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3dcb535 d3bf697 3dcb535 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | import streamlit as st
import pandas as pd
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import seaborn as sns
#trial
# Set Streamlit page config
st.set_page_config(page_title="Legislative Visualizations", layout="wide")
st.title("Legislative Bill Analysis Dashboard")
# Upload dataset
uploaded_file = st.file_uploader("Illinois_Entire_Data_Insights_Final_v2.csv", type=["csv", "xlsx"])
if uploaded_file:
# Load dataset
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!")
# Sankey Diagram
st.header("🔗 Sankey Diagram: Intent → Stance → Beneficiaries")
sankey_df = df[['intent_standardized', 'stance_standardized', 'intended_beneficiaries_standardized']].dropna()
if not sankey_df.empty:
labels = list(pd.unique(sankey_df['intent_standardized'].tolist() +
sankey_df['stance_standardized'].tolist() +
sankey_df['intended_beneficiaries_standardized'].tolist()))
label_map = {label: i for i, label in enumerate(labels)}
intent_stance = sankey_df.groupby(['intent_standardized', 'stance_standardized']).size().reset_index(name='count')
stance_beneficiary = sankey_df.groupby(['stance_standardized', 'intended_beneficiaries_standardized']).size().reset_index(name='count')
source = intent_stance['intent_standardized'].map(label_map).tolist() + stance_beneficiary['stance_standardized'].map(label_map).tolist()
target = intent_stance['stance_standardized'].map(label_map).tolist() + stance_beneficiary['intended_beneficiaries_standardized'].map(label_map).tolist()
value = intent_stance['count'].tolist() + stance_beneficiary['count'].tolist()
fig_sankey = go.Figure(data=[go.Sankey(
node=dict(pad=15, thickness=20, line=dict(color="black", width=0.5), label=labels),
link=dict(source=source, target=target, value=value)
)])
fig_sankey.update_layout(title_text="Sankey: Intent → Stance → Beneficiary", font_size=12)
st.plotly_chart(fig_sankey, use_container_width=True)
else:
st.warning("Sankey input columns contain only null values or are missing.")
# Heatmap
st.header("🧯 Heatmap: Category vs Policy Impact Area")
heat_df = df[['category_&_subcategory_standardized', 'policy_impact_areas_standardized']].dropna()
if not heat_df.empty:
heat = heat_df.pivot_table(index='category_&_subcategory_standardized',
columns='policy_impact_areas_standardized',
aggfunc=len,
fill_value=0)
plt.figure(figsize=(14, 8))
sns.heatmap(heat, cmap='coolwarm', annot=False)
plt.title("Heatmap: Category vs Policy Impact Area")
plt.xlabel("Policy Impact Area")
plt.ylabel("Category")
plt.tight_layout()
st.pyplot(plt)
else:
st.warning("Heatmap input columns contain only null values or are missing.")
else:
st.info("Please upload a dataset file to view the visualizations.")
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