Fix app

app.py

@@ -6,6 +6,7 @@ import numpy as np
 import pmdarima as pm
 import matplotlib.pyplot as plt
 from pmdarima import auto_arima
+import plotly.graph_objects as go
 
 import torch
 from transformers import pipeline, TapasTokenizer, TapasForQuestionAnswering
@@ -211,8 +212,7 @@ def get_converted_answer(table, query):
 
 
 # Web Application
-
-st.title("Sales Forecasting Dashboard 📈")
+st.title("📈 SalesCast Forecasting Dashboard")
 st.subheader("Welcome User, start using the application by uploading your file in the sidebar!")
 
 # Session States
@@ -222,9 +222,6 @@ if 'uploaded' not in st.session_state:
 if 'preprocessed_data' not in st.session_state:
     st.session_state.preprocessed_data = None
 
-if 'fitted_models' not in st.session_state:
-    st.session_state.fitted_models = {}
-
 # Sidebar Menu
 with st.sidebar:
     uploaded_file = st.file_uploader("Upload your Store Data here (must atleast contain Date and Sale)", type=["csv"])
@@ -297,18 +294,53 @@ if (st.session_state.uploaded):
 
     st.title("Forecasted Sales")
 
-    plt.figure(figsize=(12,8))
-    plt.plot(training_y[-80:])
-    plt.plot(test_y, color = 'red', label = 'Actual Sales')
-    plt.plot(fitted_series, color='darkgreen', label = 'Predicted Sales')
-    plt.fill_between(lower_series.index,
-                     lower_series,
-                     upper_series,
-                     color='k', alpha=.15)
-
-    plt.title("SARIMAX - Forecast of Retail Sales VS Actual Sales")
-    plt.legend(loc='upper left', fontsize=8)
-    plt.show()
+    # Plot
+    # plt.figure(figsize=(12,8))
+    # plt.plot(training_y[-80:])
+    # plt.plot(test_y, color = 'red', label = 'Actual Sales')
+    # plt.plot(fitted_series, color='darkgreen', label = 'Predicted Sales')
+    # plt.fill_between(lower_series.index,
+    #                  lower_series,
+    #                  upper_series,
+    #                  color='k', alpha=.15)
+
+    # plt.title("SARIMAX - Forecast of Retail Sales VS Actual Sales")
+    # plt.legend(loc='upper left', fontsize=8)
+    # plt.show()
+
+    trace_actual = go.Scatter(x=range(len(training_y) - 80, len(training_y)),
+                              y=training_y[-80:],
+                              mode='lines',
+                              name='Training Data')
+
+    trace_actual_sales = go.Scatter(x=range(len(training_y), len(training_y) + len(test_y)),
+                                    y=test_y,
+                                    mode='lines',
+                                    name='Actual Sales',
+                                    line=dict(color='red'))
+
+    trace_predicted_sales = go.Scatter(x=range(len(training_y), len(training_y) + len(fitted_series)),
+                                       y=fitted_series,
+                                       mode='lines',
+                                       name='Predicted Sales',
+                                       line=dict(color='darkgreen'))
+
+    trace_fill_between = go.Scatter(x=list(range(len(training_y), len(training_y) + len(lower_series))) +
+                                       list(range(len(training_y) + len(lower_series), len(training_y) + len(upper_series))),
+                                    y=list(lower_series) + list(upper_series)[::-1],
+                                    fill='toself',
+                                    fillcolor='rgba(0,100,80,0.2)',
+                                    line=dict(color='rgba(255,255,255,0)'),
+                                    name='Prediction Interval')
+
+    # Combine traces and create layout
+    data = [trace_actual, trace_actual_sales, trace_predicted_sales, trace_fill_between]
+    layout = go.Layout(title="SARIMAX - Forecast of Retail Sales VS Actual Sales",
+                       legend=dict(x=0, y=1.0),
+                       xaxis=dict(title='X-axis Label'),
+                       yaxis=dict(title='Y-axis Label'))
+    fig_test = go.Figure(data=data, layout=layout)
+    st.plotly_chart(fig_test)
 
     # Forecast (actual)
     n_periods = forecast_period
@@ -323,16 +355,45 @@ if (st.session_state.uploaded):
     future_upper_series = pd.Series(confint[:, 1], index=future_index_of_fc)
 
     # Plot
-    plt.figure(figsize=(12,8))
-    plt.plot(df['Sales'][-50:])
-    plt.plot(future_fitted_series, color='darkgreen')
-    plt.fill_between(future_lower_series.index,
-                     future_lower_series,
-                     future_upper_series,
-                     color='k', alpha=.15)
-
-    plt.title("SARIMA - Final Forecast of Retail Sales")
-    plt.show()
+    # plt.figure(figsize=(12,8))
+    # plt.plot(df['Sales'][-50:])
+    # plt.plot(future_fitted_series, color='darkgreen')
+    # plt.fill_between(future_lower_series.index,
+    #                  future_lower_series,
+    #                  future_upper_series,
+    #                  color='k', alpha=.15)
+
+    # plt.title("SARIMA - Final Forecast of Retail Sales")
+    # plt.show()
+
+    # Create traces for each line and fill_between
+    trace_sales = go.Scatter(x=df.index[-50:],
+                            y=df['Sales'][-50:],
+                            mode='lines',
+                            name='Sales')
+
+    trace_predicted_sales = go.Scatter(x=df.index[-50:] + future_fitted_series.index,
+                                       y=future_fitted_series,
+                                       mode='lines',
+                                       name='Predicted Sales',
+                                       line=dict(color='darkgreen'))
+
+    trace_fill_between = go.Scatter(x=list(df.index[-50:] + future_lower_series.index) +
+                                       list(df.index[-50:] + future_upper_series.index[::-1]),
+                                    y=list(future_lower_series) + list(future_upper_series)[::-1],
+                                    fill='toself',
+                                    fillcolor='rgba(0,100,80,0.2)',
+                                    line=dict(color='rgba(255,255,255,0)'),
+                                    name='Prediction Interval')
+
+    # Combine traces and create layout
+    data = [trace_sales, trace_predicted_sales, trace_fill_between]
+    layout = go.Layout(title="SARIMA - Final Forecast of Retail Sales",
+                       legend=dict(x=0, y=1.0),
+                       xaxis=dict(title='X-axis Label'),
+                       yaxis=dict(title='Y-axis Label'))
+    fig_final = go.Figure(data=data, layout=layout)
+    st.plotly_chart(fig_final)
 
     auto_sales_growth = sales_growth(df, future_fitted_series)
     df = auto_sales_growth
@@ -343,7 +404,10 @@ if (st.session_state.uploaded):
     st.write("Forecasted sales in the next 3 months")
     st.write(df)
 
-    question = st.text_input('Ask a Question about the Forecasted Data', placeholder="What is the total sales in the month of December?")
-    if question:
+    with st.form("question_form"):
+        question = st.text_input('Ask a Question about the Forecasted Data', placeholder="What is the total sales in the month of December?")
+        query_button = st.form_submit_button(label='Generate Answer')
+
+    if query_button:
         answer = get_converted_answer(df, question)
         st.write("The answer is:", answer)