Create app.py

app.py

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+import pandas as pd
+from prophet import Prophet
+import plotly.express as px
+import gradio as gr
+from prophet.plot import plot_plotly, plot_components_plotly
+
+
+def forecast_timeseries(data_file, forecast_periods):
+    # Load the input data
+
+    df = pd.read_csv(data_file.name,encoding='utf-8')
+    df['WEIGHT_LBS'] = df['WEIGHT_LBS'].astype('int64')
+    df = df.rename(columns={'RECV_DATE': 'ds', 'WEIGHT_LBS': 'y'})
+
+    weekly_df = df[['ds','y']].sort_values('ds')
+    weekly_df = weekly_df.groupby('ds',as_index=False)['y'].sum()
+    weekly_df['ds'] =  pd.to_datetime(weekly_df['ds'])
+    weekly_df.set_index('ds', inplace=True)
+    weekly_df = weekly_df.resample('w').sum()
+    weekly_df.reset_index(inplace=True)
+
+    train = weekly_df[:int(0.8*(weekly_df.shape[0]))]
+    test = weekly_df[int(0.8*(weekly_df.shape[0]))+1:]
+
+    m = Prophet()
+    m.fit(train[['ds','y']])
+
+
+    # Create a future dataframe for forecasting
+    future = m.make_future_dataframe(periods=int(forecast_periods),freq='W')
+
+    # Make predictions
+    forecast = m.predict(future)
+
+    # Extract relevant columns from the forecast
+    forecast_data = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]
+
+
+    # Plot the forecasted data with upper and lower bounds
+    # fig = px.line(forecast, x='ds', y='yhat', title='Forecasted Time Series')
+    # fig.add_scatter(x=df['ds'], y=df['y'], mode='markers', name='Actual Values')
+    # fig.add_scatter(x=forecast['ds'], y=forecast['yhat_upper'], mode='lines', name='Upper Bound')
+    # fig.add_scatter(x=forecast['ds'], y=forecast['yhat_lower'], mode='lines', name='Lower Bound')
+
+    fig  = plot_plotly(m, forecast)
+    fig1 = plot_components_plotly(m, forecast)
+
+    
+
+
+    return df.head(),fig,fig1 ,forecast_data
+
+
+# Define input and output interfaces for the Gradio app
+inputs = [
+    gr.inputs.File(label="Upload CSV File"),
+    gr.inputs.Number(label="Forecast Periods", default=7),
+]
+outputs = [
+    gr.Dataframe(label="Input Data"),
+    gr.Plot(label="Forecast Plot"),
+    gr.Plot(label="Trends"),
+    gr.Dataframe(label="Forecast Results"),
+]
+
+# Create the Gradio app
+iface = gr.Interface(fn=forecast_timeseries, inputs=inputs, outputs=outputs, title="Time Series Forecasting with Prophet")
+iface.launch(inline = False,share=True,debug=False)
+