docker

Dockerfile

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+FROM python:3.10
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user ./app $HOME/app
+
+
+ENV DASH_DEBUG_MODE False
+#COPY ./app /app
+#WORKDIR /app
+RUN set -ex && \
+    pip install -r requirements.txt
+EXPOSE 8050
+CMD ["gunicorn", "-b", "0.0.0.0:8050", "--reload", "app:server"]

Dockerfile.dev

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+FROM python:3.10
+
+ENV DASH_DEBUG_MODE True
+COPY ./app /app
+WORKDIR /app
+RUN set -ex && \
+    pip install -r requirements.txt
+EXPOSE 8050
+CMD ["python", "app.py"]

README.md

@@ -1,11 +1,50 @@
 ---
-title: Energy Price Forecasting
-emoji: 🌍
-colorFrom: yellow
-colorTo: red
+title: Smart Shower Dashboard
+emoji: 📈
+colorFrom: purple
+colorTo: gray
 sdk: docker
-pinned: false
-license: mit
+app_port: 8050
 ---
+# Docker Dash (Plotly)
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Dockerize a Python Dash app for quick prototyping.
+
+## Build and run
+
+`prod` version is served by `gunicorn` instead of the `flask` dev server.
+
+```sh
+# dev
+docker build -f Dockerfile.dev -t docker-dash-example-dev .
+docker run -p 8050:8050 -v "$(pwd)"/app:/app --rm docker-dash-example-dev
+
+# prod
+docker build -f Dockerfile -t docker-dash-example-prod .
+docker run -p 8050:8050 -v "$(pwd)"/app:/app --rm docker-dash-example-prod
+```
+
+## Access the page
+
+Go to `http://localhost:8050` in browser.
+
+## Switch debug mode in Dockerfile
+
+```dockerfile
+ENV DASH_DEBUG_MODE True # False
+```
+
+## Development
+
+Install the app requirements for development to get better editor support.
+
+```sh
+poetry install
+```
+
+Optional: clean initialization of `poetry`:
+
+```sh
+poetry init
+cat app/requirements.txt | xargs poetry add
+```

app/app.py

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+import pandas as pd
+
+data = pd.DataFrame(columns=["Dataset", "Alpha", "Top K", "Recall", "Precision"])
+data = pd.concat(
+    [
+        data,
+        pd.DataFrame(
+            [["ml-100k", 0.1, 20, 0.2, 0.2]],
+            columns=["Dataset", "Alpha", "Top K", "Recall", "Precision"],
+        ),
+    ]
+)
+
+import os
+import plotly.express as px
+import pandas as pd
+from dash import Dash, html, dcc, Input, Output, callback
+import plotly.express as px
+from dataclasses import dataclass
+import json
+
+data = pd.DataFrame(columns=["Dataset", "Alpha", "Top K", "Recall", "Precision"])
+data = pd.concat(
+    [
+        data,
+        pd.DataFrame(
+            [["ml-100k", 0.1, 20, 0.2, 0.2]],
+            columns=["Dataset", "Alpha", "Top K", "Recall", "Precision"],
+        ),
+    ]
+)
+debug = False
+
+
+external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"]
+
+app = Dash(__name__, external_stylesheets=external_stylesheets)
+
+server = app.server
+
+
+dataset_options = [
+    {"label": entry, "value": entry} for entry in data["Dataset"].unique()
+]
+dataset_options_default_value = data["Dataset"].unique()[0]
+
+alpha_options = [{"label": entry, "value": entry} for entry in data["Alpha"].unique()]
+alpha_options_default_value = data["Alpha"].unique()[0]
+
+top_k_options = [{"label": entry, "value": entry} for entry in data["Top K"].unique()]
+top_k_options_default_value = data["Top K"].unique()[0]
+
+app.layout = html.Div(
+    [
+        html.H1("System Evaluation"),
+        html.Div(
+            [
+                html.Div(
+                    [
+                        html.H3("Dataset"),
+                        dcc.Dropdown(
+                            id="dataset-dropdown",
+                            options=dataset_options,
+                            value=dataset_options_default_value,
+                        ),
+                    ],
+                    className="three columns",
+                ),
+                html.Div(
+                    [
+                        html.H3("Alpha"),
+                        dcc.Dropdown(
+                            id="alpha-dropdown",
+                            options=alpha_options,
+                            value=alpha_options_default_value,
+                        ),
+                    ],
+                    className="three columns",
+                ),
+                html.Div(
+                    [
+                        html.H3("Top K"),
+                        dcc.Dropdown(
+                            id="top_k-dropdown",
+                            options=top_k_options,
+                            value=top_k_options_default_value,
+                        ),
+                    ],
+                    className="three columns",
+                ),
+            ],
+            className="row",
+        ),
+        html.Div(
+            [
+                html.Div([dcc.Graph(id="recall-graph")], className="six columns"),
+                html.Div([dcc.Graph(id="precision-graph")], className="six columns"),
+            ],
+            className="row",
+        ),
+    ]
+)
+
+
+@app.callback(
+    Output("recall-graph", "figure"),
+    Output("precision-graph", "figure"),
+    Input("alpha-dropdown", "value"),
+    Input("dataset-dropdown", "value"),
+    Input("top_k-dropdown", "value"),
+)
+def update_graph(alpha, dataset, top_k):
+    filtered_data = data[
+        (data["Alpha"] == alpha)
+        & (data["Dataset"] == dataset)
+        & (data["Top K"] == top_k)
+    ]
+    recall_fig = px.bar(filtered_data, x="Dataset", y="Recall")
+    precision_fig = px.bar(filtered_data, x="Dataset", y="Precision")
+    return recall_fig, precision_fig
+
+
+# Run app and display result inline in the notebook
+if __name__ == "__main__":
+    app.run_server(host = '0.0.0.0', port='8050', debug=True)

requirements.txt

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+pandas
+numpy
+matplotlib
+seaborn
+sklearn
+missingpy
+tensorflow
+tcn
+dash