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from flask import Flask, render_template, request |
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import folium |
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from folium.plugins import HeatMapWithTime, FeatureGroupSubGroup, HeatMap |
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import pandas as pd |
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app = Flask(__name__) |
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df = pd.read_csv('final_crop_historic_data_pkjk.csv') |
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df.columns = ['State', 'District', 'Crop_Year', 'Season', 'Crop', 'Area', 'Production', 'Latitude', 'Longitude'] |
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@app.route('/') |
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def home(): |
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return render_template('index.html', map_html='', selected_map='Home') |
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@app.route('/production_analysis', methods=['GET', 'POST']) |
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def production_analysis(): |
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crop_options = df['Crop'].unique().tolist() |
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selected_crop = request.form.get('crop_type') if request.method == 'POST' else None |
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if not selected_crop: |
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return render_template('index.html', map_html='', selected_map='Production Analysis', |
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crop_options=crop_options, selected_crop=None) |
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crop_data = df[df['Crop'] == selected_crop] |
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if crop_data.empty: |
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return render_template('index.html', map_html='', selected_map='No Data Available', |
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crop_options=crop_options, selected_crop=selected_crop) |
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time_index = crop_data['Crop_Year'].unique() |
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heatmap_data = [[ |
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[row['Latitude'], row['Longitude']] |
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for _, row in crop_data[crop_data['Crop_Year'] == year].dropna().iterrows() |
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] |
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for year in time_index |
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] |
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for year, data in zip(time_index, heatmap_data): |
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print(f'Year: {year}, Data: {data}') |
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m = folium.Map(location=[20.5937, 78.9629], zoom_start=5) |
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heatmap = HeatMapWithTime( |
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heatmap_data, |
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index=[str(year) for year in time_index], |
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auto_play=True, |
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max_opacity=0.6 |
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) |
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heatmap.add_to(m) |
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map_html = m._repr_html_() |
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return render_template('index.html', map_html=map_html, selected_map='Production Heatmap Analysis', |
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crop_options=crop_options, selected_crop=selected_crop) |
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@app.route('/heatmap_analysis') |
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def heatmap_analysis(): |
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global df |
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m = folium.Map(location=[20.5937, 78.9629], zoom_start=5) |
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fg = folium.FeatureGroup(name='Crops') |
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m.add_child(fg) |
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df_sampled = df.sample(frac=0.005, random_state=42) |
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for crop in df_sampled['Crop'].unique(): |
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subgroup = FeatureGroupSubGroup(fg, crop) |
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m.add_child(subgroup) |
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crop_data = df_sampled[df_sampled['Crop'] == crop] |
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heatmap_data = [[row['Latitude'], row['Longitude']] for _, row in crop_data.iterrows()] |
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HeatMap(heatmap_data).add_to(subgroup) |
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folium.LayerControl(collapsed=False).add_to(m) |
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map_html = m._repr_html_() |
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return render_template('index.html', map_html=map_html, selected_map='Crop Heatmap Analysis') |
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@app.route('/season_analysis') |
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def season_analysis(): |
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global df |
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m = folium.Map(location=[20.5937, 78.9629], zoom_start=5) |
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df_sampled = df.sample(frac=0.005, random_state=42) |
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top_crops = {} |
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for _, row in df_sampled.iterrows(): |
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lat_lon = (row['Latitude'], row['Longitude']) |
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crop = row['Crop'] |
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production = row['Production'] |
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if lat_lon not in top_crops: |
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top_crops[lat_lon] = {'Season': row['Season'], 'Crops': {}, 'Area': row['Area']} |
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if crop not in top_crops[lat_lon]['Crops']: |
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top_crops[lat_lon]['Crops'][crop] = 0 |
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top_crops[lat_lon]['Crops'][crop] += production |
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for location, data in top_crops.items(): |
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top_crops[location]['Crops'] = sorted(data['Crops'].items(), key=lambda x: x[1], reverse=True)[:5] |
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season_colors = { |
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'Kharif': 'orange', |
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'Rabi': 'green', |
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'Winter': 'blue', |
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'Autumn': 'pink', |
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'Summer': 'yellow', |
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'Whole Year': 'red' |
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} |
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for (latitude, longitude), data in top_crops.items(): |
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season = data['Season'] |
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top_crop_list = data['Crops'] |
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area = data['Area'] |
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top_crops_str = '<br>'.join([f'{crop[0]}: {crop[1]}' for crop in top_crop_list]) |
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folium.CircleMarker( |
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location=[latitude, longitude], |
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radius=7, |
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color=season_colors.get(season, 'gray'), |
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fill=True, |
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fill_color=season_colors.get(season, 'gray'), |
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fill_opacity=0.7, |
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tooltip=(f'Latitude: {latitude}<br>' |
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f'Longitude: {longitude}<br>' |
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f'Season: {season}<br>' |
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f'Area: {area}<br>' |
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f'Top 5 Crops:<br>{top_crops_str}') |
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).add_to(m) |
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map_html = m._repr_html_() |
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return render_template('index.html', map_html=map_html, selected_map='Season Analysis') |
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@app.route('/crop_analysis') |
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def crop_analysis(): |
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global df |
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df_sampled = df.sample(frac=0.005, random_state=42) |
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m = folium.Map(location=[20.5937, 78.9629], zoom_start=5) |
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for district in df_sampled['District'].unique(): |
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district_data = df_sampled[df_sampled['District'] == district] |
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top_crops = district_data.groupby('Crop')['Production'].sum().nlargest(5).index.tolist() |
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lat, lon = district_data.iloc[0]['Latitude'], district_data.iloc[0]['Longitude'] |
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folium.Marker( |
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location=[lat, lon], |
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popup=f'<b>District:</b> {district}<br><b>Top 5 Crops:</b> {', '.join(top_crops)}', |
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icon=folium.Icon(icon='arrow-up', color='green') |
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).add_to(m) |
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map_html = m._repr_html_() |
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return render_template('index.html', map_html=map_html, selected_map='District Crop Analysis') |
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@app.route('/combined_analysis') |
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def combined_analysis(): |
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global df |
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df_sampled = df.sample(frac=0.005, random_state=42) |
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m = folium.Map(location=[20.5937, 78.9629], zoom_start=5) |
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area_heat_data = [ |
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[row['Latitude'], row['Longitude'], row['Area']] |
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for _, row in df_sampled.iterrows() |
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] |
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HeatMap( |
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data=area_heat_data, |
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min_opacity=0.3, |
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max_opacity=0.8, |
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radius=15, |
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blur=10, |
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gradient={0.0: 'blue', 0.5: 'lightblue', 1.0: 'red'} |
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).add_to(m) |
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production_heat_data = [ |
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[row['Latitude'], row['Longitude'], row['Production']] |
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for _, row in df_sampled.iterrows() |
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] |
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HeatMap( |
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data=production_heat_data, |
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min_opacity=0.3, |
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max_opacity=0.8, |
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radius=15, |
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blur=10, |
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gradient={0.0: 'green', 0.5: 'yellow', 1.0: 'red'} |
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).add_to(m) |
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season_colors = { |
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'Kharif': 'purple', |
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'Rabi': 'orange', |
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'Winter': 'Yellow', |
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'Summer': 'Green', |
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'Whole Year': 'Red' |
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} |
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for _, row in df_sampled.iterrows(): |
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season = row['Season'] |
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color = season_colors.get(season, 'gray') |
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folium.CircleMarker( |
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location=[row['Latitude'], row['Longitude'], |
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radius=5, |
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color=color, |
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fill=True, |
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fill_opacity=0.7, |
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tooltip=(f'District: {row['District']}<br>' |
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f'Season: {row['Season']}<br>' |
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f'Area: {row['Area']}<br>' |
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f'Production: {row['Production']}') |
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).add_to(m) |
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map_html = m._repr_html_() |
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return render_template('index.html', map_html=map_html, selected_map='Combined Area & Production Heatmaps') |
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if __name__ == '__main__': |
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app.run(port=7860, host='0.0.0.0') |
