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.graph_objs as go
+import plotly.express as px
+from prophet import Prophet
+from workcell.integrations.types import PlotlyPlot
+
+
+class Input(BaseModel):
+    ticker: str = Field(default="AAPL", description="A ticker value, like `AAPL`, etc...")
+
+
+def load_data(ticker):
+    """Download ticker price data from ticker.
+    e.g. ticker = 'AAPL'|'AMZN'|'GOOG'
+    """
+    start = datetime.datetime(2022, 1, 1)
+    end = datetime.datetime.now() # latest
+    data = yf.download(ticker, start=start, end=end, interval='1d')
+    # adjust close
+    close = data['Adj Close']
+    return close
+
+
+def preprocess_data(df):
+    """
+    Preprocess dataframe for prediction.
+    - Filter out predict value.
+    """
+    # post process
+    df_processed = df.reset_index()
+    df_processed.rename(columns={'Adj Close': 'y', 'Date': 'ds'}, inplace=True)
+    return df_processed
+
+
+def predict_data(df, periods=30):
+    """Predict future prices by prophet.
+    e.g. df = preprocess_df(df)
+    """
+    # init prophet model
+    model = Prophet()
+    # fit
+    model.fit(df)
+    # predict data
+    future_prices = model.make_future_dataframe(periods=periods)
+    forecast = model.predict(future_prices)
+    # forecast data
+    df_forecast = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]
+    return df_forecast
+
+
+def visualization(df_processed, df_forecast, ticker):
+    """Visualization price plot by df_forecast dataframe.
+    """
+    trace_open = go.Scatter(
+        x = df_forecast["ds"],
+        y = df_forecast["yhat"],
+        mode = 'lines',
+        name="Forecast"
+    )
+
+    trace_high = go.Scatter(
+        x = df_forecast["ds"],
+        y = df_forecast["yhat_upper"],
+        mode = 'lines',
+        fill = "tonexty", 
+        line = {"color": "#57b8ff"}, 
+        name="Higher uncertainty interval"
+    )
+
+    trace_low = go.Scatter(
+        x = df_forecast["ds"],
+        y = df_forecast["yhat_lower"],
+        mode = 'lines',
+        fill = "tonexty", 
+        line = {"color": "#57b8ff"}, 
+        name="Lower uncertainty interval"
+    )
+
+    trace_close = go.Scatter(
+        x = df_processed["ds"],
+        y = df_processed["y"],
+        name="Data values"
+    )
+
+    data = [trace_open,trace_high,trace_low,trace_close]
+    layout = go.Layout(title="Repsol Stock Price Forecast for: {}".format(ticker), xaxis_rangeslider_visible=True)
+    fig = go.Figure(data=data,layout=layout)
+    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", 
+        legend=dict(
+            yanchor="top",
+            y=0.99,
+            xanchor="left",
+            x=0.01
+        )
+    )
+    return fig
+
+
+def stock_predictor(input: Input) -> PlotlyPlot:
+    """Input ticker, predict stocks price in 30 days by prophet. Data from yahoo finance."""
+    # Step1. load data & preprocess
+    df = load_data(input.ticker)
+    df_processed = preprocess_data(df)
+    # Step2. predict
+    df_forecast = predict_data(df_processed)
+    # Step3. visualization
+    fig = visualization(df_processed, df_forecast, input.ticker)
+    # Step3. wrapped by output
+    output = PlotlyPlot(data=fig)
+    return output

requirements.txt

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+workcell
+yfinance
+plotly
+prophet

workcell.yaml

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