Create app.py

app.py

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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('/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']])
+
+    # 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()