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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+final_crop_historic_data_pkjk.csv filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +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)

final_crop_historic_data_pkjk.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9cf39965597f1b461e1351928200036a07929f434420cc40e84d097f9226fc3a
+size 311198769

templates/crop_analysis.html

@@ -0,0 +1,11 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Crop Analysis</title>
+</head>
+<body>
+    <iframe src="{{ map_url }}"></iframe>
+</body>
+</html>

templates/index.html

@@ -0,0 +1,88 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Crop Production Analysis</title>
+    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css">
+    <style>
+        body {
+            padding: 20px;
+            background-color: #f4f4f9;
+        }
+        .navbar {
+            background-color: black;
+            color: white;
+        }
+        .navbar a {
+            color: white;
+        }
+        .navbar-nav {
+            flex-direction: row;
+            justify-content: space-between;
+            width: 100%;
+        }
+        .container {
+            background: white;
+            border-radius: 8px;
+            padding: 20px;
+            max-height: 1500px;
+            max-width: 1700px;
+            margin-top: 20px;
+            margin-bottom: 20px;
+        }
+        .heading {
+            font-weight: bold;
+            color: green;
+        }
+        .btn-red {
+            background-color: red;
+            color: white;
+        }
+        #map-container {
+            margin-top: 20px;
+            margin-bottom:20px;
+            height: 500px;
+            border: 2px solid #ddd;
+        }
+    </style>
+</head>
+<body>
+    <nav class="navbar navbar-expand-lg">
+        <a class="navbar-brand" href="/">Crop Analysis</a>
+        <div class="collapse navbar-collapse">
+            <ul class="navbar-nav mr-auto">
+                <li class="nav-item"><a class="nav-link" href="/prodction_analysis">Production Analysis</a></li>
+                <li class="nav-item"><a class="nav-link" href="/heatmap_analysis">Heatmap Analysis</a></li>
+                <li class="nav-item"><a class="nav-link" href="/season_analysis">Season Analysis</a></li>
+                <li class="nav-item"><a class="nav-link" href="/crop_analysis">Geospatial Analysis</a></li>
+                <li class="nav-item"><a class="nav-link" href="/combined_analysis">Combined Analysis</a></li>
+            </ul>
+        </div>
+    </nav>
+
+    <div class="container">
+        <h2 class="heading">Crop Production Analysis</h2>
+        <form method="POST" action="/prodction_analysis">
+            <div class="form-group">
+                <label for="crop_type">Select Crop Type:</label>
+                <select class="form-control" id="crop_type" name="crop_type">
+                    <option value="">Select Crop</option>
+                    {% for crop in crop_options %}
+                    <option value="{{ crop }}" {% if crop == selected_crop %}selected{% endif %}>{{ crop }}</option>
+                    {% endfor %}
+                </select>
+            </div>
+            <button type="submit" class="btn btn-success">Analyze</button>
+        </form>
+
+        <div id="map-container">
+            {{ map_html | safe }}
+        </div>
+    </div>
+
+    <script src="https://code.jquery.com/jquery-3.2.1.slim.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.11.0/umd/popper.min.js"></script>
+    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js"></script>
+</body>
+</html>

templates/prediction_analysis.html

@@ -0,0 +1,11 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Production Analysis</title>
+</head>
+<body>
+    <iframe src="{{ map_url }}"></iframe>
+</body>
+</html>

templates/production_heatmap.html

@@ -0,0 +1,21 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Production Heatmap</title>
+</head>
+<body>
+    <form method="get">
+        <div class="scrollable">
+            <label>Select Crops:</label><br>
+            {% for crop in crops %}
+                <input type="checkbox" name="crops" value="{{ crop }}" {% if crop in selected_crops %}checked{% endif %}>
+                {{ crop }}<br>
+            {% endfor %}
+        </div>
+        <input type="submit" value="Update Heatmap">
+    </form>
+    <iframe src="{{ map_url }}"></iframe>
+</body>
+</html>

templates/seasonal_analysis.html

@@ -0,0 +1,11 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Season Analysis</title>
+</head>
+<body>
+    <img src="{{ image_url }}" alt="Season Analysis">
+</body>
+</html>