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import gradio as gr
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import numpy as np
import statsmodels.api as sm
import os
# Load main metrics
df = pd.read_csv("data/india_growth_metrics.csv")
df.columns = df.columns.str.strip()
cities = df['City'].unique()
metrics = df.columns[1:-1] # Exclude City and Gini Coefficient
# Cluster setup
scaler = StandardScaler()
cluster_data = scaler.fit_transform(df[metrics])
kmeans = KMeans(n_clusters=4, random_state=0).fit(cluster_data)
df['Cluster'] = kmeans.labels_
def create_bar_chart(selected_cities, selected_metric):
"""Create bar chart for selected cities and metric"""
if not selected_cities or not selected_metric:
return None
filtered = df[df['City'].isin(selected_cities)]
fig = px.bar(filtered, x='City', y=selected_metric, color='City',
title=f"{selected_metric} Comparison")
return fig
def create_radar_chart(selected_cities):
"""Create radar chart for selected cities"""
if not selected_cities:
return None
radar_df = df[df['City'].isin(selected_cities)].set_index('City')
melted = radar_df[metrics].reset_index().melt(id_vars='City', var_name='Metric', value_name='Value')
fig = px.line_polar(melted, r='Value', theta='Metric', color='City',
line_close=True, title="Radar View")
fig.update_traces(fill='toself')
return fig
def create_correlation_matrix(selected_cities):
"""Create correlation matrix for selected cities"""
if not selected_cities:
return None
corr = df[df['City'].isin(selected_cities)][metrics].corr()
fig = px.imshow(corr, text_auto=True, title="Correlation Matrix")
return fig
def generate_ai_insights(selected_cities):
"""Generate AI insights for selected cities"""
if not selected_cities:
return "Please select cities to generate insights."
insights = []
for city in selected_cities:
city_data = df[df['City'] == city]
if not city_data.empty:
highest = city_data[metrics].idxmax(axis=1).values[0]
lowest = city_data[metrics].idxmin(axis=1).values[0]
insights.append(f"๐๏ธ **{city}** excels in **{highest}** but needs improvement in **{lowest}**.")
return "\n".join(insights) if insights else "No insights available."
def find_twin_cities(selected_cities):
"""Find twin cities for selected cities"""
if not selected_cities:
return "Please select cities to find twin cities."
twin_info = []
for city in selected_cities:
city_data = df[df['City'] == city]
if not city_data.empty:
city_vec = city_data[metrics].values
df_temp = df.copy()
df_temp['distance'] = np.linalg.norm(df_temp[metrics].values - city_vec, axis=1)
nearest = df_temp[df_temp['City'] != city].sort_values('distance').iloc[0]['City']
twin_info.append(f"๐๏ธ **{city}**'s most similar city is **{nearest}**.")
return "\n".join(twin_info) if twin_info else "No twin cities found."
def create_time_series(selected_metric, selected_city):
"""Create time series forecast for selected metric and city"""
if not selected_metric or not selected_city:
return None
try:
time_df = pd.read_csv("data/timeseries.csv", encoding='utf-8-sig')
time_df.columns = time_df.columns.str.strip()
time_df['City'] = time_df['City'].str.strip()
fig = go.Figure()
city_df = time_df[time_df['City'] == selected_city].sort_values('Year')
city_df['Year'] = pd.to_datetime(city_df['Year'], format='%Y')
if selected_metric in city_df.columns:
ts = city_df.set_index('Year')[selected_metric]
fig.add_trace(go.Scatter(x=ts.index, y=ts.values, mode='lines+markers',
name=selected_metric))
try:
model = sm.tsa.ARIMA(ts, order=(1, 1, 0)).fit()
forecast = model.get_forecast(steps=3)
forecast_index = pd.date_range(start=ts.index[-1] + pd.offsets.YearBegin(),
periods=3, freq='YS')
forecast_series = pd.Series(forecast.predicted_mean.values, index=forecast_index)
fig.add_trace(go.Scatter(x=forecast_series.index, y=forecast_series.values,
mode='lines+markers', name=f"{selected_metric} Forecast",
line=dict(dash='dash')))
except:
pass
fig.update_layout(title=f"Time Series Forecast: {selected_metric} in {selected_city}",
xaxis_title='Year')
return fig
except Exception as e:
return None
def create_cluster_view(metric):
"""Create cluster visualization"""
if not metric:
return None
fig = px.scatter(df, x=metric, y='Gini Coefficient', color='Cluster',
hover_data=['City'], title="City Clustering")
return fig
# Create Gradio interface
with gr.Blocks(title="India Growth Metrics Dashboard", theme=gr.themes.Soft()) as demo:
gr.Markdown("# ๐ฎ๐ณ Decode India: Smart AI-Enhanced Dashboard for Growth Metrics")
gr.Markdown("A powerful interactive dashboard that visualizes and analyzes growth metrics for 35 major Indian cities using AI, time series forecasting, and cluster-based insights.")
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### ๐ Dashboard Controls")
city_selector = gr.Dropdown(
choices=list(cities),
value=list(cities[:3]),
label="Select Cities",
multiselect=True
)
metric_selector = gr.Dropdown(
choices=list(metrics),
value=metrics[0],
label="Select Metric"
)
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### ๐ Bar Chart Comparison")
bar_chart = gr.Plot(label="Bar Chart")
with gr.Column(scale=1):
gr.Markdown("### ๐ Radar Chart")
radar_chart = gr.Plot(label="Radar Chart")
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### ๐งฎ Correlation Matrix")
correlation_matrix = gr.Plot(label="Correlation Matrix")
with gr.Column(scale=1):
gr.Markdown("### ๐ฏ City Clustering")
cluster_view = gr.Plot(label="Cluster View")
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### ๐ Time Series Forecast")
time_series = gr.Plot(label="Time Series")
with gr.Column(scale=1):
gr.Markdown("### ๐ง AI Insights")
ai_insights = gr.Markdown(label="AI Insights")
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### ๐ Twin City Recommendations")
twin_cities = gr.Markdown(label="Twin Cities")
# Event handlers
def update_all_outputs(selected_cities, selected_metric):
# Time series uses the first selected city
time_series_city = selected_cities[0] if selected_cities else None
bar_fig = create_bar_chart(selected_cities, selected_metric)
radar_fig = create_radar_chart(selected_cities)
corr_fig = create_correlation_matrix(selected_cities)
cluster_fig = create_cluster_view(selected_metric)
ai_md = generate_ai_insights(selected_cities)
twin_md = find_twin_cities(selected_cities)
ts_fig = create_time_series(selected_metric, time_series_city)
return bar_fig, radar_fig, corr_fig, cluster_fig, ts_fig, ai_md, twin_md
# A list of all outputs for convenience
all_outputs = [bar_chart, radar_chart, correlation_matrix, cluster_view, time_series, ai_insights, twin_cities]
# Connect inputs to outputs
city_selector.change(
fn=update_all_outputs,
inputs=[city_selector, metric_selector],
outputs=all_outputs
)
metric_selector.change(
fn=update_all_outputs,
inputs=[city_selector, metric_selector],
outputs=all_outputs
)
demo.load(
fn=update_all_outputs,
inputs=[city_selector, metric_selector],
outputs=all_outputs
)
# Launch the app
if __name__ == "__main__":
demo.launch()
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