Initial commit

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

.gitignore

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+.env
+.vscode/
+.venv/
+.__pycache__/

Dockerfile

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+# Dockerfile
+FROM python:3.12-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY ./app .
+CMD ["bokeh", "serve", "--port", "7860", "--allow-websocket-origin=*", "app.py"]

README.md

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+---
+title: Auto Forecast Tool
+emoji: 📈
+colorFrom: blue
+colorTo: gray
+sdk: docker
+app_port: 7860
+pinned: false
+---
+The above yaml frontmatter is required to configure huggingface space.
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app/app.py

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+from bokeh.io import curdoc
+from bokeh.models import FileInput, Div, ColumnDataSource, DataTable, TableColumn, TextInput, NumericInput,Button
+from bokeh.layouts import column,row
+from bokeh.plotting import figure
+import base64
+import io
+import pandas as pd
+import forecast 
+
+#Initialize an empty data source, table, and figure
+source = ColumnDataSource(data={})
+columns = [] # Initially empty, will be built on upload
+data_table = DataTable(source=source, columns=columns, width=300, height=400)
+table_title = Div(text="""<h2>Data Table</h2>""", width=300)
+fig = figure(title = "Auto Forecast Plot",width=1500,height=800)
+fig.title.text_font_style = 'bold'
+fig.title.text_color = 'black'
+fig.title.text_font_size = '14pt'
+fig.title.align = 'center'
+#Create bokeh objects
+title = Div(text="""
+    <div style="text-align: center; width: 100%;">
+        <h1>Auto Forecast Tool</h1>
+    </div>
+""", width=1800,align='center',sizing_mode='stretch_width')
+status_msg = Div(text = "<b>STATUS:</b> Ready")
+file_input = FileInput(accept=".csv",title="Upload Time Series Data as CSV")
+seasonality = NumericInput(value=1,title="Number of periods in season? Enter 1 if no seasonality.", mode='int')
+n_ahead = NumericInput(value=12,title="Number of periods to forecast?", mode='int')
+column_name = TextInput(title="Column name to forecast?")
+forecast = Button(label="Auto Forecast")
+
+
+def update_status():
+    #Update status
+    status_msg.text = "<b>STATUS:</b> Running......"
+    curdoc().add_next_tick_callback(upload_callback)
+
+def upload_callback():
+    # Decode upload, save to memory, and read in as df
+    decoded = base64.b64decode(file_input.value)
+    file = io.BytesIO(decoded)
+    df = pd.read_csv(file)
+
+    # Make forecasts and append to df
+    forecasts = forecast.forecast(df,column_name.value,seasonality.value,n_ahead.value)
+    new_data = pd.DataFrame()
+    new_data['Forecasts'] = forecasts
+    df = pd.concat([df, new_data], ignore_index=True).reset_index()
+
+    #Update the data source
+    source.data = df.to_dict('list')
+    #Update the columns based on the new file's headers
+    data_table.columns = [TableColumn(field=col, title=col) for col in df.columns]
+
+    #Update figure with all data
+    fig.line(x='index',y= 'Sales',source=source,line_width=2)
+    fig.line(x='index',y= 'Forecasts',source=source,line_width=2,line_color='Red')
+    #Update status
+    status_msg.text = "<b>STATUS:</b> Complete!"
+
+forecast.on_click(update_status)
+
+layout = column(row(title, align='center'),row(column(status_msg,file_input, seasonality, n_ahead, column_name,forecast, table_title, data_table),fig),sizing_mode='stretch_both')
+curdoc().add_root(layout)

app/forecast.py

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+from pmdarima import auto_arima
+import pandas as pd
+
+def forecast(df, column_name: str, seasonality: int, ahead: int):
+    data = df[column_name]
+    oos = round(len(data) * .1)
+    model = auto_arima(data,m = seasonality, max_order = 10, information_criterion = 'oob', stepwise = False, out_of_sample_size = oos, scoring = 'mse')
+    forecasts = model.predict(ahead,return_conf_int = False)
+    return forecasts

docker-compose.yaml

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+services:
+  app:
+    build: .
+    container_name: AutoForecast
+    restart: unless-stopped
+    ports:
+      - "7860:7860" 

requirements.txt

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+bokeh==3.9.0
+contourpy==1.3.3
+Cython==3.2.4
+Jinja2==3.1.6
+joblib==1.5.3
+MarkupSafe==3.0.3
+narwhals==2.18.1
+numpy==2.4.4
+packaging==26.0
+pandas==3.0.2
+patsy==1.0.2
+pillow==12.2.0
+pmdarima==2.1.1
+python-dateutil==2.9.0.post0
+PyYAML==6.0.3
+scikit-learn==1.8.0
+scipy==1.17.1
+setuptools==82.0.1
+six==1.17.0
+statsmodels==0.14.6
+threadpoolctl==3.6.0
+tornado==6.5.5
+tzdata==2026.1
+urllib3==2.6.3
+xyzservices==2026.3.0

sample_ts.csv

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