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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() |