darts gradio demo space

app.py

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+import gradio as gr
+import pypistats
+from datetime import date
+from dateutil.relativedelta import relativedelta
+from darts import TimeSeries
+from darts.models import ExponentialSmoothing
+from matplotlib import pyplot
+import pandas as pd
+
+
+def get_forecast(lib, time):
+    data = pypistats.overall(lib, total=True, format="pandas")
+    data = data.groupby("category").get_group("with_mirrors").sort_values("date")
+    start_date = date.today() - relativedelta(months=int(time.split(" ")[0]))
+    df = data[(data["date"] > str(start_date))]
+
+    df1 = df[["date", "downloads"]]
+    df1["date"] = pd.to_datetime(df1["date"])
+    df1 = df1.set_index(["date"])
+
+    target = TimeSeries.from_dataframe(df1, freq="D")
+
+    model = ExponentialSmoothing()
+    model.fit(target)
+    prediction = model.predict(90, num_samples=500)
+
+    fig, axes = pyplot.subplots(figsize=(20, 12))
+    target.plot()
+    prediction.plot()
+    pyplot.legend()
+    return fig
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+    **Pypi Download Stats 📈 with Darts Forecasting**: see live download stats for popular open-source libraries 🤗 along with a 3 month forecast using Darts. The [ source code for this Gradio demo is here](https://huggingface.co/spaces/gradio/timeseries-forecasting-with-darts/blob/main/app.py).
+    """
+    )
+    with gr.Row():
+        lib = gr.Dropdown(
+            ["pandas", "scikit-learn", "torch", "prophet", "darts"],
+            label="Library",
+            value="darts",
+        )
+        time = gr.Dropdown(
+            ["3 months", "6 months", "9 months", "12 months"],
+            label="Downloads over the last...",
+            value="12 months",
+        )
+
+    plt = gr.Plot()
+
+    lib.change(get_forecast, [lib, time], plt, queue=False)
+    time.change(get_forecast, [lib, time], plt, queue=False)
+    demo.load(get_forecast, [lib, time], plt, queue=False)
+
+demo.launch()