Update app.py

app.py

@@ -1,57 +1,54 @@
 from dash import Dash, html, dcc, Input, Output
 import pandas as pd
 import plotly.express as px
-import os
-from kaggle.api.kaggle_api_extended import KaggleApi
 
-# Set Kaggle configuration directory
-os.environ['KAGGLE_CONFIG_DIR'] = os.path.expanduser('/app/kaggle')
+# # Load the dataset
+# df = pd.read_csv('C:/Users/aammann/OneDrive - Great American Insurance Group/Documents/cincinnati_traffic_crash_data__cpd.csv')
+# df['ZIP']=df['ZIP'].astype(str)
+# df.to_parquet("C:/Users/aammann/OneDrive - Great American Insurance Group/Documents/cincinnati_traffic_crash_data__cpd.parquet")
+df = pd.read_parquet('C:/Users/aammann/OneDrive - Great American Insurance Group/Documents/cincinnati_traffic_crash_data__cpd.parquet')
 
-# Ensure the directory exists
-os.makedirs(os.environ['KAGGLE_CONFIG_DIR'], exist_ok=True)
-
-# Initialize the Kaggle API
-api = KaggleApi()
-api.authenticate()
-
-# Download the dataset
-dataset_path = 'cincinnati-car-crash-data.csv'
-if not os.path.exists(dataset_path):
-    api.dataset_download_file('steverusso/cincinnati-car-crash-data', 'cincinnati-car-crash-data.csv', path='.')
-    # Unzip the downloaded file if necessary
-    import zipfile
-    with zipfile.ZipFile('cincinnati-car-crash-data.csv.zip', 'r') as zip_ref:
-        zip_ref.extractall('.')
-
-# Load the CSV file
-df = pd.read_csv(dataset_path)
+# Preprocess the data
+df_grouped = df.groupby('SNA_NEIGHBORHOOD')['DATECRASHREPORTED'].count().reset_index()
+df_grouped.columns = ['Neighborhood', 'Total Crashes']
 
 # Initialize the Dash app
 app = Dash(__name__)
 
+# Create a bar chart
+fig = px.bar(df_grouped, x='Neighborhood', y='Total Crashes', title='Total Traffic Crashes by Neighborhood in Cincinnati')
+
 # Define the layout of the app
-app.layout = html.Div([
+app.layout = html.Div(children=[
+    html.H1(children='Cincinnati Traffic Crashes Dashboard'),
+
+    html.Div(children='''Dash: A web application framework for your data.'''),
+
     dcc.Dropdown(
-        id='city-dropdown',
-        options=[{'label': city, 'value': city} for city in df['City'].unique()],
-        value='SF'
+        id='neighborhood-dropdown',
+        options=[{'label': n, 'value': n} for n in df_grouped['Neighborhood']],
+        value=df_grouped['Neighborhood'][0]
     ),
-    dcc.Graph(id='bar-chart')
+
+    dcc.Graph(
+        id='example-graph',
+        figure=fig
+    )
 ])
 
-# Define the callback to update the graph
+# Callback to update graph based on dropdown selection
 @app.callback(
-    Output('bar-chart', 'figure'),
-    [Input('city-dropdown', 'value')]
+    Output('example-graph', 'figure'),
+    [Input('neighborhood-dropdown', 'value')]
 )
-def update_chart(selected_city):
-    filtered_df = df[df['City'] == selected_city]
-    fig = px.bar(filtered_df, x='Fruit', y='Amount', color='Fruit', barmode='group')
+def update_graph(selected_neighborhood):
+    filtered_df = df_grouped[df_grouped['Neighborhood'] == selected_neighborhood]
+    fig = px.bar(filtered_df, x='Neighborhood', y='Total Crashes', title=f'Total Traffic Crashes in {selected_neighborhood}')
     return fig
 
 # Run the app
 if __name__ == '__main__':
-    app.run_server(debug=True, host='0.0.0.0', port=7860)
+    app.run(debug=True, port=8051)