+import plotly.express as px
+import polars as pl
+from shiny import reactive, render
+from shiny.express import input as app_input
+from shiny.express import ui
+from shinywidgets import render_widget
+
+# --- Constants ---
+MIN_POINTS_FOR_TRENDLINE = 2
+DATA_PATH = Path(__file__).parent / "data" / "scb_months_lvl1.parquet"
+
+
+# --- Data Loading ---
+def load_data():
+    if not DATA_PATH.exists():
+        return pl.DataFrame()
+    return pl.read_parquet(DATA_PATH)
+
+
+df_full = load_data()
+
+# Identify metric columns
+daioe_metrics = [
+    col for col in df_full.columns if col.startswith("daioe_") and col.endswith("_wavg")
+]
+change_metrics = ["pct_chg_1m", "pct_chg_3m", "pct_chg_6m"]
+sexes = df_full["sex"].unique().to_list() if not df_full.is_empty() else []
+years = sorted(df_full["year"].unique().to_list()) if not df_full.is_empty() else []
+occupations = (
+    sorted(df_full["occupation"].unique().to_list())
+    if not df_full.is_empty() and "occupation" in df_full.columns
+    else []
+)
+
+# --- Page Options ---
+ui.page_opts(title="AI Exposure & Employment Dashboard", fillable=True)
+
+# --- Sidebar ---
+with ui.sidebar():
+    ui.input_select(
+        "ai_metric",
+        "Select AI Exposure Metric (Weighted Avg)",
+        choices={
+            m: m.replace("daioe_", "").replace("_wavg", "").title()
+            for m in daioe_metrics
+        },
+        selected=daioe_metrics[-1] if daioe_metrics else None,
+    )
+    ui.input_select(
+        "change_horizon",
+        "Select Employment Change Horizon",
+        choices={
+            m: m.replace("pct_chg_", "").replace("m", " Month").title()
+            for m in change_metrics
+        },
+        selected="pct_chg_3m",
+    )
+    ui.input_slider(
+        "year_filter",
+        "Filter by Year",
+        min=min(years) if years else 2015,
+        max=max(years) if years else 2026,
+        value=[min(years), max(years)] if years else [2015, 2026],
+        sep="",
+    )
+    ui.input_checkbox_group(
+        "sex_filter",
+        "Filter by Sex",
+        choices=sexes,
+        selected=sexes,
+    )
+    ui.input_selectize(
+        "occ_filter",
+        "Filter by Occupation (Leave blank for all)",
+        choices=occupations,
+        multiple=True,
+    )
+    ui.hr()
+    ui.markdown("""
+    **About this Dashboard**
+    This app visualizes the relationship between AI Occupational Exposure (DAIOE)
+    and monthly employment changes in Sweden.
+    """)
+
+
+# --- Reactive Logic ---
+@reactive.calc
+def filtered_df():
+    if df_full.is_empty():
+        return pl.DataFrame()
+
+    df = df_full.filter(
+        (pl.col("year") >= app_input.year_filter()[0])
+        & (pl.col("year") <= app_input.year_filter()[1])
+        & (pl.col("sex").is_in(app_input.sex_filter())),
+    )
+
+    if app_input.occ_filter():
+        df = df.filter(pl.col("occupation").is_in(app_input.occ_filter()))
+
+    return df
+
+
+# --- Main Layout ---
+with ui.layout_columns(fill=False):
+    with ui.value_box(theme="primary"):
+        "Avg Exposure"
+
+        @render.text
+        def avg_exposure():
+            df = filtered_df()
+            if df.is_empty():
+                return "0.0"
+            val = df[app_input.ai_metric()].mean()
+            return f"{val:.2f}"
+
+    with ui.value_box(theme="secondary"):
+        "Median % Change"
+
+        @render.text
+        def median_change():
+            df = filtered_df()
+            if df.is_empty():
+                return "0.0%"
+            val = df[app_input.change_horizon()].median()
+            return f"{val:+.2f}%"
+
+    with ui.value_box(theme="info"):
+        "Observation Count"
+
+        @render.text
+        def obs_count():
+            return f"{len(filtered_df()):,}"
+
+
+with ui.card(full_screen=True):
+    ui.card_header("AI Exposure vs. Employment Change")
+
+    @render_widget
+    def scatter_plot():
+        df = filtered_df().to_pandas()
+        if df.empty:
+            return px.scatter(title="No data available for selected filters")
+
+        fig = px.scatter(
+            df,
+            x=app_input.ai_metric(),
+            y=app_input.change_horizon(),
+            color="occupation",
+            size="emp_count" if "emp_count" in df.columns else None,
+            hover_data=["month", "sex", "emp_count"],
+            labels={
+                app_input.ai_metric(): "AI Exposure Score",
+                app_input.change_horizon(): "% Change in Employment",
+            },
+            template="plotly_white",
+            opacity=0.7,
+            trendline="ols" if len(df) > MIN_POINTS_FOR_TRENDLINE else None,
+            trendline_scope="overall" if len(df) > MIN_POINTS_FOR_TRENDLINE else None,
+        )
+        fig.update_layout(legend_title_text="Occupation")
+        return fig
+
+
+with ui.card(full_screen=True):
+    ui.card_header("Filtered Data Table")
+
+    @render.data_frame
+    def data_table():
+        return render.DataGrid(filtered_df().to_pandas())

data/scb_months_lvl1.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eefa85998192d9c10cbc16d4ab2dc74f66015a681715e8586fecee259868ebc6
+size 167326

pyproject.toml

@@ -0,0 +1,16 @@
+[project]
+name = "ai-econ-daioe-months-v2"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.14"
+dependencies = [
+    "pandas>=3.0.2",
+    "pathlib>=1.0.1",
+    "plotly>=6.7.0",
+    "polars>=1.40.1",
+    "pyarrow>=24.0.0",
+    "shiny>=1.6.0",
+    "shinywidgets>=0.8.0",
+    "statsmodels>=0.14.6",
+]

uv.lock

@@ -0,0 +1,916 @@
+version = 1
+revision = 3
+requires-python = ">=3.14"
+resolution-markers = [
+    "sys_platform == 'win32'",
+    "sys_platform == 'emscripten'",
+    "sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "ai-econ-daioe-months-v2"
+version = "0.1.0"
+source = { virtual = "." }
+dependencies = [
+    { name = "pandas" },
+    { name = "pathlib" },
+    { name = "plotly" },
+    { name = "polars" },
+    { name = "pyarrow" },
+    { name = "shiny" },
+    { name = "shinywidgets" },
+    { name = "statsmodels" },
+]
+
+[package.metadata]
+requires-dist = [
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+    { name = "pathlib", specifier = ">=1.0.1" },
+    { name = "plotly", specifier = ">=6.7.0" },
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