Deploy India Growth Metrics Dashboard

README.md

@@ -1,12 +1,133 @@
+# 🇮🇳 Decode India: Smart AI-Enhanced Dashboard for Growth Metrics
+
+A powerful interactive dashboard that visualizes and analyzes growth metrics for 35 major Indian cities using AI, time series forecasting, and cluster-based insights.
+
+## 🚀 Live Demo
+
+**🌐 View the live dashboard**: [Hugging Face Spaces](https://huggingface.co/spaces/your-username/decode-india-dashboard)
+
+---
+
+## 🚀 Features
+
+- 📊 **Compare Development Metrics**: Visual comparisons between cities on metrics like health, education, infrastructure, etc.
+- 🔁 **Time Series Forecasting**: Uses ARIMA models to predict future values for selected indicators.
+- 🌐 **Radar Chart Visualization**: View cities' SDG performance across multiple indicators.
+- 🧠 **AI-Powered Insights**: Automatically highlights strongest and weakest areas for each city.
+- 🌍 **Twin City Matching**: Suggests cities with similar developmental patterns.
+- 🎯 **Clustering**: KMeans-based segmentation to group cities with similar development profiles.
+- 🧮 **Correlation Analysis**: Examine relationships between key indicators.
+
+---
+
+## 📁 Project Structure
+```
+Decode-India/
+│
+├── app.py # Main Dash application
+├── data/
+│ ├── india_growth_metrics.csv # City-wide metrics data
+│ └── timeseries.csv # Year-wise data (2015–2023) for forecasting
+├── requirements.txt # Python dependencies
+└── README.md # Instructions and documentation
+```
+
+---
+
+## 🌟 Deploy on Hugging Face Spaces (Recommended)
+
+### Step 1: Create Hugging Face Account
+1. Go to [huggingface.co](https://huggingface.co) and sign up
+2. Verify your email address
+
+### Step 2: Create a New Space
+1. Click on your profile → "New Space"
+2. Choose settings:
+   - **Owner**: Your username
+   - **Space name**: `decode-india-dashboard`
+   - **Space SDK**: **Gradio** (we'll use this for Dash)
+   - **License**: MIT
+   - **Visibility**: Public
+
+### Step 3: Upload Your Code
+1. Clone the space repository:
+```bash
+git clone https://huggingface.co/spaces/your-username/decode-india-dashboard
+cd decode-india-dashboard
+```
+
+2. Copy your files:
+```bash
+# Copy all your project files
+cp -r /path/to/your/project/* .
+```
+
+3. Push to Hugging Face:
+```bash
+git add .
+git commit -m "Initial deployment"
+git push
+```
+
+### Step 4: Your App is Live!
+Your dashboard will be available at:
+`https://huggingface.co/spaces/your-username/decode-india-dashboard`
+
 ---
-title: Codeclash
-emoji: 🐢
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 5.34.2
-app_file: app.py
-pinned: false
+
+## ⚙️ How to Run the Dashboard Locally
+
+### 1️⃣ Clone the Repository
+
+```bash
+git clone https://github.com/your-username/Decode-India.git
+cd Decode-India
+```
+### Create & Activate a Virtual Environment
+python -m venv .venv
+# Activate the environment:
+# On Windows
+```bash
+.venv\Scripts\activate
+```
+# On macOS/Linux
+```bash
+source .venv/bin/activate
+```
+### Install Required Dependencies
+```bash
+pip install -r requirements.txt
+```
+Or install individually:
+```bash
+pip install dash pandas plotly numpy scikit-learn statsmodels
+```
+### Add the Data Files
+Place the following files in the data/ folder:
+
+india_growth_metrics.csv – Contains city-wise growth indicators.
+
+timeseries.csv – Contains yearly data for each city for time series analysis (2015–2023)
+
+### Run the Application
+```bash
+python app.py
+```
+
+### View in Browser
+Visit: 
+
+```bash
+http://127.0.0.1:8050/
+```
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 🎯 Alternative Deployment Options
+
+See [DEPLOYMENT.md](DEPLOYMENT.md) for other deployment options including:
+- Railway
+- Render
+- Heroku
+- Docker
+- VPS/Cloud servers

app.py

@@ -0,0 +1,232 @@
+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"🏙️ **{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"🏙️ **{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("# 🇮🇳 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("### 📊 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"
+            )
+            
+            time_series_city = gr.Dropdown(
+                choices=list(cities),
+                value=cities[0],
+                label="Select City for Time Series"
+            )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 📈 Bar Chart Comparison")
+            bar_chart = gr.Plot(label="Bar Chart")
+        
+        with gr.Column(scale=1):
+            gr.Markdown("### 🌐 Radar Chart")
+            radar_chart = gr.Plot(label="Radar Chart")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 🧮 Correlation Matrix")
+            correlation_matrix = gr.Plot(label="Correlation Matrix")
+        
+        with gr.Column(scale=1):
+            gr.Markdown("### 🎯 City Clustering")
+            cluster_view = gr.Plot(label="Cluster View")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 📈 Time Series Forecast")
+            time_series = gr.Plot(label="Time Series")
+        
+        with gr.Column(scale=1):
+            gr.Markdown("### 🧠 AI Insights")
+            ai_insights = gr.Markdown(label="AI Insights")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 🌍 Twin City Recommendations")
+            twin_cities = gr.Markdown(label="Twin Cities")
+    
+    # Event handlers
+    def update_charts(cities, metric):
+        return (
+            create_bar_chart(cities, metric),
+            create_radar_chart(cities),
+            create_correlation_matrix(cities),
+            create_cluster_view(metric),
+            generate_ai_insights(cities),
+            find_twin_cities(cities)
+        )
+    
+    def update_time_series(metric, city):
+        return create_time_series(metric, city)
+    
+    # Connect inputs to outputs
+    city_selector.change(
+        update_charts,
+        inputs=[city_selector, metric_selector],
+        outputs=[bar_chart, radar_chart, correlation_matrix, cluster_view, ai_insights, twin_cities]
+    )
+    
+    metric_selector.change(
+        update_charts,
+        inputs=[city_selector, metric_selector],
+        outputs=[bar_chart, radar_chart, correlation_matrix, cluster_view, ai_insights, twin_cities]
+    )
+    
+    time_series_city.change(
+        update_time_series,
+        inputs=[metric_selector, time_series_city],
+        outputs=[time_series]
+    )
+    
+    metric_selector.change(
+        update_time_series,
+        inputs=[metric_selector, time_series_city],
+        outputs=[time_series]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

data/india_growth_metrics.csv

@@ -0,0 +1,38 @@
+City,Population Growth Rate,Education Index,Health Index,Infrastructure Quality Index,Digital Penetration Rate,Green Cover %,Employment Rate,Gini Coefficient
+Delhi,2.5,0.82,0.78,0.81,0.88,20.5,72.1,0.35
+Mumbai,2.3,0.84,0.76,0.79,0.91,18.3,70.5,0.37
+Bangalore,2.7,0.87,0.80,0.83,0.95,24.1,74.2,0.34
+Hyderabad,2.6,0.85,0.79,0.82,0.93,22.9,73.0,0.36
+Chennai,2.4,0.83,0.77,0.80,0.89,19.8,71.3,0.35
+Kolkata,2.2,0.80,0.75,0.76,0.86,21.4,69.9,0.38
+Ahmedabad,2.5,0.81,0.74,0.78,0.87,20.0,70.2,0.37
+Pune,2.6,0.86,0.79,0.81,0.92,23.0,72.5,0.34
+Jaipur,2.3,0.78,0.72,0.75,0.84,18.7,68.4,0.39
+Lucknow,2.1,0.76,0.70,0.72,0.82,17.2,67.1,0.40
+Kanpur,2.0,0.75,0.69,0.70,0.80,16.8,66.5,0.41
+Nagpur,2.4,0.82,0.76,0.77,0.88,21.1,70.0,0.36
+Indore,2.5,0.83,0.77,0.78,0.89,20.5,70.8,0.35
+Bhopal,2.3,0.80,0.74,0.76,0.85,19.2,69.3,0.37
+Patna,2.0,0.74,0.68,0.69,0.78,15.9,65.0,0.42
+Ranchi,2.1,0.75,0.69,0.70,0.79,16.2,65.7,0.41
+Raipur,2.2,0.76,0.70,0.71,0.80,16.5,66.1,0.40
+Chandigarh,2.6,0.88,0.82,0.84,0.94,25.0,75.2,0.33
+Amritsar,2.3,0.80,0.74,0.76,0.86,20.3,69.7,0.36
+Surat,2.7,0.84,0.77,0.79,0.90,22.0,71.8,0.35
+Vadodara,2.5,0.82,0.75,0.77,0.88,21.3,70.6,0.36
+Nashik,2.4,0.81,0.74,0.76,0.87,20.7,70.1,0.37
+Faridabad,2.3,0.80,0.73,0.75,0.85,19.5,69.2,0.38
+Meerut,2.2,0.79,0.72,0.74,0.83,18.9,68.0,0.39
+Rajkot,2.5,0.83,0.76,0.78,0.89,21.0,71.0,0.36
+Varanasi,2.1,0.77,0.70,0.72,0.81,17.4,66.8,0.40
+Madurai,2.3,0.80,0.73,0.75,0.85,19.1,69.0,0.37
+Coimbatore,2.4,0.82,0.75,0.77,0.87,20.0,70.3,0.36
+Jodhpur,2.2,0.78,0.71,0.73,0.83,18.2,67.9,0.39
+Gwalior,2.1,0.76,0.69,0.71,0.80,17.0,66.0,0.40
+Agra,2.2,0.77,0.70,0.72,0.82,17.8,67.2,0.39
+Thiruvananthapuram,2.5,0.89,0.84,0.85,0.95,26.1,76.0,0.32
+Vijayawada,2.4,0.81,0.74,0.76,0.86,19.9,70.0,0.37
+Visakhapatnam,2.5,0.83,0.76,0.78,0.88,21.1,71.2,0.36
+Ludhiana,2.3,0.79,0.72,0.74,0.84,18.8,68.5,0.38
+Jamshedpur,2.2,0.78,0.71,0.73,0.83,18.0,67.7,0.39
+Hubli,2.3,0.79,0.72,0.74,0.84,18.5,68.2,0.38

data/timeseries.csv

@@ -0,0 +1,334 @@
+City,Year,Population Growth Rate,Education Index,Health Index,Infrastructure Quality Index
+Delhi,2015,2.50,0.80,0.75,0.78
+Delhi,2016,2.52,0.81,0.76,0.79
+Delhi,2017,2.54,0.82,0.77,0.80
+Delhi,2018,2.56,0.83,0.78,0.81
+Delhi,2019,2.58,0.84,0.79,0.82
+Delhi,2020,2.60,0.85,0.80,0.83
+Delhi,2021,2.62,0.86,0.81,0.84
+Delhi,2022,2.64,0.87,0.82,0.85
+Delhi,2023,2.66,0.88,0.83,0.86
+Mumbai,2015,2.30,0.79,0.72,0.75
+Mumbai,2016,2.32,0.80,0.73,0.76
+Mumbai,2017,2.34,0.81,0.74,0.77
+Mumbai,2018,2.36,0.82,0.75,0.78
+Mumbai,2019,2.38,0.83,0.76,0.79
+Mumbai,2020,2.40,0.84,0.77,0.80
+Mumbai,2021,2.42,0.85,0.78,0.81
+Mumbai,2022,2.44,0.86,0.79,0.82
+Mumbai,2023,2.46,0.87,0.80,0.83
+Bangalore,2015,2.70,0.81,0.76,0.78
+Bangalore,2016,2.72,0.82,0.77,0.79
+Bangalore,2017,2.74,0.83,0.78,0.80
+Bangalore,2018,2.76,0.84,0.79,0.81
+Bangalore,2019,2.78,0.85,0.80,0.82
+Bangalore,2020,2.80,0.86,0.81,0.83
+Bangalore,2021,2.82,0.87,0.82,0.84
+Bangalore,2022,2.84,0.88,0.83,0.85
+Bangalore,2023,2.86,0.89,0.84,0.86
+Hyderabad,2015,2.60,0.80,0.75,0.76
+Hyderabad,2016,2.62,0.81,0.76,0.77
+Hyderabad,2017,2.64,0.82,0.77,0.78
+Hyderabad,2018,2.66,0.83,0.78,0.79
+Hyderabad,2019,2.68,0.84,0.79,0.80
+Hyderabad,2020,2.70,0.85,0.80,0.81
+Hyderabad,2021,2.72,0.86,0.81,0.82
+Hyderabad,2022,2.74,0.87,0.82,0.83
+Hyderabad,2023,2.76,0.88,0.83,0.84
+Chennai,2015,2.40,0.78,0.74,0.74
+Chennai,2016,2.42,0.79,0.75,0.75
+Chennai,2017,2.44,0.80,0.76,0.76
+Chennai,2018,2.46,0.81,0.77,0.77
+Chennai,2019,2.48,0.82,0.78,0.78
+Chennai,2020,2.50,0.83,0.79,0.79
+Chennai,2021,2.52,0.84,0.80,0.80
+Chennai,2022,2.54,0.85,0.81,0.81
+Chennai,2023,2.56,0.86,0.82,0.82
+Ahmedabad,2015,2.50,0.76,0.70,0.72
+Ahmedabad,2016,2.52,0.77,0.71,0.73
+Ahmedabad,2017,2.54,0.78,0.72,0.74
+Ahmedabad,2018,2.56,0.79,0.73,0.75
+Ahmedabad,2019,2.58,0.80,0.74,0.76
+Ahmedabad,2020,2.60,0.81,0.75,0.77
+Ahmedabad,2021,2.62,0.82,0.76,0.78
+Ahmedabad,2022,2.64,0.83,0.77,0.79
+Ahmedabad,2023,2.66,0.84,0.78,0.80
+Pune,2015,2.40,0.79,0.74,0.75
+Pune,2016,2.42,0.80,0.75,0.76
+Pune,2017,2.44,0.81,0.76,0.77
+Pune,2018,2.46,0.82,0.77,0.78
+Pune,2019,2.48,0.83,0.78,0.79
+Pune,2020,2.50,0.84,0.79,0.80
+Pune,2021,2.52,0.85,0.80,0.81
+Pune,2022,2.54,0.86,0.81,0.82
+Pune,2023,2.56,0.87,0.82,0.83
+Jaipur,2015,2.30,0.74,0.70,0.71
+Jaipur,2016,2.32,0.75,0.71,0.72
+Jaipur,2017,2.34,0.76,0.72,0.73
+Jaipur,2018,2.36,0.77,0.73,0.74
+Jaipur,2019,2.38,0.78,0.74,0.75
+Jaipur,2020,2.40,0.79,0.75,0.76
+Jaipur,2021,2.42,0.80,0.76,0.77
+Jaipur,2022,2.44,0.81,0.77,0.78
+Jaipur,2023,2.46,0.82,0.78,0.79
+Lucknow,2015,2.20,0.72,0.68,0.70
+Lucknow,2016,2.22,0.73,0.69,0.71
+Lucknow,2017,2.24,0.74,0.70,0.72
+Lucknow,2018,2.26,0.75,0.71,0.73
+Lucknow,2019,2.28,0.76,0.72,0.74
+Lucknow,2020,2.30,0.77,0.73,0.75
+Lucknow,2021,2.32,0.78,0.74,0.76
+Lucknow,2022,2.34,0.79,0.75,0.77
+Lucknow,2023,2.36,0.80,0.76,0.78
+Kanpur,2015,2.00,0.70,0.65,0.67
+Kanpur,2016,2.02,0.71,0.66,0.68
+Kanpur,2017,2.04,0.72,0.67,0.69
+Kanpur,2018,2.06,0.73,0.68,0.70
+Kanpur,2019,2.08,0.74,0.69,0.71
+Kanpur,2020,2.10,0.75,0.70,0.72
+Kanpur,2021,2.12,0.76,0.71,0.73
+Kanpur,2022,2.14,0.77,0.72,0.74
+Kanpur,2023,2.16,0.78,0.73,0.75
+Nagpur,2015,2.30,0.76,0.71,0.73
+Nagpur,2016,2.32,0.77,0.72,0.74
+Nagpur,2017,2.34,0.78,0.73,0.75
+Nagpur,2018,2.36,0.79,0.74,0.76
+Nagpur,2019,2.38,0.80,0.75,0.77
+Nagpur,2020,2.40,0.81,0.76,0.78
+Nagpur,2021,2.42,0.82,0.77,0.79
+Nagpur,2022,2.44,0.83,0.78,0.80
+Nagpur,2023,2.46,0.84,0.79,0.81
+Indore,2015,2.50,0.78,0.73,0.74
+Indore,2016,2.52,0.79,0.74,0.75
+Indore,2017,2.54,0.80,0.75,0.76
+Indore,2018,2.56,0.81,0.76,0.77
+Indore,2019,2.58,0.82,0.77,0.78
+Indore,2020,2.60,0.83,0.78,0.79
+Indore,2021,2.62,0.84,0.79,0.80
+Indore,2022,2.64,0.85,0.80,0.81
+Indore,2023,2.66,0.86,0.81,0.82
+Bhopal,2015,2.30,0.74,0.69,0.71
+Bhopal,2016,2.32,0.75,0.70,0.72
+Bhopal,2017,2.34,0.76,0.71,0.73
+Bhopal,2018,2.36,0.77,0.72,0.74
+Bhopal,2019,2.38,0.78,0.73,0.75
+Bhopal,2020,2.40,0.79,0.74,0.76
+Bhopal,2021,2.42,0.80,0.75,0.77
+Bhopal,2022,2.44,0.81,0.76,0.78
+Bhopal,2023,2.46,0.82,0.77,0.79
+Patna,2015,2.00,0.68,0.64,0.65
+Patna,2016,2.02,0.69,0.65,0.66
+Patna,2017,2.04,0.70,0.66,0.67
+Patna,2018,2.06,0.71,0.67,0.68
+Patna,2019,2.08,0.72,0.68,0.69
+Patna,2020,2.10,0.73,0.69,0.70
+Patna,2021,2.12,0.74,0.70,0.71
+Patna,2022,2.14,0.75,0.71,0.72
+Patna,2023,2.16,0.76,0.72,0.73
+Ranchi,2015,2.10,0.69,0.65,0.66
+Ranchi,2016,2.12,0.70,0.66,0.67
+Ranchi,2017,2.14,0.71,0.67,0.68
+Ranchi,2018,2.16,0.72,0.68,0.69
+Ranchi,2019,2.18,0.73,0.69,0.70
+Ranchi,2020,2.20,0.74,0.70,0.71
+Ranchi,2021,2.22,0.75,0.71,0.72
+Ranchi,2022,2.24,0.76,0.72,0.73
+Ranchi,2023,2.26,0.77,0.73,0.74
+Raipur,2015,2.20,0.70,0.66,0.67
+Raipur,2016,2.22,0.71,0.67,0.68
+Raipur,2017,2.24,0.72,0.68,0.69
+Raipur,2018,2.26,0.73,0.69,0.70
+Raipur,2019,2.28,0.74,0.70,0.71
+Raipur,2020,2.30,0.75,0.71,0.72
+Raipur,2021,2.32,0.76,0.72,0.73
+Raipur,2022,2.34,0.77,0.73,0.74
+Raipur,2023,2.36,0.78,0.74,0.75
+Chandigarh,2015,2.60,0.85,0.81,0.82
+Chandigarh,2016,2.62,0.86,0.82,0.83
+Chandigarh,2017,2.64,0.87,0.83,0.84
+Chandigarh,2018,2.66,0.88,0.84,0.85
+Chandigarh,2019,2.68,0.89,0.85,0.86
+Chandigarh,2020,2.70,0.90,0.86,0.87
+Chandigarh,2021,2.72,0.91,0.87,0.88
+Chandigarh,2022,2.74,0.92,0.88,0.89
+Chandigarh,2023,2.76,0.93,0.89,0.90
+Amritsar,2015,2.30,0.75,0.71,0.72
+Amritsar,2016,2.32,0.76,0.72,0.73
+Amritsar,2017,2.34,0.77,0.73,0.74
+Amritsar,2018,2.36,0.78,0.74,0.75
+Amritsar,2019,2.38,0.79,0.75,0.76
+Amritsar,2020,2.40,0.80,0.76,0.77
+Amritsar,2021,2.42,0.81,0.77,0.78
+Amritsar,2022,2.44,0.82,0.78,0.79
+Amritsar,2023,2.46,0.83,0.79,0.80
+Surat,2015,2.70,0.79,0.74,0.76
+Surat,2016,2.72,0.80,0.75,0.77
+Surat,2017,2.74,0.81,0.76,0.78
+Surat,2018,2.76,0.82,0.77,0.79
+Surat,2019,2.78,0.83,0.78,0.80
+Surat,2020,2.80,0.84,0.79,0.81
+Surat,2021,2.82,0.85,0.80,0.82
+Surat,2022,2.84,0.86,0.81,0.83
+Surat,2023,2.86,0.87,0.82,0.84
+Vadodara,2015,2.50,0.76,0.72,0.73
+Vadodara,2016,2.52,0.77,0.73,0.74
+Vadodara,2017,2.54,0.78,0.74,0.75
+Vadodara,2018,2.56,0.79,0.75,0.76
+Vadodara,2019,2.58,0.80,0.76,0.77
+Vadodara,2020,2.60,0.81,0.77,0.78
+Vadodara,2021,2.62,0.82,0.78,0.79
+Vadodara,2022,2.64,0.83,0.79,0.80
+Vadodara,2023,2.66,0.84,0.80,0.81
+Nashik,2015,2.40,0.74,0.70,0.72
+Nashik,2016,2.42,0.75,0.71,0.73
+Nashik,2017,2.44,0.76,0.72,0.74
+Nashik,2018,2.46,0.77,0.73,0.75
+Nashik,2019,2.48,0.78,0.74,0.76
+Nashik,2020,2.50,0.79,0.75,0.77
+Nashik,2021,2.52,0.80,0.76,0.78
+Nashik,2022,2.54,0.81,0.77,0.79
+Nashik,2023,2.56,0.82,0.78,0.80
+Faridabad,2015,2.30,0.73,0.69,0.70
+Faridabad,2016,2.32,0.74,0.70,0.71
+Faridabad,2017,2.34,0.75,0.71,0.72
+Faridabad,2018,2.36,0.76,0.72,0.73
+Faridabad,2019,2.38,0.77,0.73,0.74
+Faridabad,2020,2.40,0.78,0.74,0.75
+Faridabad,2021,2.42,0.79,0.75,0.76
+Faridabad,2022,2.44,0.80,0.76,0.77
+Faridabad,2023,2.46,0.81,0.77,0.78
+Meerut,2015,2.20,0.72,0.67,0.69
+Meerut,2016,2.22,0.73,0.68,0.70
+Meerut,2017,2.24,0.74,0.69,0.71
+Meerut,2018,2.26,0.75,0.70,0.72
+Meerut,2019,2.28,0.76,0.71,0.73
+Meerut,2020,2.30,0.77,0.72,0.74
+Meerut,2021,2.32,0.78,0.73,0.75
+Meerut,2022,2.34,0.79,0.74,0.76
+Meerut,2023,2.36,0.80,0.75,0.77
+Rajkot,2015,2.50,0.77,0.72,0.73
+Rajkot,2016,2.52,0.78,0.73,0.74
+Rajkot,2017,2.54,0.79,0.74,0.75
+Rajkot,2018,2.56,0.80,0.75,0.76
+Rajkot,2019,2.58,0.81,0.76,0.77
+Rajkot,2020,2.60,0.82,0.77,0.78
+Rajkot,2021,2.62,0.83,0.78,0.79
+Rajkot,2022,2.64,0.84,0.79,0.80
+Rajkot,2023,2.66,0.85,0.80,0.81
+Varanasi,2015,2.10,0.70,0.66,0.68
+Varanasi,2016,2.12,0.71,0.67,0.69
+Varanasi,2017,2.14,0.72,0.68,0.70
+Varanasi,2018,2.16,0.73,0.69,0.71
+Varanasi,2019,2.18,0.74,0.70,0.72
+Varanasi,2020,2.20,0.75,0.71,0.73
+Varanasi,2021,2.22,0.76,0.72,0.74
+Varanasi,2022,2.24,0.77,0.73,0.75
+Varanasi,2023,2.26,0.78,0.74,0.76
+Madurai,2015,2.30,0.73,0.68,0.70
+Madurai,2016,2.32,0.74,0.69,0.71
+Madurai,2017,2.34,0.75,0.70,0.72
+Madurai,2018,2.36,0.76,0.71,0.73
+Madurai,2019,2.38,0.77,0.72,0.74
+Madurai,2020,2.40,0.78,0.73,0.75
+Madurai,2021,2.42,0.79,0.74,0.76
+Madurai,2022,2.44,0.80,0.75,0.77
+Madurai,2023,2.46,0.81,0.76,0.78
+Coimbatore,2015,2.40,0.75,0.70,0.72
+Coimbatore,2016,2.42,0.76,0.71,0.73
+Coimbatore,2017,2.44,0.77,0.72,0.74
+Coimbatore,2018,2.46,0.78,0.73,0.75
+Coimbatore,2019,2.48,0.79,0.74,0.76
+Coimbatore,2020,2.50,0.80,0.75,0.77
+Coimbatore,2021,2.52,0.81,0.76,0.78
+Coimbatore,2022,2.54,0.82,0.77,0.79
+Coimbatore,2023,2.56,0.83,0.78,0.80
+Jodhpur,2015,2.20,0.72,0.67,0.69
+Jodhpur,2016,2.22,0.73,0.68,0.70
+Jodhpur,2017,2.24,0.74,0.69,0.71
+Jodhpur,2018,2.26,0.75,0.70,0.72
+Jodhpur,2019,2.28,0.76,0.71,0.73
+Jodhpur,2020,2.30,0.77,0.72,0.74
+Jodhpur,2021,2.32,0.78,0.73,0.75
+Jodhpur,2022,2.34,0.79,0.74,0.76
+Jodhpur,2023,2.36,0.80,0.75,0.77
+Gwalior,2015,2.10,0.71,0.66,0.68
+Gwalior,2016,2.12,0.72,0.67,0.69
+Gwalior,2017,2.14,0.73,0.68,0.70
+Gwalior,2018,2.16,0.74,0.69,0.71
+Gwalior,2019,2.18,0.75,0.70,0.72
+Gwalior,2020,2.20,0.76,0.71,0.73
+Gwalior,2021,2.22,0.77,0.72,0.74
+Gwalior,2022,2.24,0.78,0.73,0.75
+Gwalior,2023,2.26,0.79,0.74,0.76
+Agra,2015,2.20,0.70,0.66,0.68
+Agra,2016,2.22,0.71,0.67,0.69
+Agra,2017,2.24,0.72,0.68,0.70
+Agra,2018,2.26,0.73,0.69,0.71
+Agra,2019,2.28,0.74,0.70,0.72
+Agra,2020,2.30,0.75,0.71,0.73
+Agra,2021,2.32,0.76,0.72,0.74
+Agra,2022,2.34,0.77,0.73,0.75
+Agra,2023,2.36,0.78,0.74,0.76
+Hubli,2015,2.30,0.74,0.69,0.70
+Hubli,2016,2.32,0.75,0.70,0.71
+Hubli,2017,2.34,0.76,0.71,0.72
+Hubli,2018,2.36,0.77,0.72,0.73
+Hubli,2019,2.38,0.78,0.73,0.74
+Hubli,2020,2.40,0.79,0.74,0.75
+Hubli,2021,2.42,0.80,0.75,0.76
+Hubli,2022,2.44,0.81,0.76,0.77
+Hubli,2023,2.46,0.82,0.77,0.78
+Jamshedpur,2015,2.20,0.73,0.68,0.70
+Jamshedpur,2016,2.22,0.74,0.69,0.71
+Jamshedpur,2017,2.24,0.75,0.70,0.72
+Jamshedpur,2018,2.26,0.76,0.71,0.73
+Jamshedpur,2019,2.28,0.77,0.72,0.74
+Jamshedpur,2020,2.30,0.78,0.73,0.75
+Jamshedpur,2021,2.32,0.79,0.74,0.76
+Jamshedpur,2022,2.34,0.80,0.75,0.77
+Jamshedpur,2023,2.36,0.81,0.76,0.78
+Kolkata,2015,2.20,0.74,0.70,0.72
+Kolkata,2016,2.22,0.75,0.71,0.73
+Kolkata,2017,2.24,0.76,0.72,0.74
+Kolkata,2018,2.26,0.77,0.73,0.75
+Kolkata,2019,2.28,0.78,0.74,0.76
+Kolkata,2020,2.30,0.79,0.75,0.77
+Kolkata,2021,2.32,0.80,0.76,0.78
+Kolkata,2022,2.34,0.81,0.77,0.79
+Kolkata,2023,2.36,0.82,0.78,0.80
+Vijayawada,2015,2.40,0.77,0.72,0.74
+Vijayawada,2016,2.42,0.78,0.73,0.75
+Vijayawada,2017,2.44,0.79,0.74,0.76
+Vijayawada,2018,2.46,0.80,0.75,0.77
+Vijayawada,2019,2.48,0.81,0.76,0.78
+Vijayawada,2020,2.50,0.82,0.77,0.79
+Vijayawada,2021,2.52,0.83,0.78,0.80
+Vijayawada,2022,2.54,0.84,0.79,0.81
+Vijayawada,2023,2.56,0.85,0.80,0.82
+Visakhapatnam,2015,2.50,0.78,0.73,0.75
+Visakhapatnam,2016,2.52,0.79,0.74,0.76
+Visakhapatnam,2017,2.54,0.80,0.75,0.77
+Visakhapatnam,2018,2.56,0.81,0.76,0.78
+Visakhapatnam,2019,2.58,0.82,0.77,0.79
+Visakhapatnam,2020,2.60,0.83,0.78,0.80
+Visakhapatnam,2021,2.62,0.84,0.79,0.81
+Visakhapatnam,2022,2.64,0.85,0.80,0.82
+Visakhapatnam,2023,2.66,0.86,0.81,0.83
+Ludhiana,2015,2.30,0.75,0.70,0.71
+Ludhiana,2016,2.32,0.76,0.71,0.72
+Ludhiana,2017,2.34,0.77,0.72,0.73
+Ludhiana,2018,2.36,0.78,0.73,0.74
+Ludhiana,2019,2.38,0.79,0.74,0.75
+Ludhiana,2020,2.40,0.80,0.75,0.76
+Ludhiana,2021,2.42,0.81,0.76,0.77
+Ludhiana,2022,2.44,0.82,0.77,0.78
+Ludhiana,2023,2.46,0.83,0.78,0.79
+Thiruvananthapuram,2015,2.50,0.85,0.81,0.83
+Thiruvananthapuram,2016,2.52,0.86,0.82,0.84
+Thiruvananthapuram,2017,2.54,0.87,0.83,0.85
+Thiruvananthapuram,2018,2.56,0.88,0.84,0.86
+Thiruvananthapuram,2019,2.58,0.89,0.85,0.87
+Thiruvananthapuram,2020,2.60,0.90,0.86,0.88
+Thiruvananthapuram,2021,2.62,0.91,0.87,0.89
+Thiruvananthapuram,2022,2.64,0.92,0.88,0.90
+Thiruvananthapuram,2023,2.66,0.93,0.89,0.91

requirements.txt

@@ -0,0 +1,8 @@
+dash==3.0.4
+pandas==2.2.2
+plotly==6.1.2
+scikit-learn==1.4.2
+numpy==1.26.4
+statsmodels==0.14.2
+gunicorn==21.2.0
+gradio==4.44.0