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elephant_historic_data.csv

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+State,Elephants in 1993,Elephants in 1997,Elephants in 2002,Elephants in 2007,Elephants in 2012,Elephants in 2017
+Arunachal Pradesh,2102,1800,1607,1690,890,1614
+Assam,5524,5312,5246,5281,5620,5719
+Meghalaya,2872,1840,1868,1811,1811,1754
+Nagaland,178,158,145,152,212,446
+Mizoram,15,22,33,12,12,7
+Manipur,50,30,12,12,12,9
+Tripura,100,70,40,59,59,102
+West Bengal ,200,276,338,350,647,682
+Jharkhand,550,618,772,624,688,679
+Odisha,1750,1800,1841,1862,1930,1976
+Chhattisgarh,82,89,102,122,247,247
+Bihar ,0,0,0,10,10,25
+Madhya Pradesh,0,0,0,7,7,7
+Uttarakhand ,828,1130,1582,1346,1346,1839
+Uttar Pradesh,47,70,85,380,291,232
+Haryana,0,0,0,0,7,7
+Himachal Pradesh,0,0,0,0,5,7
+Tamil Nadu,2307,2971,3052,3867,4015,2761
+Karnataka,5500,6088,5838,4035,5678,6049
+Kerala,3500,3600,3850,6068,6133,5706
+Andhra Pradesh,46,57,74,28,41,65
+Maharashtra,2,4,3,7,4,6
+Andaman & Nicobar Islands ,0,0,2,9,13,25

elephants.py

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+import pandas as pd
+import folium
+import plotly.express as px
+from prophet import Prophet
+import streamlit as st
+
+def elephants_page():
+    st.markdown(
+        '<h1 style="text-align: center; color: lightgreen; font-weight: bold;">Elephant Monitoring in India</h1>',
+        unsafe_allow_html=True)
+
+    df = pd.read_csv('elephant_historic_data.csv')
+
+    # Update years list
+    years = ['1993', '1997', '2002', '2007', '2012', '2017']
+
+    # Add a select box for year selection
+    selected_year = st.selectbox('Select Year', years)
+
+    # Create a map centered around India
+    india_map = folium.Map(location=[17.5937, 82.9629], zoom_start=5, width=1300, height=800)
+
+    # Add bubble markers for each state based on the selected year
+    for i, row in df.iterrows():
+        state = row['State']
+        location = {
+            'Andhra Pradesh': [15.9129, 79.7400],
+            'Arunachal Pradesh': [27.1137, 93.6054],
+            'Assam': [26.2006, 92.9376],
+            'Bihar': [25.0961, 85.3131],
+            'Chhattisgarh': [21.2787, 81.8661],
+            'Goa': [15.2993, 74.1240],
+            'Gujarat': [22.2587, 71.1924],
+            'Jharkhand': [23.6102, 85.2799],
+            'Karnataka': [15.3173, 75.7139],
+            'Kerala': [10.8505, 76.2711],
+            'Madhya Pradesh': [23.4734, 77.9479],
+            'Maharashtra': [19.6633, 75.3202],
+            'Meghalaya': [25.4670, 91.3662],
+            'Mizoram': [23.1645, 92.9376],
+            'Nagaland': [26.1584, 94.5624],
+            'Odisha': [20.9517, 85.0985],
+            'Rajasthan': [27.0238, 74.2176],
+            'Tamil Nadu': [11.1271, 78.6569],
+            'Telangana': [17.0220, 78.3555],
+            'Tripura': [23.9408, 91.9882],
+            'Uttar Pradesh': [27.2599, 79.4126],
+            'Uttarakhand ': [30.0668, 79.0193],
+            'West Bengal ': [22.9868, 87.8550],
+            'Sikkim': [27.5330, 88.6139],
+            'Jammu & Kashmir': [33.2778, 76.5765],
+            'Punjab': [31.1471, 75.3412],
+            'Haryana': [29.0588, 76.0856],
+            'Himachal Pradesh': [31.1048, 77.1734],
+            'Manipur': [24.6637, 93.9063],
+            'Andaman & Nicobar Islands ': [11.7401, 92.6586]
+        }.get(state, [20.5937, 78.9629])
+
+        elephant_count = row[f'Elephants in {selected_year}']
+
+        if elephant_count <= 0:
+            continue
+
+        if elephant_count > 200:
+            color = 'pink'
+        elif elephant_count > 50:
+            color = 'orange'
+        else:
+            color = 'yellow'
+
+        bubble_size = 20
+
+        folium.CircleMarker(
+            location=location,
+            radius=bubble_size,
+            color=color,
+            fill=True,
+            fill_color=color,
+            fill_opacity=0.8
+        ).add_to(india_map)
+
+        folium.map.Marker(
+            location,
+            icon=folium.DivIcon(html=f'''
+                <div style="text-align: left; font-size: 8.5pt; font-weight: bold; width:{bubble_size * 0.2}px; color: black;">
+                    {elephant_count:.0f}
+                </div>''')
+        ).add_to(india_map)
+
+    title_html = f'''
+        <h3 align="center" style="font-size:20px"><b>Elephant Count in India ({selected_year})</b></h3>
+    '''
+    india_map.get_root().html.add_child(folium.Element(title_html))
+
+    legend_html = '''
+    <div style="position: fixed; 
+         bottom: 50px; left: 50px; width: 150px; height: 110px; 
+         border:2px solid grey; z-index:9999; font-size:12px;
+         background-color:white; padding: 10px;">
+         <b>Elephant Frequency</b><br>
+         <i class="fa fa-circle" style="color:pink"></i> > 200 Elephants<br>
+         <i class="fa fa-circle" style="color:orange"></i> 50-200 Elephants<br>
+         <i class="fa fa-circle" style="color:yellow"></i> < 50 Elephants<br>
+    </div>
+    '''
+    india_map.get_root().html.add_child(folium.Element(legend_html))
+
+    st.components.v1.html(india_map._repr_html_(), height=700)
+
+    selected_state = st.selectbox('Select a State', df['State'].unique())
+
+    state_data = df[df['State'] == selected_state]
+
+    # Reshape the DataFrame
+    state_data = state_data.melt(id_vars=['State'], var_name='Year', value_name='Elephant Count')
+    state_data['Year'] = state_data['Year'].str.extract(r'(\d{4})').astype(int)
+
+    line_fig = px.line(state_data, x='Year', y='Elephant Count', title=f'Elephant Population Over Time in {selected_state}')
+    bar_fig = px.bar(state_data, x='Year', y='Elephant Count', title=f'Elephant Population Count in {selected_state}')
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.plotly_chart(line_fig)
+
+    with col2:
+        st.plotly_chart(bar_fig)
+
+    st.subheader('Predict Future Elephant Counts')
+    future_year = st.number_input('Enter Future Year (2025 and onwards)', min_value=2025, step=1)
+
+    # Add a button to start the prediction
+    if st.button('Start Prediction'):
+        if future_year >= 2025:
+            future_data = []
+            for state in df['State'].unique():
+                state_df = df[df['State'] == state]
+                state_df = state_df.melt(id_vars=['State'], var_name='Year', value_name='Elephant Count')
+                state_df['Year'] = state_df['Year'].str.extract(r'(\d{4})').astype(int)
+                state_df = state_df.rename(columns={'Year': 'ds', 'Elephant Count': 'y'})
+                state_df['ds'] = pd.to_datetime(state_df['ds'], format='%Y')  # Ensure correct date format
+
+                # Use Prophet for forecasting
+                model = Prophet()
+                state_df = state_df[['ds', 'y']]
+                model.fit(state_df)
+
+                future = pd.DataFrame({'ds': pd.date_range(start='2023-01-01', periods=future_year - 2022, freq='Y')})
+                forecast = model.predict(future)
+
+                # Ensure forecast is not empty
+                if forecast['yhat'].size > 0:
+                    future_elephant_count = abs(
+                        forecast['yhat'].iloc[-1])  # Use absolute value to handle negative predictions
+                else:
+                    future_elephant_count = 0  # Default value if forecast is empty
+
+                # Only append data if the elephant count is greater than zero
+                if future_elephant_count > 0:
+                    future_data.append(
+                        {'State': state, 'Year': future_year, 'Predicted Elephant Count': future_elephant_count})
+
+            future_df = pd.DataFrame(future_data)
+
+            # Map for predicted elephant counts
+            pred_map = folium.Map(location=[17.5937, 82.9629], zoom_start=5, width=1300, height=800)
+
+            for i, row in future_df.iterrows():
+                state = row['State']
+                location = {
+                    'Andhra Pradesh': [15.9129, 79.7400],
+                    'Arunachal Pradesh': [27.1137, 93.6054],
+                    'Assam': [26.2006, 92.9376],
+                    'Bihar': [25.0961, 85.3131],
+                    'Chhattisgarh': [21.2787, 81.8661],
+                    'Goa': [15.2993, 74.1240],
+                    'Gujarat': [22.2587, 71.1924],
+                    'Jharkhand': [23.6102, 85.2799],
+                    'Karnataka': [15.3173, 75.7139],
+                    'Kerala': [10.8505, 76.2711],
+                    'Madhya Pradesh': [23.4734, 77.9479],
+                    'Maharashtra': [19.6633, 75.3202],
+                    'Meghalaya': [25.4670, 91.3662],
+                    'Mizoram': [23.1645, 92.9376],
+                    'Nagaland': [26.1584, 94.5624],
+                    'Odisha': [20.9517, 85.0985],
+                    'Rajasthan': [27.0238, 74.2176],
+                    'Tamil Nadu': [11.1271, 78.6569],
+                    'Telangana': [17.0220, 78.3555],
+                    'Tripura': [23.9408, 91.9882],
+                    'Uttar Pradesh': [27.2599, 79.4126],
+                    'Uttarakhand ': [30.0668, 79.0193],
+                    'West Bengal ': [22.9868, 87.8550],
+                    'Sikkim': [27.5330, 88.6139],
+                    'Jammu & Kashmir': [33.2778, 76.5765],
+                    'Punjab': [31.1471, 75.3412],
+                    'Haryana': [29.0588, 76.0856],
+                    'Himachal Pradesh': [31.1048, 77.1734],
+                    'Manipur': [24.6637, 93.9063],
+                    'Andaman & Nicobar Islands ': [11.7401, 92.6586]
+                }.get(state, [20.5937, 78.9629])
+
+                predicted_count = row['Predicted Elephant Count']
+
+                if predicted_count > 200:
+                    color = 'pink'
+                elif predicted_count > 50:
+                    color = 'orange'
+                else:
+                    color = 'yellow'
+
+                bubble_size = 20
+
+                folium.CircleMarker(
+                    location=location,
+                    radius=bubble_size,
+                    color=color,
+                    fill=True,
+                    fill_color=color,
+                    fill_opacity=0.8
+                ).add_to(pred_map)
+
+                folium.map.Marker(
+                    location,
+                    icon=folium.DivIcon(html=f'''
+                        <div style="text-align: left; font-size: 8.5pt; font-weight: bold; width:{bubble_size * 0.2}px; color: black;">
+                            {predicted_count:.0f}
+                        </div>''')
+                ).add_to(pred_map)
+
+            pred_title_html = f'''
+                <h3 align="center" style="font-size:20px"><b>Predicted Elephant Count in {future_year}</b></h3>
+            '''
+            pred_map.get_root().html.add_child(folium.Element(pred_title_html))
+
+            pred_legend_html = '''
+            <div style="position: fixed; 
+                 bottom: 50px; left: 50px; width: 150px; height: 110px; 
+                 border:2px solid grey; z-index:9999; font-size:12px;
+                 background-color:white; padding: 10px;">
+                 <b>Predicted Elephant Frequency</b><br>
+                 <i class="fa fa-circle" style="color:pink"></i> > 200 Elephants<br>
+                 <i class="fa fa-circle" style="color:orange"></i> 50-200 Elephants<br>
+                 <i class="fa fa-circle" style="color:yellow"></i> < 50 Elephants<br>
+            </div>
+            '''
+            pred_map.get_root().html.add_child(folium.Element(pred_legend_html))
+
+            st.components.v1.html(pred_map._repr_html_(), height=700)
+
+# Add your Streamlit app configuration and page registration
+if __name__ == '__main__':
+    st.set_page_config(page_title='Elephant Monitoring', layout='wide')
+    elephants_page()

leopard.py

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+import pandas as pd
+import folium
+import plotly.express as px
+from prophet import Prophet
+import streamlit as st
+
+def leopard_page():
+    st.markdown(
+        '<h1 style="text-align: center; color: orange; font-weight: bold;">Leopard Monitoring in India</h1>',
+        unsafe_allow_html=True)
+
+    df = pd.read_csv('leopard_historic_data.csv')
+
+    # Add a select box for year selection
+    selected_year = st.selectbox('Select Year', ['2006', '2010', '2014', '2018', '2024'])
+
+    # Create a map centered around India
+    india_map = folium.Map(location=[17.5937, 82.9629], zoom_start=5, width=1300, height=800)
+
+    # Add bubble markers for each state based on the selected year
+    for i, row in df.iterrows():
+        state = row['State']
+        location = {
+            'Andhra Pradesh': [15.9129, 79.7400],
+            'Arunachal Pradesh': [27.1137, 93.6054],
+            'Assam': [26.2006, 92.9376],
+            'Bihar': [25.0961, 85.3131],
+            'Chhattisgarh': [21.2787, 81.8661],
+            'Goa': [15.2993, 74.1240],
+            'Gujarat': [22.2587, 71.1924],
+            'Jharkhand': [23.6102, 85.2799],
+            'Karnataka': [15.3173, 75.7139],
+            'Kerala': [10.8505, 76.2711],
+            'Madhya Pradesh': [23.4734, 77.9479],
+            'Maharashtra': [19.6633, 75.3202],
+            'Meghalaya': [25.4670, 91.3662],
+            'Mizoram': [23.1645, 92.9376],
+            'Nagaland': [26.1584, 94.5624],
+            'Odisha': [20.9517, 85.0985],
+            'Rajasthan': [27.0238, 74.2176],
+            'Tamil Nadu': [11.1271, 78.6569],
+            'Telangana': [17.0220, 78.3555],
+            'Tripura': [23.9408, 91.9882],
+            'Uttar Pradesh': [27.2599, 79.4126],
+            'Uttarakhand': [30.0668, 79.0193],
+            'West Bengal': [22.9868, 87.8550],
+            'Sikkim': [27.5330, 88.6139],
+            'Jammu & Kashmir': [33.2778, 76.5765],
+            'Punjab': [31.1471, 75.3412],
+            'Haryana': [29.0588, 76.0856],
+            'Himachal Pradesh': [31.1048, 77.1734],
+            'Manipur': [24.6637, 93.9063]
+        }.get(state, [20.5937, 78.9629])
+
+        leopard_count = row[f'Leopards in {selected_year}']
+
+        if leopard_count <= 0:
+            continue
+
+        if leopard_count > 200:
+            color = 'green'
+        elif leopard_count > 50:
+            color = 'purple'
+        else:
+            color = 'orange'
+
+        bubble_size = 20
+
+        folium.CircleMarker(
+            location=location,
+            radius=bubble_size,
+            color=color,
+            fill=True,
+            fill_color=color,
+            fill_opacity=0.8
+        ).add_to(india_map)
+
+        folium.map.Marker(
+            location,
+            icon=folium.DivIcon(html=f'''
+                <div style="text-align: left; font-size: 8.5pt; font-weight: bold; width:{bubble_size * 0.4}px; color: black;">
+                    {leopard_count:.0f}
+                </div>''')
+        ).add_to(india_map)
+
+    title_html = f'''
+        <h3 align="center" style="font-size:20px"><b>Leopard Count in India ({selected_year})</b></h3>
+    '''
+    india_map.get_root().html.add_child(folium.Element(title_html))
+
+    legend_html = '''
+    <div style="position: fixed; 
+         bottom: 50px; left: 50px; width: 150px; height: 120px; 
+         border:2px solid grey; z-index:9999; font-size:14px;
+         background-color:white; padding: 10px;">
+         <b>Leopard Frequency</b><br>
+         <i class="fa fa-circle" style="color:green"></i> > 200 Leopards<br>
+         <i class="fa fa-circle" style="color:purple"></i> 50-200 Leopards<br>
+         <i class="fa fa-circle" style="color:orange"></i> < 50 Leopards<br>
+    </div>
+    '''
+    india_map.get_root().html.add_child(folium.Element(legend_html))
+
+    st.components.v1.html(india_map._repr_html_(), height=700)
+
+    selected_state = st.selectbox('Select a State', df['State'].unique())
+
+    state_data = df[df['State'] == selected_state]
+
+    # Reshape the DataFrame
+    state_data = state_data.melt(id_vars=['State'], var_name='Year', value_name='Leopard Count')
+    state_data['Year'] = state_data['Year'].str.extract(r'(\d{4})').astype(int)
+
+    line_fig = px.line(state_data, x='Year', y='Leopard Count',
+                       title=f'Leopard Population Over Time in {selected_state}')
+    bar_fig = px.bar(state_data, x='Year', y='Leopard Count', title=f'Leopard Population Count in {selected_state}')
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.plotly_chart(line_fig)
+
+    with col2:
+        st.plotly_chart(bar_fig)
+
+    st.subheader('Predict Future Leopard Counts')
+    future_year = st.number_input('Enter Future Year (2025 and onwards)', min_value=2025, step=1)
+
+    # Add a button to start the prediction
+    if st.button('Start Prediction'):
+        if future_year >= 2025:
+            future_data = []
+            for state in df['State'].unique():
+                state_df = df[df['State'] == state]
+                state_df = state_df.melt(id_vars=['State'], var_name='Year', value_name='Leopard Count')
+                state_df['Year'] = state_df['Year'].str.extract(r'(\d{4})').astype(int)
+                state_df = state_df.rename(columns={'Year': 'ds', 'Leopard Count': 'y'})
+                state_df['ds'] = pd.to_datetime(state_df['ds'], format='%Y')  # Ensure correct date format
+
+                # Use Prophet for forecasting
+                model = Prophet()
+                state_df = state_df[['ds', 'y']]
+                model.fit(state_df)
+
+                future = pd.DataFrame({'ds': pd.date_range(start='2023-01-01', periods=future_year - 2022, freq='Y')})
+                forecast = model.predict(future)
+
+                # Ensure forecast is not empty
+                if forecast['yhat'].size > 0:
+                    future_leopard_count = abs(
+                        forecast['yhat'].iloc[-1])  # Use absolute value to handle negative predictions
+                else:
+                    future_leopard_count = 0  # Default value if forecast is empty
+
+                # Only append data if the leopard count is greater than zero
+                if future_leopard_count > 0:
+                    future_data.append(
+                        {'State': state, 'Year': future_year, 'Predicted Leopard Count': future_leopard_count})
+
+            future_df = pd.DataFrame(future_data)
+
+            # Create a map for the future predicted leopard counts
+            future_map = folium.Map(location=[17.5937, 82.9629], zoom_start=5, width=1300, height=800)
+
+            for i, row in future_df.iterrows():
+                state = row['State']
+                location = {
+                    'Andhra Pradesh': [15.9129, 79.7400],
+                    'Arunachal Pradesh': [27.1137, 93.6054],
+                    'Assam': [26.2006, 92.9376],
+                    'Bihar': [25.0961, 85.3131],
+                    'Chhattisgarh': [21.2787, 81.8661],
+                    'Goa': [15.2993, 74.1240],
+                    'Gujarat': [22.2587, 71.1924],
+                    'Jharkhand': [23.6102, 85.2799],
+                    'Karnataka': [15.3173, 75.7139],
+                    'Kerala': [10.8505, 76.2711],
+                    'Madhya Pradesh': [23.4734, 77.9479],
+                    'Maharashtra': [19.6633, 75.3202],
+                    'Meghalaya': [25.4670, 91.3662],
+                    'Mizoram': [23.1645, 92.9376],
+                    'Nagaland': [26.1584, 94.5624],
+                    'Odisha': [20.9517, 85.0985],
+                    'Rajasthan': [27.0238, 74.2176],
+                    'Tamil Nadu': [11.1271, 78.6569],
+                    'Telangana': [17.0220, 78.3555],
+                    'Tripura': [23.9408, 91.9882],
+                    'Uttar Pradesh': [27.2599, 79.4126],
+                    'Uttarakhand': [30.0668, 79.0193],
+                    'West Bengal': [22.9868, 87.8550],
+                    'Sikkim': [27.5330, 88.6139],
+                    'Jammu & Kashmir': [33.2778, 76.5765],
+                    'Punjab': [31.1471, 75.3412],
+                    'Haryana': [29.0588, 76.0856],
+                    'Himachal Pradesh': [31.1048, 77.1734],
+                    'Manipur': [24.6637, 93.9063]
+                }.get(state, [20.5937, 78.9629])
+
+                leopard_count = row['Predicted Leopard Count']
+
+                if leopard_count <= 0:
+                    continue
+
+                if leopard_count > 200:
+                    color = 'green'
+                elif leopard_count > 50:
+                    color = 'purple'
+                else:
+                    color = 'orange'
+
+                bubble_size = 20
+
+                folium.CircleMarker(
+                    location=location,
+                    radius=bubble_size,
+                    color=color,
+                    fill=True,
+                    fill_color=color,
+                    fill_opacity=0.8
+                ).add_to(future_map)
+
+                folium.map.Marker(
+                    location,
+                    icon=folium.DivIcon(html=f'''
+                        <div style="text-align: left; font-size: 8.5pt; font-weight: bold; width:{bubble_size * 0.4}px; color: black;">
+                            {leopard_count:.0f}
+                        </div>''')
+                ).add_to(future_map)
+
+            title_html = f'''
+                <h3 align="center" style="font-size:20px"><b>Predicted Leopard Count in India ({future_year})</b></h3>
+            '''
+            future_map.get_root().html.add_child(folium.Element(title_html))
+
+            legend_html = '''
+            <div style="position: fixed; 
+                 bottom: 50px; left: 50px; width: 150px; height: 120px; 
+                 border:2px solid grey; z-index:9999; font-size:14px;
+                 background-color:white; padding: 10px;">
+                 <b>Leopard Frequency</b><br>
+                 <i class="fa fa-circle" style="color:blue"></i> > 200 Leopards<br>
+                 <i class="fa fa-circle" style="color:purple"></i> 50-200 Leopards<br>
+                 <i class="fa fa-circle" style="color:orange"></i> < 50 Leopards<br>
+            </div>
+            '''
+            future_map.get_root().html.add_child(folium.Element(legend_html))
+
+            st.components.v1.html(future_map._repr_html_(), height=900)
+
+        else:
+            st.error("Please enter a year greater than or equal to 2025.")

leopard_historic_data.csv

@@ -0,0 +1,30 @@
+State,Leopards in 2006,Leopards in 2010,Leopards in 2014,Leopards in 2018,Leopards in 2024
+Andhra Pradesh,89,120,134,320,343
+Arunachal Pradesh,1,2,5,20,27
+Assam,100,150,180,200,324
+Bihar,5,10,25,40,25
+Chhattisgarh,250,260,270,480,447
+Goa,5,7,15,30,0
+Gujarat,500,600,1100,1300,1355
+Jharkhand,5,10,20,30,29
+Karnataka,789,686,1245,1456,1131
+Kerala,200,500,600,650,472
+Madhya Pradesh,300,500,1000,2000,1817
+Maharashtra,400,600,1000,1400,908
+Meghalaya,1,2,5,10,0
+Mizoram,2,3,7,8,0
+Nagaland,1,2,4,6,0
+Odisha,600,700,750,800,345
+Rajasthan,60,70,80,100,194
+Tamil Nadu,800,900,950,1000,815
+Telangana,2,5,10,20,268
+Tripura,2,3,4,5,0
+Uttar Pradesh,220,250,270,300,194
+Uttarakhand,800,1000,1200,1500,703
+West Bengal,30,50,80,100,233
+Sikkim,10,20,30,40,0
+Jammu & Kashmir,2,3,5,7,0
+Punjab,2,5,8,10,0
+Haryana,3,5,6,8,0
+Himachal Pradesh,2,3,5,7,0
+Manipur,2,4,5,7,0

tigers.py

@@ -0,0 +1,249 @@
+import pandas as pd
+import folium
+import plotly.express as px
+from prophet import Prophet
+import streamlit as st
+
+def tigers_page():
+    st.markdown(
+        '<h1 style="text-align: center; color: skyblue; font-weight: bold;">Tiger Monitoring in India</h1>',
+        unsafe_allow_html=True)
+
+    df = pd.read_excel('tiger_historic_data.xlsx')
+
+    # Add a select box for year selection
+    selected_year = st.selectbox('Select Year', ['2006', '2010', '2014', '2018', '2022'])
+
+    # Create a map centered around India
+    india_map = folium.Map(location=[17.5937, 82.9629], zoom_start=5, width=1300, height=800)
+
+    # Add bubble markers for each state based on the selected year
+    for i, row in df.iterrows():
+        state = row['State']
+        location = {
+            'Andhra Pradesh': [15.9129, 79.7400],
+            'Arunachal Pradesh': [27.1137, 93.6054],
+            'Assam': [26.2006, 92.9376],
+            'Bihar': [25.0961, 85.3131],
+            'Chhattisgarh': [21.2787, 81.8661],
+            'Goa': [15.2993, 74.1240],
+            'Gujarat': [22.2587, 71.1924],
+            'Jharkhand': [23.6102, 85.2799],
+            'Karnataka': [15.3173, 75.7139],
+            'Kerala': [10.8505, 76.2711],
+            'Madhya Pradesh': [23.4734, 77.9479],
+            'Maharashtra': [19.6633, 75.3202],
+            'Meghalaya': [25.4670, 91.3662],
+            'Mizoram': [23.1645, 92.9376],
+            'Nagaland': [26.1584, 94.5624],
+            'Odisha': [20.9517, 85.0985],
+            'Rajasthan': [27.0238, 74.2176],
+            'Tamil Nadu': [11.1271, 78.6569],
+            'Telangana': [17.0220, 78.3555],
+            'Tripura': [23.9408, 91.9882],
+            'Uttar Pradesh': [27.2599, 79.4126],
+            'Uttarakhand': [30.0668, 79.0193],
+            'West Bengal': [22.9868, 87.8550],
+            'Sikkim': [27.5330, 88.6139],
+            'Jammu & Kashmir': [33.2778, 76.5765],
+            'Punjab': [31.1471, 75.3412],
+            'Haryana': [29.0588, 76.0856],
+            'Himachal Pradesh': [31.1048, 77.1734],
+            'Manipur': [24.6637, 93.9063]
+        }.get(state, [20.5937, 78.9629])
+
+        tiger_count = row[f'Tigers  in {selected_year}']
+
+        if tiger_count <= 0:
+            continue
+
+        if tiger_count > 200:
+            color = 'green'
+        elif tiger_count > 50:
+            color = 'yellow'
+        else:
+            color = 'red'
+
+        bubble_size = 20
+
+        folium.CircleMarker(
+            location=location,
+            radius=bubble_size,
+            color=color,
+            fill=True,
+            fill_color=color,
+            fill_opacity=0.8
+        ).add_to(india_map)
+
+        folium.map.Marker(
+            location,
+            icon=folium.DivIcon(html=f'''
+                <div style="text-align: left; font-size: 10pt; font-weight: bold; width:{bubble_size * 2}px; color: black;">
+                    {tiger_count:.0f}
+                </div>''')
+        ).add_to(india_map)
+
+    title_html = f'''
+        <h3 align="center" style="font-size:20px"><b>Tiger Count in India ({selected_year})</b></h3>
+    '''
+    india_map.get_root().html.add_child(folium.Element(title_html))
+
+    legend_html = '''
+    <div style="position: fixed; 
+         bottom: 50px; left: 50px; width: 150px; height: 120px; 
+         border:2px solid grey; z-index:9999; font-size:14px;
+         background-color:white; padding: 10px;">
+         <b>Tiger Frequency</b><br>
+         <i class="fa fa-circle" style="color:green"></i> > 200 Tigers<br>
+         <i class="fa fa-circle" style="color:yellow"></i> 50-200 Tigers<br>
+         <i class="fa fa-circle" style="color:red"></i> < 50 Tigers<br>
+    </div>
+    '''
+    india_map.get_root().html.add_child(folium.Element(legend_html))
+
+    st.components.v1.html(india_map._repr_html_(), height=700)
+
+    selected_state = st.selectbox('Select a State', df['State'].unique())
+
+    state_data = df[df['State'] == selected_state]
+
+    # Reshape the DataFrame
+    state_data = state_data.melt(id_vars=['State'], var_name='Year', value_name='Tiger Count')
+    state_data['Year'] = state_data['Year'].str.extract(r'(\d{4})').astype(int)
+
+    line_fig = px.line(state_data, x='Year', y='Tiger Count', title=f'Tiger Population Over Time in {selected_state}')
+    bar_fig = px.bar(state_data, x='Year', y='Tiger Count', title=f'Tiger Population Count in {selected_state}')
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.plotly_chart(line_fig)
+
+    with col2:
+        st.plotly_chart(bar_fig)
+
+    st.subheader('Predict Future Tiger Counts')
+    future_year = st.number_input('Enter Future Year (2025 and onwards)', min_value=2025, step=1)
+
+    # Add a button to start the prediction
+    if st.button('Start Prediction'):
+        if future_year >= 2025:
+            future_data = []
+            for state in df['State'].unique():
+                state_df = df[df['State'] == state]
+                state_df = state_df.melt(id_vars=['State'], var_name='Year', value_name='Tiger Count')
+                state_df['Year'] = state_df['Year'].str.extract(r'(\d{4})').astype(int)
+                state_df = state_df.rename(columns={'Year': 'ds', 'Tiger Count': 'y'})
+                state_df['ds'] = pd.to_datetime(state_df['ds'], format='%Y')  # Ensure correct date format
+
+                # Use Prophet for forecasting
+                model = Prophet()
+                state_df = state_df[['ds', 'y']]
+                model.fit(state_df)
+
+                future = pd.DataFrame({'ds': pd.date_range(start='2023-01-01', periods=future_year - 2022, freq='Y')})
+                forecast = model.predict(future)
+
+                # Ensure forecast is not empty
+                if forecast['yhat'].size > 0:
+                    future_tiger_count = abs(
+                        forecast['yhat'].iloc[-1])  # Use absolute value to handle negative predictions
+                else:
+                    future_tiger_count = 0  # Default value if forecast is empty
+
+                # Only append data if the tiger count is greater than zero
+                if future_tiger_count > 0:
+                    future_data.append(
+                        {'State': state, 'Year': future_year, 'Predicted Tiger Count': future_tiger_count})
+
+            future_df = pd.DataFrame(future_data)
+
+            # Map for predicted tiger counts
+            pred_map = folium.Map(location=[17.5937, 82.9629], zoom_start=5, width=1300, height=800)
+
+            for i, row in future_df.iterrows():
+                state = row['State']
+                location = {
+                    'Andhra Pradesh': [15.9129, 79.7400],
+                    'Arunachal Pradesh': [27.1137, 93.6054],
+                    'Assam': [26.2006, 92.9376],
+                    'Bihar': [25.0961, 85.3131],
+                    'Chhattisgarh': [21.2787, 81.8661],
+                    'Goa': [15.2993, 74.1240],
+                    'Gujarat': [22.2587, 71.1924],
+                    'Jharkhand': [23.6102, 85.2799],
+                    'Karnataka': [15.3173, 75.7139],
+                    'Kerala': [10.8505, 76.2711],
+                    'Madhya Pradesh': [23.4734, 77.9479],
+                    'Maharashtra': [19.6633, 75.3202],
+                    'Meghalaya': [25.4670, 91.3662],
+                    'Mizoram': [23.1645, 92.9376],
+                    'Nagaland': [26.1584, 94.5624],
+                    'Odisha': [20.9517, 85.0985],
+                    'Rajasthan': [27.0238, 74.2176],
+                    'Tamil Nadu': [11.1271, 78.6569],
+                    'Telangana': [17.0220, 78.3555],
+                    'Tripura': [23.9408, 91.9882],
+                    'Uttar Pradesh': [27.2599, 79.4126],
+                    'Uttarakhand': [30.0668, 79.0193],
+                    'West Bengal': [22.9868, 87.8550],
+                    'Sikkim': [27.5330, 88.6139],
+                    'Jammu & Kashmir': [33.2778, 76.5765],
+                    'Punjab': [31.1471, 75.3412],
+                    'Haryana': [29.0588, 76.0856],
+                    'Himachal Pradesh': [31.1048, 77.1734],
+                    'Manipur': [24.6637, 93.9063]
+                }.get(state, [20.5937, 78.9629])
+
+                predicted_count = row['Predicted Tiger Count']
+
+                if predicted_count <= 0:
+                    continue
+
+                if predicted_count > 200:
+                    color = 'green'
+                elif predicted_count > 50:
+                    color = 'yellow'
+                else:
+                    color = 'red'
+
+                bubble_size = 20
+
+                folium.CircleMarker(
+                    location=location,
+                    radius=bubble_size,
+                    color=color,
+                    fill=True,
+                    fill_color=color,
+                    fill_opacity=0.8
+                ).add_to(pred_map)
+
+                folium.map.Marker(
+                    location,
+                    icon=folium.DivIcon(html=f'''
+                        <div style="text-align: left; font-size: 9pt; font-weight: bold; width:{bubble_size * 0.4}px; color: black;">
+                            {predicted_count:.0f}
+                        </div>''')
+                ).add_to(pred_map)
+
+            title_html = f'''
+                <h3 align="center" style="font-size:20px"><b>Predicted Tiger Count in India ({future_year})</b></h3>
+            '''
+            pred_map.get_root().html.add_child(folium.Element(title_html))
+
+            legend_html = '''
+            <div style="position: fixed; 
+                 bottom: 50px; left: 50px; width: 150px; height: 120px; 
+                 border:2px solid grey; z-index:9999; font-size:14px;
+                 background-color:white; padding: 10px;">
+                 <b>Tiger Frequency</b><br>
+                 <i class="fa fa-circle" style="color:green"></i> > 200 Tigers<br>
+                 <i class="fa fa-circle" style="color:yellow"></i> 50-200 Tigers<br>
+                 <i class="fa fa-circle" style="color:red"></i> < 50 Tigers<br>
+            </div>
+            '''
+            pred_map.get_root().html.add_child(folium.Element(legend_html))
+
+            st.components.v1.html(pred_map._repr_html_(), height=800)
+        else:
+            st.error("Please enter a year greater than or equal to 2025.")