Update app.py

app.py

@@ -5,7 +5,7 @@ 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')
+    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)
@@ -30,10 +30,9 @@ def predict_sales(time_frame):
     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 = {
-        '24 hours': 1 * 24 * 60,
         '7 days': 7 * 24 * 60,
         '10 days': 10 * 24 * 60,
         '15 days': 15 * 24 * 60,
@@ -42,7 +41,14 @@ def predict_sales(time_frame):
 
     # 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)
+    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])
@@ -71,7 +77,7 @@ def predict_sales(time_frame):
 # Gradio interface
 def run_gradio():
     # Create the Gradio Interface
-    time_options = ['24 hours', '7 days', '10 days', '15 days', '1 month']
+    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