Update app.py

app.py

@@ -1,50 +1,182 @@
+# import pandas as pd
+# from prophet import Prophet
+# import gradio as gr
+# import plotly.graph_objs as go
+# import numpy as np
+# import requests
+# # Function to train the model and generate forecast
+# def predict_sales(time_frame):
+#     #login
+#     url="https://livesystem.hisabkarlay.com/auth/login"
+#     payload={
+#       'username':'testuser',
+#       'password':'testuser',
+#       'client_secret':'3udPXhYSfCpktnls1C3TSzI96JLypqUGwJR05RHf',
+#       'client_id':'4',
+#       'grant_type':'password'
+#     }
+#     response=requests.post(url,data=payload)
+#     print(response.text)
+#     access_token=response.json()['access_token']
+#     print(access_token)
+#     #fetch all sell data
+#     per_page=-1
+#     url=f"https://livesystem.hisabkarlay.com/connector/api/sell?per_page={per_page}"
+#     headers={
+#       'Authorization':f'Bearer {access_token}'
+#     }
+#     response=requests.get(url,headers=headers)
+#     data=response.json()['data']
+#     date=[]
+#     amount=[]
+#     for item in data:
+#       date.append(item.get('transaction_date'))
+#       amount.append(float(item.get('final_total')))
+#     data_dict={
+#         'date':date,
+#         'amount':amount
+#     }
+#     data_frame=pd.DataFrame(data_dict)
+#     # Convert 'date' column to datetime format
+#     data_frame['date'] = pd.to_datetime(data_frame['date'])
+
+#     # Extract only the date part
+#     data_frame['date_only'] = data_frame['date'].dt.date
+
+#     # Group by date and calculate total sales
+#     daily_sales = data_frame.groupby('date_only').agg(total_sales=('amount', 'sum')).reset_index()
+
+#     # Prepare the DataFrame for Prophet
+#     df = pd.DataFrame({
+#         'Date': daily_sales['date_only'],
+#         'Total paid': daily_sales['total_sales']
+#     })
+
+#     # Apply log transformation
+#     df['y'] = np.log1p(df['Total paid'])  # Using log1p to avoid log(0)
+
+#     # Prepare Prophet model
+#     model = Prophet(weekly_seasonality=True)  # Enable weekly seasonality
+#     df['ds'] = df['Date']
+#     model.fit(df[['ds', 'y']])
+
+#     # Future forecast based on the time frame
+#     future_periods = {
+#         'Next Day': 1,
+#         '7 days': 7,
+#         '10 days': 10,
+#         '15 days': 15,
+#         '1 month': 30
+#     }
+
+#     # Get the last historical date and calculate the start date for the forecast
+#     last_date_value = df['Date'].iloc[-1]
+#     forecast_start_date = pd.Timestamp(last_date_value) + pd.Timedelta(days=1)  # Start the forecast from the next day
+
+#     # Generate the future time DataFrame starting from the day after the last date
+#     future_time = model.make_future_dataframe(periods=future_periods[time_frame], freq='D')
+
+#     # Filter future_time to include only future dates starting from forecast_start_date
+#     future_only = future_time[future_time['ds'] >= forecast_start_date]
+#     forecast = model.predict(future_only)
+
+#     # Exponentiate the forecast to revert back to the original scale
+#     forecast['yhat'] = np.expm1(forecast['yhat'])  # Use expm1 to handle the log transformation
+#     forecast['yhat_lower'] = np.expm1(forecast['yhat_lower'])  # Exponentiate lower bound
+#     forecast['yhat_upper'] = np.expm1(forecast['yhat_upper'])  # Exponentiate upper bound
+
+#     # Create a DataFrame for weekends only
+#     forecast['day_of_week'] = forecast['ds'].dt.day_name()  # Get the day name from the date
+#     weekends = forecast[forecast['day_of_week'].isin(['Saturday', 'Sunday'])]  # Filter for weekends
+
+#     # Display the forecasted data for the specified period
+#     forecast_table = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].head(future_periods[time_frame])
+#     weekend_forecast_table = weekends[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]  # Weekend forecast
+
+#     # 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>'
+#     ))
+
+#     # Add lines for yhat_lower and yhat_upper
+#     fig.add_trace(go.Scatter(
+#         x=forecast['ds'], y=forecast['yhat_lower'],
+#         mode='lines',
+#         name='Lower Bound',
+#         line=dict(color='red', dash='dash')
+#     ))
+
+#     fig.add_trace(go.Scatter(
+#         x=forecast['ds'], y=forecast['yhat_upper'],
+#         mode='lines',
+#         name='Upper Bound',
+#         line=dict(color='green', dash='dash')
+#     ))
+
+#     fig.update_layout(
+#         title='Sales Forecast using Prophet',
+#         xaxis_title='Date',
+#         yaxis_title='Sales Price',
+#         xaxis=dict(tickformat="%Y-%m-%d"),
+#         yaxis=dict(autorange=True)
+#     )
+
+#     return forecast_table, weekend_forecast_table, fig  # Return the forecast table, weekend forecast, and plot
+
+# # Gradio interface
+# def run_gradio():
+#     # Create the Gradio Interface
+#     time_options = ['Next Day', '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.Dataframe(label="Weekend Forecasted Sales Table"),  # Weekend forecast data
+#             gr.components.Plot(label="Sales Forecast Plot",min_width=500,scale=2)  # 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()
 import pandas as pd
 from prophet import Prophet
 import gradio as gr
 import plotly.graph_objs as go
 import numpy as np
-import requests
+
 # Function to train the model and generate forecast
 def predict_sales(time_frame):
-    #login
-    url="https://livesystem.hisabkarlay.com/auth/login"
-    payload={
-      'username':'testuser',
-      'password':'testuser',
-      'client_secret':'3udPXhYSfCpktnls1C3TSzI96JLypqUGwJR05RHf',
-      'client_id':'4',
-      'grant_type':'password'
-    }
-    response=requests.post(url,data=payload)
-    print(response.text)
-    access_token=response.json()['access_token']
-    print(access_token)
-    #fetch all sell data
-    per_page=-1
-    url=f"https://livesystem.hisabkarlay.com/connector/api/sell?per_page={per_page}"
-    headers={
-      'Authorization':f'Bearer {access_token}'
-    }
-    response=requests.get(url,headers=headers)
-    data=response.json()['data']
-    date=[]
-    amount=[]
-    for item in data:
-      date.append(item.get('transaction_date'))
-      amount.append(float(item.get('final_total')))
-    data_dict={
-        'date':date,
-        'amount':amount
-    }
-    data_frame=pd.DataFrame(data_dict)
-    # Convert 'date' column to datetime format
-    data_frame['date'] = pd.to_datetime(data_frame['date'])
+    all_sales_data = pd.read_csv('All sales - House of Pizza.csv')
+
+    # Clean up the 'Total paid' column by splitting based on '₨' symbol and converting to float
+    def clean_total_paid(val):
+        if isinstance(val, str):  # Only process if the value is a string
+            amounts = [float(x.replace(',', '').strip()) for x in val.split('₨') if x.strip()]
+            return sum(amounts)  # Sum if multiple values exist
+        elif pd.isna(val):  # Handle NaN values
+            return 0.0
+        return val  # If it's already a float, return it as-is
+
+    # Apply the cleaning function to the 'Total paid' column
+    all_sales_data['Total paid'] = all_sales_data['Total paid'].apply(clean_total_paid)
 
-    # Extract only the date part
-    data_frame['date_only'] = data_frame['date'].dt.date
+    # 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')
 
-    # Group by date and calculate total sales
-    daily_sales = data_frame.groupby('date_only').agg(total_sales=('amount', 'sum')).reset_index()
+    # Drop rows with invalid dates
+    all_sales_data = all_sales_data.dropna(subset=['Date'])
+    all_sales_data['date_only'] = all_sales_data['Date'].dt.date
+    daily_sales = all_sales_data.groupby('date_only').agg(total_sales=('Total paid', 'sum')).reset_index()
 
     # Prepare the DataFrame for Prophet
     df = pd.DataFrame({
@@ -139,7 +271,7 @@ def run_gradio():
         outputs=[
             gr.components.Dataframe(label="Forecasted Sales Table"),  # Forecasted data in tabular form
             gr.components.Dataframe(label="Weekend Forecasted Sales Table"),  # Weekend forecast data
-            gr.components.Plot(label="Sales Forecast Plot",min_width=500,scale=2)  # Plotly graph output
+            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."