style table

src/read_logs.py

@@ -20,9 +20,9 @@ class LogAnalyser:
 
         self.path = container_param.selectbox(
             'Select the log path', log_file_paths)
-        self.df_path=f'data/df/{self.path.split("/")[-1]}'
-        if 'button1_counter' not in st.session_state:
-            st.session_state.button1_counter = 0
+        self.df_path = f'data/df/{self.path.split("/")[-1]}'
+        # if 'button1_counter' not in st.session_state:
+        #     st.session_state.button1_counter = 0
         # if 'df' not in st.session_state:
         #     self.df=0
         st.markdown(self.get_text())
@@ -41,14 +41,14 @@ class LogAnalyser:
         '''
 
     def display_logs(self):
-        self.container_param.markdown(
-            f'st.session_state.button1_counter: {st.session_state.button1_counter}')
-        
+        # self.container_param.markdown(
+        #     f'st.session_state.button1_counter: {st.session_state.button1_counter}')
+
         if exists(self.df_path):
             self.df = pd.read_csv(self.df_path)
         else:
             self.df = self.get_log()
-            
+
         # if 'path' not in st.session_state:
         #     st.session_state.path=self.path
         # if 'df' not in st.session_state or st.session_state.path!=self.path:
@@ -56,6 +56,7 @@ class LogAnalyser:
         #     st.session_state.path=self.path
 
         self.update_df()
+
     def update_df(self):
         reaction_weight = set_input(self.container_param,
                                     label='Reaction Weight w', min_value=0.0, max_value=1.0, value=0.5, step=0.01,
@@ -73,12 +74,16 @@ class LogAnalyser:
                                      step=.01,)
         table_mode = self.container_param.radio(
             "Table Style:", ["Dataframe", "Table"])
-        # self.show_pe_data = self.container_param.checkbox('Show Probability Emote', value=True, key='show_pe_data_log')
+        self.show_pe_data = self.container_param.checkbox(
+            'Show Probability Emote', value=True, key='show_pe_data_log')
+        df_reaction_pattern = pd.DataFrame()
+        reaction_pattern_dict = dict()
         for story_id in self.df.Story.unique():
             reaction_num = 0
-
-            dfs = self.df[(self.df.Story == story_id)
-                          & (self.df.Turn == 'user')]
+            def get_subset_condition(data):
+                return (data.Story == story_id) & (data.Turn == 'user')
+            subset_condition = get_subset_condition(self.df)
+            dfs = self.df[subset_condition]
             for i, (index, row) in enumerate(dfs.iterrows()):
                 if row.Emotion == 'neutral':
                     reaction_show = False
@@ -96,50 +101,92 @@ class LogAnalyser:
                 self.df.at[index, 'probability_emote'] = probability_emote
                 self.df.at[index, 'random_value'] = random_value
                 self.df.at[index, 'reaction_show'] = reaction_show
+            s = ''
+            df_edit = self.df[get_subset_condition(self.df)].reaction_show.copy()
+            df_edit=df_edit.dropna()
+            for v in df_edit:
+                s += str(int(v))
+            # df_reaction_pattern.at[story_id]
+            # reaction_pattern_dict['story_id']=story_id
+            reaction_pattern_dict['reaction_pattern']=s
+            df_reaction_pattern = pd.concat(
+                [df_reaction_pattern, pd.DataFrame(reaction_pattern_dict, index=[f'Story_{story_id}'])])
+            # st.markdown(df_edit)
+            # st.markdown(s)
+
         for story_id in self.df.Story.unique():
             dfs = self.df[(self.df.Story == story_id)].copy()
-            for c in ['Unnamed: 0', 'Story']:
+            columns2hide = ['Unnamed: 0', 'Story', ]
+            if not self.show_pe_data:
+                columns2hide += [
+                    "reaction_frequency", "probability_emote", "random_value", "reaction_show"]
+            for c in columns2hide:
                 dfs.drop(c, axis=1, inplace=True)
+
             st.markdown(f'#### Story {story_id}')
             dfs = dfs.style
-            # if not self.show_pe_data:
-            #     dfs = dfs.hide_columns(["reaction_frequency", "probability_emote", "random_value", "reaction_show"]) 
-            dfs = dfs.apply(self.rower, axis=None).set_table_styles([{
+            dfs = dfs.hide_index()
+            if self.show_pe_data:
+                dfs = dfs.apply(self.dfstyle_color_text_col, axis=1)
+            # dfs = dfs.applymap(self.dfstyle_color_text)
+            dfs = dfs.apply(self.rower, axis=None)
+            dfs = dfs.set_table_styles([{
                 'selector': 'tr:hover',
                 'props': 'font-size: 1.01em;'  # background-color: #eeee66;
-                }]).applymap(self.dfstyle_color_text)    # .hide_index()
-                
+            }])   # .hide_index()
+
             if table_mode == 'Dataframe':
-                st.dataframe(dfs)  # set_na_rep(" ").s
+                st.dataframe(dfs)
+                st.dataframe(df_reaction_pattern.iloc[story_id-1] )  # set_na_rep(" ").s
             elif table_mode == 'Table':
                 st.table(dfs)
-            print(dfs.render())
-
+                st.table(df_reaction_pattern.iloc[story_id-1] ) 
+            # print(dfs.render())
+        if table_mode == 'Dataframe':
+            st.dataframe(df_reaction_pattern)
+        elif table_mode == 'Table':
+            st.table(df_reaction_pattern)
 
     @staticmethod
     @st.cache
-    def dfstyle_color_text(val):
-        if type(val)==str:
-            color = 'red' if val =='neutral' else 'black'
-        # elif type(val)==float:
-        #     color = 'red' if val > .50000 else 'black'
-        elif val==None:
-            color = '#ffffff' 
+    def dfstyle_color_text_col(s):
+        result = ['background-color: white']*len(s)
+        if s.Emotion == 'neutral' and s.Turn == 'user':
+            result[2:-1] = ['color: #992222']+['color: #333333']+['color: #fcfcfc']*3
+        if s.reaction_show == 1:
+            result[-1] = 'color: #222222'
+        elif s.reaction_show == 0:
+            result[-1] = 'color: #222222'
         else:
-            color = 'black'
-        return 'color: %s' % color
-        
+            print(s.reaction_show)
+            print(type(s.reaction_show))
+            result[4:] = ['color: #fcfcfc']*4
+        # if s.probability_emote!=s.probability_emote:
+        #     result[5] = 'color: #eeeeee'
+        return result
+    # @staticmethod
+    # @st.cache
+    # def dfstyle_color_text(val):
+    #     if type(val)==str:
+    #         color = 'red' if val =='neutral' else 'black'
+    #     # elif type(val)==float:
+    #     #     color = 'red' if val > .50000 else 'black'
+    #     elif val==None:
+    #         color = '#ffffff'
+    #     else:
+    #         color = None
+    #     return 'color: %s' % color if color is not None else ''
+
     @staticmethod
     @st.cache
     def rower(data):
         s = data.index % 2 != 0
         s = pd.concat([pd.Series(s)] * data.shape[1],
-                      axis=1) 
-        z = pd.DataFrame(np.where(s, 'background-color:#f2f2f2', ''),
+                      axis=1)
+        z = pd.DataFrame(np.where(s, 'background-color:#f9f9f9', ''),
                          index=data.index, columns=data.columns)
         return z
 
-    
     def get_log(self):
         df = pd.DataFrame(data=[], columns=[])
         log_dict = dict()