Delete app.py

app.py

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-import streamlit as st
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-import plotly.express as px
-from datetime import datetime
-
-# Set page config
-st.set_page_config(page_title="Nuisance Complaints Dashboard", layout="wide")
-
-# Title and introduction
-st.title("Nuisance Complaints Analysis Dashboard")
-st.markdown("""
-**Team Members:**
-* Lu Chang (luchang2@illinois.edu)
-* Qiming Li (qimingl4@illinois.edu)
-* Ruchita Alate (ralate2@illinois.edu)
-* Shreyas Kulkarni (ssk16@illinois.edu)
-* Vishal Devulapalli (nsd3@illinois.edu)
-""")
-st.write("This dashboard analyzes nuisance complaints data from the City of Urbana.")
-
-# Load and clean data
-@st.cache_data
-def load_and_clean_data():
-    try:
-        # Load data
-        data = pd.read_csv('Nuisance_Complaints_20241204.csv')
-
-        # Drop rows with missing 'File Number'
-        data = data.dropna(subset=['File Number'])
-
-        # Convert dates and handle date-related columns
-        data['Date Reported'] = pd.to_datetime(data['Date Reported'])
-        data['Date Notice Mailed or Given'] = pd.to_datetime(data['Date Notice Mailed or Given'])
-        data['File Close Date'] = pd.to_datetime(data['File Close Date'], errors='coerce')
-
-        # Handle 'Date Notice Mailed or Given'
-        median_delay = (data['Date Notice Mailed or Given'] - data['Date Reported']).dt.days.median()
-        data.loc[data['Date Notice Mailed or Given'].isna(), 'Date Notice Mailed or Given'] = \
-            data.loc[data['Date Notice Mailed or Given'].isna(), 'Date Reported'] + pd.Timedelta(days=median_delay)
-
-        # Handle 'Type of Complaint'
-        data['Type of Complaint'] = data['Type of Complaint'].fillna('Unknown')
-
-        # Handle 'Disposition'
-        most_common_disposition = data.groupby('Type of Complaint')['Disposition'].agg(
-            lambda x: x.mode().iloc[0] if len(x.mode()) > 0 else 'Pending'
-        )
-        data['Disposition'] = data.apply(
-            lambda row: most_common_disposition[row['Type of Complaint']]
-            if pd.isna(row['Disposition']) else row['Disposition'],
-            axis=1
-        )
-
-        # Calculate processing time for resolved cases
-        data['Processing Time'] = np.where(
-            data['File Close Date'].notna(),
-            (data['File Close Date'] - data['Date Reported']).dt.days,
-            np.nan
-        )
-
-        # Handle 'Method Submitted'
-        data.loc[
-            (data['Submitted Online?']) & (data['Method Submitted'].isna()),
-            'Method Submitted'
-        ] = 'Online'
-        data['Method Submitted'] = data['Method Submitted'].fillna(data['Method Submitted'].mode()[0])
-
-        # Drop rows with missing critical values
-        data = data.dropna(subset=['Submitted Online?', 'Mapped Location'])
-
-        # Extract and clean location data
-        data['Latitude'] = data['Mapped Location'].str.extract(r'\(([^,]+),')[0].astype(float)
-        data['Longitude'] = data['Mapped Location'].str.extract(r', ([^,]+)\)')[0].astype(float)
-
-        # Ensure Year Reported is integer
-        data['Year Reported'] = data['Year Reported'].astype(int)
-
-        return data
-
-    except Exception as e:
-        st.error(f"Error in data preprocessing: {str(e)}")
-        raise e
-
-# Load the data
-try:
-    data = load_and_clean_data()
-    st.success("Data successfully loaded and cleaned!")
-except Exception as e:
-    st.error(f"Error loading data: {str(e)}")
-    st.stop()
-
-# Create sidebar
-
-st.sidebar.header("Dashboard Controls")
-
-# Get unique years and convert to list for selectbox
-year_list = sorted(data['Year Reported'].unique().tolist())
-year_options = ['All Time'] + [int(year) for year in year_list]  # Convert years to integers
-
-selected_year = st.sidebar.selectbox(
-    "Select Year",
-    options=year_options,
-)
-# Add visualization type selector
-viz_type = st.sidebar.selectbox(
-    "Select Visualization",
-    ["Complaint Types", "Geographic Distribution", "Resolution Status",
-     "Submission Methods", "Processing Time"]
-)
-
-
-# Filter data based on selected year
-if selected_year == 'All Time':
-    filtered_data = data  # Use complete dataset when 'All Time' is selected
-else:
-    filtered_data = data[data['Year Reported'] == selected_year]
-
-# Update header text
-if selected_year == 'All Time':
-    st.header("Analysis for All Time")
-else:
-    st.header(f"Analysis for Year {selected_year}")
-# Main content
-
-# Create metrics
-# Create metrics
-# Create metrics
-# Create metrics
-# Create metrics
-col1, col2, col3 = st.columns(3)
-with col1:
-    st.metric("Total Complaints", len(filtered_data))
-with col2:
-    avg_time = filtered_data['Processing Time'].mean()
-    st.metric("Average Processing Time", f"{avg_time:.1f} days" if pd.notna(avg_time) else "N/A")
-with col3:
-    if not filtered_data.empty:
-        most_common = filtered_data['Type of Complaint'].value_counts().index[0]
-        st.metric("Most Common Type", most_common)
-    else:
-        st.metric("Most Common Type", "N/A")
-if viz_type == "Complaint Types":
-    # Interactive Pie Chart
-    st.subheader("Interactive Complaint Types Pie Chart")
-    complaint_counts = filtered_data['Type of Complaint'].value_counts().reset_index()
-    complaint_counts.columns = ['Complaint Type', 'Count']
-
-    fig = px.pie(
-        complaint_counts,
-        names='Complaint Type',
-        values='Count',
-        title=f'Complaint Types Distribution in {selected_year}',
-        hole=0.4  # Donut style
-    )
-    fig.update_traces(textinfo='percent+label')
-    st.plotly_chart(fig, use_container_width=True)
-
-elif viz_type == "Geographic Distribution":
-    # Clustered Heatmap
-    st.subheader("Clustered Heatmap of Complaints")
-    map_center = [filtered_data['Latitude'].mean(), filtered_data['Longitude'].mean()]
-    m = folium.Map(location=map_center, zoom_start=12)
-
-    heat_data = filtered_data[['Latitude', 'Longitude']].dropna().values.tolist()
-    HeatMap(heat_data).add_to(m)
-
-    st_data = st_folium(m, width=700, height=500)
-
-
-elif viz_type == "Resolution Status":
-    st.subheader("Complaint Resolution Status")
-    fig, ax = plt.subplots(figsize=(10, 6))
-    resolution_counts = filtered_data['Disposition'].value_counts()
-    sns.barplot(x=resolution_counts.values, y=resolution_counts.index)
-    plt.title(f'Resolution Status Distribution in {selected_year}')
-    st.pyplot(fig)
-
-elif viz_type == "Submission Methods":
-    st.subheader("Submission Methods Analysis")
-    fig, ax = plt.subplots(figsize=(10, 6))
-    submission_counts = filtered_data['Method Submitted'].value_counts()
-    sns.barplot(x=submission_counts.values, y=submission_counts.index)
-    plt.title(f'Submission Methods in {selected_year}')
-    st.pyplot(fig)
-
-
-elif viz_type == "Processing Time":
-    st.subheader("Processing Time Analysis")
-    # Filter for resolved cases only
-    resolved_data = filtered_data[filtered_data['File Close Date'].notna()]
-    if len(resolved_data) > 0:
-        fig, ax = plt.subplots(figsize=(10, 6))
-        sns.histplot(data=resolved_data, x='Processing Time', bins=30)
-        plt.title(f'Distribution of Processing Times in {selected_year}')
-        plt.xlabel('Processing Time (Days)')
-        st.pyplot(fig)
-    else:
-        st.write("No resolved cases in this period")
-
-# Additional insights
-st.header("Key Insights")
-col1, col2 = st.columns(2)
-
-with col1:
-    st.subheader("Top 3 Complaint Types")
-    top_complaints = filtered_data['Type of Complaint'].value_counts().head(3)
-    st.write(top_complaints)
-
-with col2:
-    st.subheader("Resolution Efficiency")
-    resolution_rate = (filtered_data['Disposition'].value_counts() /
-                      len(filtered_data) * 100).round(2)
-    st.write(resolution_rate)
-
-# Footer
-st.markdown("---")
-st.markdown("Dataset provided by the City of Urbana Open Data Portal")