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README.md

@@ -6,7 +6,6 @@ colorFrom: indigo
 colorTo: indigo
 sdk: gradio
 sdk_version: 3.4.1
-
-app_file: app.py
+app_file: run.py
 pinned: false
 ---

run.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import pypistats
+from datetime import date
+from dateutil.relativedelta import relativedelta
+import pandas as pd
+from prophet import Prophet
+pd.options.plotting.backend = "plotly"
+
+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.columns = ['ds','y']
+
+    m = Prophet()
+    m.fit(df1)
+    future = m.make_future_dataframe(periods=90)
+    forecast = m.predict(future)
+    fig1 = m.plot(forecast)
+    return fig1 
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+    """
+    **Pypi Download Stats 📈 with Prophet Forecasting**: see live download stats for popular open-source libraries 🤗 along with a 3 month forecast using Prophet. The [ source code for this Gradio demo is here](https://huggingface.co/spaces/gradio/timeseries-forecasting-with-prophet/blob/main/app.py).
+    """)
+    with gr.Row():
+        lib = gr.Dropdown(["pandas", "scikit-learn", "torch", "prophet"], label="Library", value="pandas")
+        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()