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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('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']])
# Future forecast based on the time frame
future_periods = {
'24 hours': 1 * 24 * 60,
'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')
forecast = model.predict(future_time)
# 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 = ['24 hours', '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()
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