Merge branch 'main' of https://huggingface.co/spaces/Thamed-Chowdhury/BAMS

app.py

@@ -28,6 +28,14 @@ if st.button("Generate Dataset"):
     # Convert 'Publish Date' column to datetime with 'day-month-year' format
     df3['Publish Date'] = pd.to_datetime(df3['Publish Date'], format='%d-%m-%Y')
 
+    # Fixing date formats
+    for i in range(len(df3)):
+        if '/' in df3['Accident Date'][i]:
+            day=int(df3['Accident Date'][i].split('/')[0])
+            mon=int(df3['Accident Date'][i].split('/')[1])
+            yr=int(df3['Accident Date'][i].split('/')[2])
+            df3['Publish Date'][i]=f"{day}-{mon}-{yr}"
+    print(df3.tail())
     # Convert user input dates to datetime
     start_date = pd.to_datetime(start_string, format='%d-%m-%Y')
     end_date = pd.to_datetime(end_string, format='%d-%m-%Y')
@@ -38,70 +46,91 @@ if st.button("Generate Dataset"):
 
     # Display the filtered data
     st.dataframe(filtered_entries)
-    
     # Create a bar chart for accident count over days
     if not filtered_entries.empty:
-        # Accident count over days
-        accident_counts = filtered_entries['Accident Date'].value_counts().sort_index()
-        accident_counts = accident_counts.reset_index()
-        accident_counts.columns = ['Accident Date', 'Accident Count']
-
-        fig1 = px.bar(accident_counts, 
-                      x='Accident Date', 
-                      y='Accident Count', 
-                      title="Accident Count Over Days",
-                      labels={'Accident Date': 'Date', 'Accident Count': 'Number of Accidents'})
-        st.plotly_chart(fig1)
-
-        # Bar chart showing number of people killed each day
-        # Grouping by 'Publish Date' and summing 'Killed' column
-        killed_per_day = filtered_entries.groupby('Accident Date')['Killed'].sum().reset_index()
-        killed_per_day.columns = ['Accident Date', 'Total Killed']
-        
-        fig2 = px.bar(killed_per_day, 
-                      x='Accident Date', 
-                      y='Total Killed', 
-                      title="Number of People Killed Each Day",
-                      labels={'Accident Date': 'Date', 'Total Killed': 'Number of People Killed'},
-                      color='Total Killed',
-                      color_continuous_scale='Reds')
-        st.plotly_chart(fig2)
-
-        # Bar chart showing the number of accidents in each district
-        district_accidents = filtered_entries['District'].value_counts().reset_index()
-        district_accidents.columns = ['District', 'Number of Accidents']
-        fig3 = px.bar(district_accidents, 
-                      x='District', 
-                      y='Number of Accidents',
-                      title="Accidents in Each District",
-                      labels={'Number of Accidents': 'Number of Accidents', 'District': 'District'},
-                      color='Number of Accidents',
-                      color_continuous_scale='Blues')
-        st.plotly_chart(fig3)
-
-        ### Pie Chart Code ###
-        yes_count=0
-        no_count=0
-        not_available_count=0
-        for i in range(len(filtered_entries)):
-            if ('Yes' in filtered_entries['Pedestrian_Involved'][i] or 'yes' in filtered_entries['Pedestrian_Involved'][i]): yes_count+=1
-            if ('No' in filtered_entries['Pedestrian_Involved'][i] or 'no' in filtered_entries['Pedestrian_Involved'][i]): no_count+=1
-            if ('Not Available' in filtered_entries['Pedestrian_Involved'][i]): not_available_count+=1
-        Pedestrian_Involved_list = ['Yes', 'No', 'Not Available']
-        Count_list = [yes_count, no_count, not_available_count]
-        # dictionary of lists 
-        dict = {'Pedestrian Involved': Pedestrian_Involved_list, 'Count':Count_list} 
-        pedestrian_involvement = pd.DataFrame(dict)
-        # Pie chart showing the percentage of accidents involving pedestrians vs. those that don't
-        # pedestrian_involvement = filtered_entries['Pedestrian_Involved'].value_counts().reset_index()
-        # pedestrian_involvement.columns = ['Pedestrian Involved', 'Count']
-
-        fig4 = px.pie(pedestrian_involvement, 
-                        names='Pedestrian Involved', 
-                        values='Count', 
-                        title="Accidents Involving Pedestrians",
-                        labels={'Pedestrian Involved': 'Pedestrian Involved'})
-        st.plotly_chart(fig4)
+        # Create a bar chart for accident count over days
+        if not filtered_entries.empty:
+            import plotly.express as px
+
+            # Convert 'Accident Date' to datetime format
+            filtered_entries['Accident Date'] = pd.to_datetime(filtered_entries['Accident Date'], format='%d-%m-%Y')
+
+            # Count accidents per date and sort by date
+            accident_counts = filtered_entries['Accident Date'].value_counts().sort_index()
+
+            # Reset the index and rename columns
+            accident_counts = accident_counts.reset_index()
+            accident_counts.columns = ['Accident Date', 'Accident Count']
+
+            # Convert 'Accident Date' back to string format
+            accident_counts['Accident Date'] = accident_counts['Accident Date'].dt.strftime('%d-%m-%Y')
+            filtered_entries['Accident Date'] = accident_counts['Accident Date']
+            fig1 = px.bar(accident_counts, 
+                        x='Accident Date', 
+                        y='Accident Count', 
+                        title="Accident Count Over Days",
+                        labels={'Accident Date': 'Date', 'Accident Count': 'Number of Accidents'},
+                        color='Accident Count',
+                        color_continuous_scale='Viridis')
+            st.plotly_chart(fig1)
+        # Convert 'Accident Date' to datetime format
+            filtered_entries['Accident Date'] = pd.to_datetime(filtered_entries['Accident Date'], format='%d-%m-%Y')
+
+            # Group by 'Accident Date' and sum the 'Killed' column
+            killed_per_day = filtered_entries.groupby('Accident Date')['Killed'].sum().reset_index()
+            killed_per_day.columns = ['Accident Date', 'Total Killed']
+
+            # Sort the dates in ascending order
+            killed_per_day = killed_per_day.sort_values(by='Accident Date')
+
+            # Convert 'Accident Date' back to string format
+            killed_per_day['Accident Date'] = killed_per_day['Accident Date'].dt.strftime('%d-%m-%Y')
+
+            fig2 = px.bar(killed_per_day, 
+                            x='Accident Date', 
+                            y='Total Killed', 
+                            title="Number of People Killed Each Day",
+                            labels={'Accident Date': 'Date', 'Total Killed': 'Number of People Killed'},
+                            color='Total Killed',
+                            color_continuous_scale='Reds')
+            st.plotly_chart(fig2)
+
+            # Bar chart showing the number of accidents in each district
+            district_accidents = filtered_entries['District'].value_counts().reset_index()
+            district_accidents.columns = ['District', 'Number of Accidents']
+            fig3 = px.bar(district_accidents, 
+                            x='District', 
+                            y='Number of Accidents',
+                            title="Accidents in Each District",
+                            labels={'Number of Accidents': 'Number of Accidents', 'District': 'District'},
+                            color='Number of Accidents',
+                            color_continuous_scale='Cividis')
+            st.plotly_chart(fig3)
+
+            ### Pie Chart Code ###
+            yes_count=0
+            no_count=0
+            not_available_count=0
+            for i in range(len(filtered_entries)):
+                if ('Yes' in filtered_entries['Pedestrian_Involved'][i] or 'yes' in filtered_entries['Pedestrian_Involved'][i]): yes_count+=1
+                if ('No' in filtered_entries['Pedestrian_Involved'][i] or 'no' in filtered_entries['Pedestrian_Involved'][i]): no_count+=1
+                if ('Not Available' in filtered_entries['Pedestrian_Involved'][i]): not_available_count+=1
+            Pedestrian_Involved_list = ['Yes', 'No', 'Not Available']
+            Count_list = [yes_count, no_count, not_available_count]
+            # dictionary of lists 
+            dict = {'Pedestrian Involved': Pedestrian_Involved_list, 'Count':Count_list} 
+            pedestrian_involvement = pd.DataFrame(dict)
+            # Pie chart showing the percentage of accidents involving pedestrians vs. those that don't
+            # pedestrian_involvement = filtered_entries['Pedestrian_Involved'].value_counts().reset_index()
+            # pedestrian_involvement.columns = ['Pedestrian Involved', 'Count']
+
+            fig4 = px.pie(pedestrian_involvement, 
+                            names='Pedestrian Involved', 
+                            values='Count', 
+                            title="Accidents Involving Pedestrians",
+                            labels={'Pedestrian Involved': 'Pedestrian Involved'},
+                            color_discrete_sequence=['Green', 'Red', 'Blue'])
+            st.plotly_chart(fig4)
         
     else:
         st.write("No data available for the selected date range.")