app.py

import pandas as pd
from prophet import Prophet
import gradio as gr
import plotly.graph_objs as go

# Function to train the model and generate forecast
def predict_sales(time_frame):
    all_sales_data = pd.read_csv('/content/All sales - House of Pizza.csv')

    # Clean up the 'Total paid' column
    amount = all_sales_data['Total paid'].str.replace('₨', '', regex=False)
    amount = amount.str.replace(',', '', regex=False)
    amount = amount.str.strip()
    amount = amount.astype(float)

    # Convert the 'Date' column to datetime, coercing errors
    all_sales_data['Date'] = pd.to_datetime(all_sales_data['Date'], format='%m/%d/%Y %H:%M', errors='coerce')

    # Drop rows with invalid dates
    all_sales_data = all_sales_data.dropna(subset=['Date'])

    # Prepare the DataFrame
    df = pd.DataFrame({
        'Date': all_sales_data['Date'],
        'Total paid': amount
    })

    # Prepare Prophet model
    model = Prophet()
    df['ds'] = df['Date']
    df['y'] = df['Total paid']
    model.fit(df[['ds', 'y']])
    last_date_value = df['Date'].iloc[-2]
    # Future forecast based on the time frame
    future_periods = {
        '7 days': 7 * 24 * 60,
        '10 days': 10 * 24 * 60,
        '15 days': 15 * 24 * 60,
        '1 month': 30 * 24 * 60
    }

    # Get the future time based on the selected time frame
    future_time = model.make_future_dataframe(periods=future_periods[time_frame], freq='T')
    current_time = pd.Timestamp.now()

    # Set the last historical date in the format 'MM/DD/YYYY HH:MM'
    last_historical_date = pd.to_datetime(last_date_value, format='%m/%d/%Y %H:%M')

    # Filter future_time to include rows from the current time and onwards, including future hours and minutes
    future_only = future_time[(future_time['ds'] >= current_time) & (future_time['ds'] > last_historical_date)]
    forecast = model.predict(future_only)

    # Display the forecasted data
    forecast_table = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(future_periods[time_frame])
    
    # Create a Plotly graph
    fig = go.Figure()
    fig.add_trace(go.Scatter(
        x=forecast['ds'], y=forecast['yhat'],
        mode='lines+markers',
        name='Forecasted Sales',
        line=dict(color='orange'),
        marker=dict(size=6),
        hovertemplate='Date: %{x}<br>Forecasted Sales: %{y}<extra></extra>'
    ))
    
    fig.update_layout(
        title='Sales Forecast using Prophet',
        xaxis_title='Date and Time',
        yaxis_title='Sales Price',
        xaxis=dict(tickformat="%Y-%m-%d %H:%M"),
        yaxis=dict(autorange=True)
    )

    return forecast_table, fig

# Gradio interface
def run_gradio():
    # Create the Gradio Interface
    time_options = ['7 days', '10 days', '15 days', '1 month']
    gr.Interface(
        fn=predict_sales,  # Function to be called
        inputs=gr.components.Dropdown(time_options, label="Select Forecast Time Range"),  # User input
        outputs=[
            gr.components.Dataframe(label="Forecasted Sales Table"),  # Forecasted data in tabular form
            gr.components.Plot(label="Sales Forecast Plot")  # Plotly graph output
        ],
        title="Sales Forecasting with Prophet",
        description="Select a time range for the forecast and click on the button to train the model and see the results."
    ).launch(debug=True)

# Run the Gradio interface
if __name__ == '__main__':
    run_gradio()