app.py

import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

# Streamlit page setup
st.set_page_config(page_title="Legislative Bill Analysis", layout="wide")
st.title("Legislative Bill Analysis Dashboard")

# File uploader
uploaded_file = st.file_uploader("Upload Illinois_Entire_Data_Insights_Final_v2.csv", type=["csv", "xlsx"])

if uploaded_file:
    # Read file
    if uploaded_file.name.endswith('.csv'):
        df = pd.read_csv(uploaded_file)
    else:
        df = pd.read_excel(uploaded_file)

    st.success("File uploaded and read successfully!")

    # Preprocessing date and year
    df['status_date'] = pd.to_datetime(df['status_date'], errors='coerce')
    df['year'] = df['status_date'].dt.year

    # ------------------------
    #  Visualization 1: Yearly Bills by Intent
    # ------------------------
    st.header(" Bills Over Time by Intent")
    yearly_intent_counts = df.groupby(['year', 'intent_standardized']).size().reset_index(name='bill_count')

    fig1 = px.bar(
        yearly_intent_counts,
        x='year',
        y='bill_count',
        color='intent_standardized',
        title='Bills Over Time by Intent',
        labels={'year': 'Year', 'bill_count': 'Number of Bills', 'intent_standardized': 'Intent'},
        barmode='group',
        height=500,
        color_discrete_sequence=px.colors.qualitative.Set2
    )
    fig1.update_layout(
        xaxis=dict(tickangle=0),
        legend_title_text='Intent',
        plot_bgcolor='white',
        paper_bgcolor='white',
        font=dict(color='black'),
        title_font=dict(size=20)
    )
    st.plotly_chart(fig1, use_container_width=True)

    # ------------------------
    #  Visualization 2: Animated Stance Distribution by Policy Area
    # ------------------------
    st.header("Stance Distribution Across Policy Areas (Animated by Year)")
    grouped = df.groupby(['year', 'policy_impact_areas_standardized', 'stance_standardized']).size().reset_index(name='count')

    fig2 = px.bar(
        grouped,
        x='count',
        y='policy_impact_areas_standardized',
        color='stance_standardized',
        orientation='h',
        animation_frame='year',
        title='Stance Distribution Across Policy Areas (Animated by Year)',
        labels={
            'count': 'Number of Bills',
            'policy_impact_areas_standardized': 'Policy Area',
            'stance_standardized': 'Stance'
        },
        height=600,
        color_discrete_sequence=px.colors.qualitative.Set2
    )
    fig2.update_layout(
        legend_title='Stance',
        xaxis_title='Number of Bills',
        yaxis_title='Policy Area',
        plot_bgcolor='white',
        paper_bgcolor='white',
        font=dict(color='black'),
        title_font=dict(size=20),
        margin=dict(t=60, l=150)
    )
    st.plotly_chart(fig2, use_container_width=True)

    # ------------------------
    #  Visualization 3: Sankey Diagram - Intent → Beneficiaries → Increasing Aspects
    # ------------------------
    st.header("🔗 Top Intent → Beneficiaries → Increasing Aspect Flows (Sankey)")

    def shorten(text, max_len=35):
        return text if len(text) <= max_len else text[:max_len] + "..."

    sankey_data = df[['intent_standardized', 'intended_beneficiaries_standardized', 'increasing_aspects_standardized']].dropna()
    path_counts = (
        sankey_data.groupby(['intent_standardized', 'intended_beneficiaries_standardized', 'increasing_aspects_standardized'])
        .size()
        .reset_index(name='count')
        .sort_values(by='count', ascending=False)
    )

    TOP_N = 15
    filtered_paths = path_counts.head(TOP_N)
    unique_labels = pd.unique(filtered_paths[['intent_standardized', 'intended_beneficiaries_standardized', 'increasing_aspects_standardized']].values.ravel())
    short_labels = [shorten(label) for label in unique_labels]
    label_to_index = {label: i for i, label in enumerate(unique_labels)}
    label_to_short = dict(zip(unique_labels, short_labels))

    sources = list(filtered_paths['intent_standardized'].map(label_to_index))
    targets = list(filtered_paths['intended_beneficiaries_standardized'].map(label_to_index))
    values = list(filtered_paths['count'])

    sources += list(filtered_paths['intended_beneficiaries_standardized'].map(label_to_index))
    targets += list(filtered_paths['increasing_aspects_standardized'].map(label_to_index))
    values += list(filtered_paths['count'])

    fig3 = go.Figure(data=[go.Sankey(
        arrangement="snap",
        node=dict(
            pad=25,
            thickness=20,
            line=dict(color="black", width=0.3),
            label=[label_to_short[label] for label in unique_labels],
            color="lightsteelblue"
        ),
        link=dict(
            source=sources,
            target=targets,
            value=values,
            color="rgba(150,150,150,0.4)"
        )
    )])

    fig3.update_layout(
        title_text="Top Intent → Beneficiaries → Increasing Aspect Flows",
        font_size=12,
        height=600,
        margin=dict(l=50, r=50, t=80, b=30)
    )
    st.plotly_chart(fig3, use_container_width=True)

else:
    st.info(" Please upload a dataset file to view the visualizations.")