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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()
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