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from flask import Flask, render_template, request
import folium
from folium.plugins import HeatMapWithTime, FeatureGroupSubGroup, HeatMap
import pandas as pd
import os
app = Flask(__name__)
# Load the dataset
df = pd.read_csv('final_crop_historic_data_pkjk.csv')
df.columns = ['State', 'District', 'Crop_Year', 'Season', 'Crop', 'Area', 'Production', 'Latitude', 'Longitude']
@app.route('/')
def home():
return render_template('index.html', map_html="", selected_map="Home")
@app.route('/prodction_analysis', methods=['GET', 'POST'])
def production_analysis():
crop_options = df['Crop'].unique().tolist()
selected_crop = request.form.get('crop_type') if request.method == 'POST' else None
if not selected_crop:
return render_template('index.html', map_html="", selected_map="Production Analysis",
crop_options=crop_options, selected_crop=None)
crop_data = df[df['Crop'] == selected_crop]
if crop_data.empty:
return render_template('index.html', map_html="", selected_map="No Data Available",
crop_options=crop_options, selected_crop=selected_crop)
time_index = crop_data['Crop_Year'].unique()
heatmap_data = [
[[row['Latitude'], row['Longitude']] for _, row in crop_data[crop_data['Crop_Year'] == year].iterrows()]
for year in time_index
]
m = folium.Map(location=[20.5937, 78.9629], zoom_start=5)
heatmap = HeatMapWithTime(
heatmap_data,
index=[str(year) for year in time_index],
auto_play=True,
max_opacity=0.6
)
heatmap.add_to(m)
map_html = m._repr_html_()
return render_template('index.html', map_html=map_html, selected_map="Production Heatmap Analysis",
crop_options=crop_options, selected_crop=selected_crop)
@app.route('/heatmap_analysis')
def heatmap_analysis():
global df # Declare df as global
m = folium.Map(location=[20.5937, 78.9629], zoom_start=5)
fg = folium.FeatureGroup(name="Crops")
m.add_child(fg)
df_sampled = df.sample(frac=0.005, random_state=42) # Use a different variable for sampled df
for crop in df_sampled['Crop'].unique():
subgroup = FeatureGroupSubGroup(fg, crop)
m.add_child(subgroup)
crop_data = df_sampled[df_sampled['Crop'] == crop]
heatmap_data = [[row['Latitude'], row['Longitude']] for _, row in crop_data.iterrows()]
HeatMap(heatmap_data).add_to(subgroup)
folium.LayerControl(collapsed=False).add_to(m)
map_html = m._repr_html_()
return render_template('index.html', map_html=map_html, selected_map="Crop Heatmap Analysis")
@app.route('/season_analysis')
def season_analysis():
global df # Declare df as global
# Initialize the map centered over India with an appropriate zoom level
m = folium.Map(location=[20.5937, 78.9629], zoom_start=5)
# Sample a fraction of the dataframe for performance
df_sampled = df.sample(frac=0.005, random_state=42)
# Create a dictionary to store top 5 crops for each location
top_crops = {}
# Collect the top crops for each unique location (Latitude, Longitude)
for _, row in df_sampled.iterrows():
lat_lon = (row['Latitude'], row['Longitude'])
crop = row['Crop']
production = row['Production']
if lat_lon not in top_crops:
top_crops[lat_lon] = {'Season': row['Season'], 'Crops': {}, 'Area': row['Area']}
if crop not in top_crops[lat_lon]['Crops']:
top_crops[lat_lon]['Crops'][crop] = 0
top_crops[lat_lon]['Crops'][crop] += production
# Limit to top 5 crops for each location
for location, data in top_crops.items():
top_crops[location]['Crops'] = sorted(data['Crops'].items(), key=lambda x: x[1], reverse=True)[:5]
# Add scatter points for each unique location with a different color for each season
season_colors = {
'Kharif': 'orange',
'Rabi': 'green',
'Winter': 'blue',
'Autumn':'pink',
'Rabi':'brown',
'Summer':'yellow',
'Whole Year':'Red'
}
for (latitude, longitude), data in top_crops.items():
season = data['Season']
top_crop_list = data['Crops']
area = data['Area']
# Create a string for the top crops
top_crops_str = "<br>".join([f"{crop[0]}: {crop[1]}" for crop in top_crop_list])
# Add a scatter point to the map for each location
folium.CircleMarker(
location=[latitude, longitude],
radius=7, # Fixed radius for scatter points
color=season_colors.get(season, 'gray'), # Use season color or gray if not found
fill=True,
fill_color=season_colors.get(season, 'gray'),
fill_opacity=0.7,
tooltip=(f"Latitude: {latitude}<br>"
f"Longitude: {longitude}<br>"
f"Season: {season}<br>"
f"Area: {area}<br>"
f"Top 5 Crops:<br>{top_crops_str}")
).add_to(m)
# Convert the map to HTML format for rendering
map_html = m._repr_html_()
# Render the map in the template
return render_template('index.html', map_html=map_html, selected_map="Season Analysis")
@app.route('/crop_analysis')
def crop_analysis():
global df # Declare df as global
df_sampled = df.sample(frac=0.005, random_state=42) # Use a different variable for sampled df
m = folium.Map(location=[20.5937, 78.9629], zoom_start=5)
for district in df_sampled['District'].unique():
district_data = df_sampled[df_sampled['District'] == district]
top_crops = district_data.groupby('Crop')['Production'].sum().nlargest(5).index.tolist()
lat, lon = district_data.iloc[0]['Latitude'], district_data.iloc[0]['Longitude']
folium.Marker(
location=[lat, lon],
popup=f"<b>District:</b> {district}<br><b>Top 5 Crops:</b> {', '.join(top_crops)}",
icon=folium.Icon(icon='arrow-up', color='green')
).add_to(m)
map_html = m._repr_html_()
return render_template('index.html', map_html=map_html, selected_map="District Crop Analysis")
@app.route('/combined_analysis')
def combined_analysis():
global df # Declare df as global
# Sample a fraction of the dataframe for performance
df_sampled = df.sample(frac=0.005, random_state=42)
# Create the map centered over India with an appropriate zoom level
m = folium.Map(location=[20.5937, 78.9629], zoom_start=5)
# Prepare heatmap data for area
area_heat_data = [
[row['Latitude'], row['Longitude'], row['Area']]
for _, row in df_sampled.iterrows()
]
# Add the heatmap for area (blue to red: low to high)
HeatMap(
data=area_heat_data,
min_opacity=0.3,
max_opacity=0.8,
radius=15,
blur=10,
gradient={0.0: 'blue', 0.5: 'lightblue', 1.0: 'red'}
).add_to(m)
# Prepare heatmap data for production
production_heat_data = [
[row['Latitude'], row['Longitude'], row['Production']]
for _, row in df_sampled.iterrows()
]
# Add the heatmap for production (green to red: low to high production)
HeatMap(
data=production_heat_data,
min_opacity=0.3,
max_opacity=0.8,
radius=15,
blur=10,
gradient={0.0: 'green', 0.5: 'yellow', 1.0: 'red'}
).add_to(m)
# Scatter plot for different seasons with distinct colors
season_colors = {
'Kharif': 'purple',
'Rabi': 'orange',
'Rabi': 'cyan',
'Winter':'Yellow',
'Summer':'Green',
'Whole Year':'Red'
}
for _, row in df_sampled.iterrows():
season = row['Season']
color = season_colors.get(season, 'gray') # Default to gray if the season is not recognized
folium.CircleMarker(
location=[row['Latitude'], row['Longitude']],
radius=5,
color=color,
fill=True,
fill_opacity=0.7,
tooltip=(f"District: {row['District']}<br>"
f"Season: {row['Season']}<br>"
f"Area: {row['Area']}<br>"
f"Production: {row['Production']}")
).add_to(m)
# Convert the map to HTML format
map_html = m._repr_html_()
# Render the map in the template
return render_template('index.html', map_html=map_html, selected_map="Combined Area & Production Heatmaps")
if __name__ == '__main__':
app.run(debug=True)
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