feat: plot it explanation and spinner

pages/1_📈_Plotting.py

@@ -1,5 +1,7 @@
 import streamlit as st
 import pandas as pd
+import time
+
 
 st.header("Plotting Time Series Data")
 st.markdown("Users can load their time-series data in **.csv** format and select a particular feature and plot-type.\
@@ -23,15 +25,18 @@ def plot_collection(plot_only_this, collection, number_of_desired_plots=0):
       if number_of_desired_plots:
         if plots_count == number_of_desired_plots:
           break
-            
 
+            
 with st.sidebar:
-    plot = st.radio("Select the kind of visualization:",('Plot feature collection', 'Compare users', 'Plot distribution'))
+    plot = st.radio("Select the kind of visualization:",('Feature collection', 'Users comparison', 'Data distribution'))
     file = st.file_uploader("Load CSV file", accept_multiple_files = False)
     if file:
         df = pd.read_csv(file, index_col = False)
         # df.index = df['Unnamed: 0'].tolist()
-        del df['Unnamed: 0']
+        try:
+            del df['Unnamed: 0']
+        except KeyError:
+            pass
         if 'df' not in st.session_state:
                 st.session_state['df'] = df
         st.success("Your data has been successfully loaded! 🤗")
@@ -45,13 +50,22 @@ else:
 df_base = st.session_state.df if 'df' in list(st.session_state.keys()) else pd.DataFrame()
 n = len(df_base)
 col1, col2 = st.columns(2)
-with col1:
-    if n:
+
+if not df_base.empty:
+    with col1:
         st.info(f"Your data has {n} samples.")
         slider_range = list(range(n))
         n_plot = st.slider("How many samples do you want to plot in the same graph", slider_range[0]+1, slider_range[-1]+1, 5)
         st.write(f"Action: {plot} using {n_plot} samples")
-        st.button("Plot now! 📊")
-
-if not df_base.empty:
+        plot_it = st.button("Plot now! 📊")
+        if plot_it:
+            with st.spinner(f"Drawing plot to visualize {plot.lower()}"):
+                while True:
+                    time.sleep(4)
+            # create a DataFrame with each run in the columns (depending on n)
+            with st.expander("See explanation"):
+                st.write(\"\"\"
+                    The chart above shows...
+                \"\"\")
+else:
     st.warning("Consider running outlier detection to clean your data!", icon="⚠️")