first upload

app.py

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+import gradio as gr
+import pypistats
+from datetime import date
+from dateutil.relativedelta import relativedelta
+import pandas as pd
+from fbprophet import Prophet
+
+
+pd.options.plotting.backend = "plotly"
+
+def get_data(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]))
+    data = data[(data['date'] > str(start_date))] 
+    return data
+
+def get_plot2(lib, time):
+    df = get_data(lib,time)
+    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 
+
+def get_plot(lib, time):
+    data = get_data(lib,time)
+    chart = data.plot(x="date", y="downloads")
+    return chart
+
+with gr.Blocks() as demo:
+
+    gr.Markdown(
+    """
+    ## Pypi Download Stats 📈
+    See live download stats for all of Hugging Face's open-source libraries 🤗
+    """)
+    with gr.Row():
+        lib = gr.Dropdown(["transformers", "datasets", "huggingface-hub", "gradio"], label="Library")
+        time = gr.Dropdown(["3 months", "6 months", "9 months", "12 months"], label="Downloads over the last...")
+
+    plt = gr.Plot()
+
+    lib.change(get_plot2, [lib, time], plt)
+    time.change(get_plot2, [lib, time], plt)    
+    demo.load(get_plot2, [lib, time], plt)    
+
+demo.launch()

requirements.txt

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+pypistats
+plotly
+pystan==2.19.1.1
+prophet