Update app.py

app.py

@@ -2,7 +2,6 @@ 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__)
 
@@ -10,33 +9,36 @@ app = Flask(__name__)
 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")
-
+    return render_template('index.html', map_html='', selected_map='Home')
 
-@app.route('/prodction_analysis', methods=['GET', 'POST'])
+@app.route('/production_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",
+        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",
+        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()]
+    heatmap_data = [[
+        [row['Latitude'], row['Longitude']]
+        for _, row in crop_data[crop_data['Crop_Year'] == year].dropna().iterrows()
+    ]
         for year in time_index
     ]
 
+    for year, data in zip(time_index, heatmap_data):
+        print(f'Year: {year}, Data: {data}')
+
     m = folium.Map(location=[20.5937, 78.9629], zoom_start=5)
     heatmap = HeatMapWithTime(
         heatmap_data,
@@ -47,17 +49,16 @@ def production_analysis():
     heatmap.add_to(m)
 
     map_html = m._repr_html_()
-    return render_template('index.html', map_html=map_html, selected_map="Production Heatmap Analysis",
+    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
+    global df
     m = folium.Map(location=[20.5937, 78.9629], zoom_start=5)
-    fg = folium.FeatureGroup(name="Crops")
+    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
+    df_sampled = df.sample(frac=0.005, random_state=42)
     for crop in df_sampled['Crop'].unique():
         subgroup = FeatureGroupSubGroup(fg, crop)
         m.add_child(subgroup)
@@ -69,23 +70,14 @@ def heatmap_analysis():
     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")
-
+    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
+    global df
     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']
@@ -98,19 +90,16 @@ def season_analysis():
             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'
+        'Autumn': 'pink',
+        'Summer': 'yellow',
+        'Whole Year': 'red'
     }
 
     for (latitude, longitude), data in top_crops.items():
@@ -118,35 +107,29 @@ def season_analysis():
         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])
+        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
+            radius=7,
+            color=season_colors.get(season, 'gray'),
             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}")
+            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")
-
+    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
+    global df
+    df_sampled = df.sample(frac=0.005, random_state=42)
     m = folium.Map(location=[20.5937, 78.9629], zoom_start=5)
 
     for district in df_sampled['District'].unique():
@@ -156,31 +139,24 @@ def crop_analysis():
 
         folium.Marker(
             location=[lat, lon],
-            popup=f"<b>District:</b> {district}<br><b>Top 5 Crops:</b> {', '.join(top_crops)}",
+            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")
-
+    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
+    global df
     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,
@@ -190,13 +166,11 @@ def combined_analysis():
         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,
@@ -206,36 +180,31 @@ def combined_analysis():
         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'
+        '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
+        color = season_colors.get(season, 'gray')
         folium.CircleMarker(
-            location=[row['Latitude'], row['Longitude']],
+            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']}")
+            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")
+    return render_template('index.html', map_html=map_html, selected_map='Combined Area & Production Heatmaps')
 
 if __name__ == '__main__':
-    app.run(port=7860,host='0.0.0.0')
+    app.run(port=7860, host='0.0.0.0')