Upload with huggingface_hub

Dockerfile

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+# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.8
+
+# 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 . $HOME/app
+RUN pip install --no-cache-dir --upgrade -r $HOME/app/requirements.txt
+
+CMD ["workcell", "serve", "--config", "workcell.yaml", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import datetime
+from pydantic import BaseModel, Field
+from typing import Dict, List, Optional
+import yfinance as yf
+import plotly.express as px
+from workcell.integrations.types import PlotlyPlot
+
+
+class Input(BaseModel):
+    tickers: List[str] = Field(
+        default=['AAPL','AMZN','META'], max_items=10, description="List of ticker values"
+    )
+
+
+def load_data(tickers):
+    """Download ticker price data from ticker list.
+    e.g. tickers = ['AAPL','AMZN','GOOG']
+    """
+    start = datetime.datetime(2022, 1, 1)
+    end = datetime.datetime.now() # latest
+    data = yf.download(tickers, start=start, end=end, interval='1d')
+    # adjust close
+    close = data['Adj Close']
+    return close
+
+
+def visualization(df):
+    """Visualization price plot by yfinance dataframe.
+    e.g. df = yf.download(***)
+    """
+    template = 'plotly_white'
+    fig = px.line(df, x=df.index, y=df.columns.tolist(), template=template)
+    fig.update_xaxes(
+        rangeslider_visible=True,
+        rangeselector=dict(
+            buttons=list([
+                dict(count=1, label="1m", step="month", stepmode="backward"),
+                dict(count=6, label="6m", step="month", stepmode="backward"),
+                dict(count=1, label="YTD", step="year", stepmode="todate"),
+                dict(count=1, label="1y", step="year", stepmode="backward"),
+                dict(step="all")
+            ])
+        )    
+    )
+    fig.update_layout(hovermode="x")    
+    return fig
+
+
+def stock_viewer(input: Input) -> PlotlyPlot:
+    """Input ticker list, returns multiple stocks price. Data frome yfinance."""
+    # Step1. load data
+    dataframe = load_data(input.tickers)
+    # Step2. create plot
+    fig = visualization(dataframe)
+    # Step3. wrapped by output
+    output = PlotlyPlot(data=fig)
+    return output

requirements.txt

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+workcell
+yfinance
+plotly

workcell.yaml

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+workcell_name: stock_viewer
+workcell_provider: huggingface
+workcell_id: weanalyze/stock_viewer
+workcell_version: latest
+workcell_runtime: python3.8
+workcell_entrypoint: app:stock_viewer
+workcell_code:
+  ImageUri: ''
+workcell_tags: {}
+workcell_envs: {}