Update app.py

app.py

@@ -1,241 +1,241 @@
-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)
+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(port=7860,host='0.0.0.0')