diff --git a/scripts/plotly_streamlit.py b/scripts/plotly_streamlit.py
index d555e48..fe8fa08 100755
--- a/scripts/plotly_streamlit.py
+++ b/scripts/plotly_streamlit.py
@@ -6,13 +6,11 @@ from numpy import ma
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 import plotly.io as pio
-from datetime import timedelta, datetime
-from pathlib import Path
+from datetime import timedelta
 import sqlite3
 from sqlite3 import Connection
 import plotly.express as px
 from sklearn.preprocessing import normalize
-import time
 
 pio.templates.default = "plotly_white"
 
@@ -40,9 +38,8 @@ st.markdown("""
         """, unsafe_allow_html=True)
 
 
-
 @st.cache(hash_funcs={Connection: id})
-#@st.cache(allow_output_mutation=True)
+# @st.cache(allow_output_mutation=True)
 def get_connection(path: str):
     return sqlite3.connect(path, check_same_thread=False)
 
@@ -58,19 +55,18 @@ df2 = df.copy()
 df2['DateTime'] = pd.to_datetime(df2['Date'] + " " + df2['Time'])
 df2 = df2.set_index('DateTime')
 
-daily = st.sidebar.checkbox('Single Day View', help= 'Select if you want single day view, unselect for multi-day views')
+daily = st.sidebar.checkbox('Single Day View', help='Select if you want single day view, unselect for multi-day views')
 
 if daily:
-# Date as slider
+    # Date as slider
     Start_Date = pd.to_datetime(df2.index.min()).date()
     End_Date = pd.to_datetime(df2.index.max()).date()
 #     cols1, cols2 = st.columns((1, 1))
     end_date = st.sidebar.date_input('Date to View',
-                            min_value = Start_Date,
-                            max_value = End_Date,
-                            value=(End_Date),
-                            help= 'Select date for single day view'                                     
-                            )
+                                     min_value=Start_Date,
+                                     max_value=End_Date,
+                                     value=(End_Date),
+                                     help='Select date for single day view')
     start_date = end_date
 else:
     Start_Date = pd.to_datetime(df2.index.min()).date()
@@ -78,11 +74,10 @@ else:
 
 #    cols1, cols2 = st.columns((1, 1))
     start_date, end_date = st.sidebar.slider('Date Range',
-                                    min_value = Start_Date-timedelta(days=1),
-                                    max_value = End_Date,
-                                    value=(Start_Date, End_Date),
-                                    help= 'Select start and end date, if same date get a clockplot for a single day'                                        
-                                    )
+                                             min_value=Start_Date-timedelta(days=1),
+                                             max_value=End_Date,
+                                             value=(Start_Date, End_Date),
+                                             help='Select start and end date, if same date get a clockplot for a single day')
 
 # start_date, end_date = cols1.date_input(
 #     "Date Input for Analysis - select Range for single specie analysis, select single date for daily view",
@@ -93,12 +88,14 @@ else:
 # start_date = datetime(2022 ,5 ,17).date()
 # end_date = datetime(2022 ,5 ,17).date()
 
+
 @st.cache()
 def date_filter(df, start_date, end_date):
     filt = (df2.index >= pd.Timestamp(start_date)) & (df2.index <= pd.Timestamp(end_date + timedelta(days=1)))
     df = df[filt]
     return(df)
 
+
 df2 = date_filter(df2, start_date, end_date)
 
 st.write('<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: left;} </style>',
@@ -112,7 +109,7 @@ st.write('<style>div.st-bf{flex-direction:column;} div.st-ag{font-weight:bold;pa
 if start_date == end_date:
     resample_sel = st.sidebar.radio(
         "Resample Resolution",
-        ('Raw', '15 minutes', 'Hourly'), index=1, help= 'Select resolution for single day - larger times run faster' )
+        ('Raw', '15 minutes', 'Hourly'), index=1, help='Select resolution for single day - larger times run faster')
 
     resample_times = {'Raw': 'Raw',
                       '1 minute': '1min',
@@ -124,7 +121,7 @@ if start_date == end_date:
 else:
     resample_sel = st.sidebar.radio(
         "Resample Resolution",
-        ('Raw', '15 minutes', 'Hourly', 'DAILY'), index=1, help= 'Select resolution for species - DAILY provides time series')
+        ('Raw', '15 minutes', 'Hourly', 'DAILY'), index=1, help='Select resolution for species - DAILY provides time series')
 
     resample_times = {'Raw': 'Raw',
                       '1 minute': '1min',
@@ -134,6 +131,7 @@ else:
                       }
     resample_time = resample_times[resample_sel]
 
+
 @st.cache()
 def time_resample(df, resample_time):
     if resample_time == 'Raw':
@@ -143,6 +141,8 @@ def time_resample(df, resample_time):
         df_resample = df.resample(resample_time)['Com_Name'].aggregate('unique').explode()
 
     return(df_resample)
+
+
 top_bird = df2['Com_Name'].mode()[0]
 df5 = time_resample(df2, resample_time)
 
@@ -151,12 +151,12 @@ df5 = time_resample(df2, resample_time)
 Specie_Count = df5.value_counts()
 
 # Create Hourly Crosstab
-hourly = pd.crosstab(df5, df5.index.hour, dropna=True, margins= True)
+hourly = pd.crosstab(df5, df5.index.hour, dropna=True, margins=True)
 
 # Filter on species
-species = list(hourly.sort_values("All", ascending= False).index)
+species = list(hourly.sort_values("All", ascending=False).index)
 
-#cols1, cols2 = st.columns((1, 1))
+# cols1, cols2 = st.columns((1, 1))
 top_N = st.sidebar.slider(
     'Select Number of Birds to Show',
     min_value=1,
@@ -168,26 +168,26 @@ top_N_species = (df5.value_counts()[:top_N])
 
 font_size = 15
 
-if daily == False:
-    
+if daily is False:
+
     if resample_time != '1D':
         specie = st.selectbox(
-        'Which bird would you like to explore for the dates ' 
-        + str(start_date) + ' to ' + str(end_date) + '?', 
-        species,
-        index = 0)
-
+                'Which bird would you like to explore for the dates '
+                + str(start_date) + ' to ' + str(end_date) + '?',
+                species,
+                index=0)
 
     # filt = df2['Com_Name'] == specie
         if specie == 'All':
-            df_counts = int(hourly[hourly.index==specie]['All'])
+            df_counts = int(hourly[hourly.index == specie]['All'])
             fig = make_subplots(
                 rows=3, cols=2,
-                specs=[[{"type": "xy", "rowspan": 3}, {"type": "polar", "rowspan": 2}], [{"rowspan": 1}, {"rowspan": 1}],
-                    [None, {"type": "xy", "rowspan": 1}]],
+                specs=[[{"type": "xy", "rowspan": 3}, {"type": "polar", "rowspan": 2}],
+                       [{"rowspan": 1}, {"rowspan": 1}],
+                       [None, {"type": "xy", "rowspan": 1}]],
                 subplot_titles=('<b>Top ' + str(top_N) + ' Species in Date Range ' + str(start_date) + ' to ' + str(
                     end_date) + '<br>for ' + str(resample_sel) + ' sampling interval.' + '</b>',
-                                'Total Detect:' + str('{:,}'.format(df_counts)) 
+                                'Total Detect:' + str('{:,}'.format(df_counts))
                                 # +   '   Confidence Max:' + str(
                                 #     '{:.2f}%'.format(max(df2[df2['Com_Name'] == specie]['Confidence']) * 100)) +
                                 # '   ' + '   Median:' + str(
@@ -197,9 +197,9 @@ if daily == False:
             fig.layout.annotations[1].update(x=0.7, y=0.25, font_size=15)
 
             # Plot seen species for selected date range and number of species
-            
+
             fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h', marker_color='seagreen'), row=1, col=1)
-            
+
             fig.update_layout(
                 margin=dict(l=0, r=0, t=50, b=0),
                 yaxis={'categoryorder': 'total ascending'})
@@ -232,16 +232,17 @@ if daily == False:
                         rotation=-90,
                         direction='clockwise',
                         tickmode='array',
-                        tickvals=[0, 15, 35, 45, 60, 75, 90, 105, 120, 135, 150, 165, 180, 195, 210, 225, 240, 255, 270,
-                                285, 300, 315, 330, 345],
-                        ticktext=['12am', '1am', '2am', '3am', '4am', '5am', '6am', '7am', '8am', '9am', '10am', '11am',
-                                '12pm', '1pm', '2pm', '3pm', '4pm', '5pm', '6pm', '7pm', '8pm', '9pm', '10pm', '11pm'],
+                        tickvals=[0, 15, 35, 45, 60, 75, 90, 105, 120, 135, 150, 165, 180, 195, 210,
+                                  225, 240, 255, 270, 285, 300, 315, 330, 345],
+                        ticktext=['12am', '1am', '2am', '3am', '4am', '5am', '6am', '7am', '8am', '9am',
+                                  '10am', '11am', '12pm', '1pm', '2pm', '3pm', '4pm', '5pm', '6pm',
+                                  '7pm', '8pm', '9pm', '10pm', '11pm'],
                         hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
                     ),
                 ),
             )
 
-            daily = pd.crosstab(df5, df5.index.date, dropna=True, margins = True)
+            daily = pd.crosstab(df5, df5.index.date, dropna=True, margins=True)
             fig.add_trace(go.Bar(x=daily.columns[:-1], y=daily.loc[specie][:-1], marker_color='seagreen'), row=3, col=2)
             st.plotly_chart(fig, use_container_width=True)  # , config=config)
 
@@ -250,7 +251,7 @@ if daily == False:
             with col1:
                 fig = make_subplots(
                     rows=3, cols=1,
-                    specs=[[{"type": "polar", "rowspan": 2}],[{"rowspan": 1}], [{"type": "xy", "rowspan": 1}]]
+                    specs=[[{"type": "polar", "rowspan": 2}], [{"rowspan": 1}], [{"type": "xy", "rowspan": 1}]]
                             )
                 # Set 360 degrees, 24 hours for polar plot
                 theta = np.linspace(0.0, 360, 24, endpoint=False)
@@ -280,81 +281,75 @@ if daily == False:
                             rotation=-90,
                             direction='clockwise',
                             tickmode='array',
-                            tickvals=[0, 15, 35, 45, 60, 75, 90, 105, 120, 135, 150, 165, 180, 195, 210, 225, 240, 255, 270,
-                                    285, 300, 315, 330, 345],
-                            ticktext=['12am', '1am', '2am', '3am', '4am', '5am', '6am', '7am', '8am', '9am', '10am', '11am',
-                                    '12pm', '1pm', '2pm', '3pm', '4pm', '5pm', '6pm', '7pm', '8pm', '9pm', '10pm', '11pm'],
+                            tickvals=[0, 15, 35, 45, 60, 75, 90, 105, 120, 135, 150, 165, 180, 195,
+                                      210, 225, 240, 255, 270, 285, 300, 315, 330, 345],
+                            ticktext=['12am', '1am', '2am', '3am', '4am', '5am', '6am', '7am', '8am',
+                                      '9am', '10am', '11am', '12pm', '1pm', '2pm', '3pm', '4pm', '5pm',
+                                      '6pm', '7pm', '8pm', '9pm', '10pm', '11pm'],
                             hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
                         ),
                     ),
                 )
 
-                daily = pd.crosstab(df5, df5.index.date, dropna=True, margins = True)
+                daily = pd.crosstab(df5, df5.index.date, dropna=True, margins=True)
                 fig.add_trace(go.Bar(x=daily.columns[:-1], y=daily.loc[specie][:-1], marker_color='seagreen'), row=3, col=1)
                 st.plotly_chart(fig, use_container_width=True)  # , config=config)
-                df_counts = int(hourly[hourly.index==specie]['All'])
-                st.subheader('Total Detect:' + str('{:,}'.format(df_counts)) 
-                                +   '   Confidence Max:' + str(
-                                    '{:.2f}%'.format(max(df2[df2['Com_Name'] == specie]['Confidence']) * 100)) +
-                                '   ' + '   Median:' + str(
-                                    '{:.2f}%'.format(np.median(df2[df2['Com_Name'] == specie]['Confidence']) * 100)))
+                df_counts = int(hourly[hourly.index == specie]['All'])
+                st.subheader('Total Detect:' + str('{:,}'.format(df_counts))
+                             + '   Confidence Max:' +
+                             str('{:.2f}%'.format(max(df2[df2['Com_Name'] == specie]['Confidence']) * 100))
+                             + '   ' + '   Median:' +
+                             str('{:.2f}%'.format(np.median(df2[df2['Com_Name'] == specie]['Confidence']) * 100)))
 
+            recordings = df2[df2['Com_Name'] == specie]['File_Name']
 
-            recordings=df2[df2['Com_Name']==specie]['File_Name']
-            
-        
             with col2:
                 try:
                     recording = st.selectbox('Available recordings', recordings.sort_index(ascending=False))
-                    date_specie = df2.loc[df2['File_Name']==recording,['Date','Com_Name']]
+                    date_specie = df2.loc[df2['File_Name'] == recording, ['Date', 'Com_Name']]
                     date_dir = date_specie['Date'].values[0]
-                    specie_dir = date_specie['Com_Name'].values[0].replace(" ","_")
-                    st.image(userDir + '/BirdSongs/Extracted/By_Date/'+ date_dir + '/'+ specie_dir + '/' + recording + '.png')
-                    st.audio(userDir +'/BirdSongs/Extracted/By_Date/'+ date_dir + '/'+ specie_dir + '/' + recording)
-                except:
-                    st.title('RECORDING NOT AVAILABLE :(')    
+                    specie_dir = date_specie['Com_Name'].values[0].replace(" ", "_")
+                    st.image(userDir + '/BirdSongs/Extracted/By_Date/' + date_dir + '/' + specie_dir + '/' + recording + '.png')
+                    st.audio(userDir + '/BirdSongs/Extracted/By_Date/' + date_dir + '/' + specie_dir + '/' + recording)
+                except Exception:
+                    st.title('RECORDING NOT AVAILABLE :(')
             # try:
             #     con = sqlite3.connect(userDir + '/BirdNET-Pi/scripts/birds.db')
             #     cur = con.cursor()
-            cola, colb, colc, cold = st.columns((3,1,1,1))
+            cola, colb, colc, cold = st.columns((3, 1, 1, 1))
             with colb:
                 seen = st.checkbox('Reviewed')
             if seen:
                 with colc:
-                    verified = st.radio("Verification",['True Positive','False Positive'])
-            
+                    verified = st.radio("Verification", ['True Positive', 'False Positive'])
+
                     if verified == "False Positive":
-                        df_names = pd.read_csv(userDir+'/BirdNET-Pi/model/labels.txt', delimiter= '_', names=['Sci_Name', 'Com_Name']) 
-                        df_unknown= pd.DataFrame({"Sci_Name":["UNKNOWN"],"Com_Name":["UNKNOWN"]})
-                        df_names = pd.concat([df_unknown,df_names], ignore_index=True)
+                        df_names = pd.read_csv(userDir + '/BirdNET-Pi/model/labels.txt', delimiter='_', names=['Sci_Name', 'Com_Name'])
+                        df_unknown = pd.DataFrame({"Sci_Name": ["UNKNOWN"], "Com_Name": ["UNKNOWN"]})
+                        df_names = pd.concat([df_unknown, df_names], ignore_index=True)
                         with cold:
                             corrected = st.selectbox('What species?', df_names['Com_Name'])
             #     cur.execute("UPDATE detections SET  Seen = seen WHERE File_Name = recording")
             #     con.commit()
             #     con.close()
-            
+
             # except BaseException:
             #     print("Database busy")
             #     time.sleep(2)
-            
-            
+
     else:
 
-        specie = st.selectbox(
-        'Which bird would you like to explore for the dates ' 
-        + str(start_date) + ' to ' + str(end_date) + '?', 
-        species[1:],
-        index = 0)
-
+        specie = st.selectbox('Which bird would you like to explore for the dates '
+                              + str(start_date) + ' to ' + str(end_date) + '?',
+                              species[1:],
+                              index=0)
 
     # filt = df2[df2['Com_Name'] == specie]
 
-        df_counts = int(hourly[hourly.index==specie]['All'])
+        df_counts = int(hourly[hourly.index == specie]['All'])
         fig = st.container()
-        fig = make_subplots(
-                        rows=1, cols =1)
+        fig = make_subplots(rows=1, cols=1)
     #                     specs= [[{"type":"xy","rowspan":1},{"type":"heatmap","rowspan":1}]],
-                            
 
     #                     subplot_titles=('<b>Daily Top '+ str(top_N) + ' Species in Date Range '+ str(start_date) +' to '+ str(end_date) +'</b>',
     #                                     '<b>Daily ' + specie+ ' Detections on 15 minute intervals </b>'),
@@ -365,9 +360,9 @@ if daily == False:
     #                     )
 
 #        fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1,col=1)
-        df4=df2['Com_Name'][df2['Com_Name']==specie].resample('15min').count()
-        df4.index=[df4.index.date, df4.index.time]
-        day_hour_freq=df4.unstack().fillna(0)
+        df4 = df2['Com_Name'][df2['Com_Name'] == specie].resample('15min').count()
+        df4.index = [df4.index.date, df4.index.time]
+        day_hour_freq = df4.unstack().fillna(0)
 
         fig_x = [d.strftime('%d-%m-%Y') for d in day_hour_freq.index.tolist()]
         fig_y = [h.strftime('%H:%M') for h in day_hour_freq.columns.tolist()]
@@ -377,9 +372,9 @@ if daily == False:
         # fig.update_layout(
         # margin=dict(l=0, r=0, t=50, b=0),
         # yaxis={'categoryorder':'total ascending'})
-        color_pals= px.colors.named_colorscales()
+        color_pals = px.colors.named_colorscales()
         selected_pal = st.sidebar.selectbox('Select Color Pallet for Daily Detections', color_pals)
-        fig.add_trace(go.Heatmap(x=fig_x,y=fig_y,z=fig_z, autocolorscale = False, colorscale = selected_pal), row=1, col=1)
+        fig.add_trace(go.Heatmap(x=fig_x, y=fig_y, z=fig_z, autocolorscale=False, colorscale=selected_pal), row=1, col=1)
         st.plotly_chart(fig, use_container_width=True)  # , config=config)
 else:
     fig = make_subplots(
@@ -436,9 +431,7 @@ else:
     st.plotly_chart(fig, use_container_width=True)  # , config=config)
 
 # cols3,cols4=st.columns((1,1))
-# 
 # extract_date=Date_Slider
-# 
 # audio_file = open('/home/*/BirdSongs/Extracted/By_Date/2022-03-22/Yellow-streaked_Greenbul/Yellow-streaked_Greenbul-77-2022-03-22-birdnet-15:04:28.mp3', 'rb')
 # audio_bytes = audio_file.read()
 # cols4.audio(audio_bytes, format='audio/mp3')