Update app.py

app.py

@@ -1,75 +1,75 @@
-from flask import Flask, request, render_template, jsonify
-import joblib
-import pandas as pd
-
-app = Flask(__name__)
-
-# Load the trained LightGBM model
-lgb_model = joblib.load('lgb_model_cropseason.pkl')
-
-url='https://drive.google.com/file/d/1_vd4HISZB2h2--CiXKezeWDXHHo2fY23/view?usp=sharing'
-data = pd.read_csv('https://drive.usercontent.google.com/download?id={}&export=download&authuser=0&confirm=t'.format(url.split('/')[-2]))
-
-# Extract unique values for states, districts, crops, and seasons
-unique_states = data['State'].unique()
-unique_crops = data['Crop'].unique()
-
-# Descriptions for each cropping season
-season_descriptions = {
-    'Kharif': 'Kharif season occurs from June to October, associated with the monsoon. Crops are usually sown at the start of the rainy season.',
-    'Rabi': 'Rabi season spans from October to March, during the winter cropping season, with crops like wheat and barley.',
-    'Summer': 'Summer season is from April to June, suitable for crops that need warmer temperatures.',
-    'Winter': 'Winter cropping season occurs from November to February, including cold-weather crops.',
-    'Whole Year': 'Crops can be grown throughout the year, without seasonal limitations.',
-    'Autumn': 'Autumn season, from September to November, accommodates crops suited to a post-monsoon environment.'
-}
-
-@app.route('/')
-def home():
-    return render_template('index.html', states=unique_states, crops=unique_crops, seasons=season_descriptions.keys())
-
-@app.route('/filter_districts', methods=['POST'])
-def filter_districts():
-    state = request.form.get('state')
-    filtered_districts = data[data['State'] == state]['District'].unique()
-    return jsonify({'districts': list(filtered_districts)})
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    state = request.form.get('state')
-    district = request.form.get('district')
-    crop_year = int(request.form.get('crop_year'))
-    crop = request.form.get('crop')
-    area = float(request.form.get('area'))
-
-    input_data = pd.DataFrame({
-        'State': [state],
-        'District': [district],
-        'Crop_Year': [crop_year],
-        'Crop': [crop],
-        'Area': [area]
-    })
-
-    input_data['State'] = input_data['State'].astype('category')
-    input_data['District'] = input_data['District'].astype('category')
-    input_data['Crop'] = input_data['Crop'].astype('category')
-
-    predicted_season = lgb_model.predict(input_data)[0]
-
-    # Debug: print the predicted season to console
-    print(f"Predicted Season: {predicted_season}")
-
-    # Ensure the predicted season is treated as a string for matching
-    predicted_season_str = str(predicted_season)  # Ensure it's a stri
-
-    # Check if the predicted season is in the descriptions
-    if predicted_season in season_descriptions:
-        season_description = season_descriptions[predicted_season]
-    else:
-        season_description = 'No description available'
-
-    return render_template('index.html', states=unique_states, crops=unique_crops, seasons=season_descriptions.keys(),
-                           predicted_season=predicted_season, season_description=season_description)
-
-if __name__ == '__main__':
-    app.run(debug=True)
+from flask import Flask, request, render_template, jsonify
+import joblib
+import pandas as pd
+
+app = Flask(__name__)
+
+# Load the trained LightGBM model
+lgb_model = joblib.load('lgb_model_cropseason.pkl')
+
+url='https://drive.google.com/file/d/1_vd4HISZB2h2--CiXKezeWDXHHo2fY23/view?usp=sharing'
+data = pd.read_csv('https://drive.usercontent.google.com/download?id={}&export=download&authuser=0&confirm=t'.format(url.split('/')[-2]))
+
+# Extract unique values for states, districts, crops, and seasons
+unique_states = data['State'].unique()
+unique_crops = data['Crop'].unique()
+
+# Descriptions for each cropping season
+season_descriptions = {
+    'Kharif': 'Kharif season occurs from June to October, associated with the monsoon. Crops are usually sown at the start of the rainy season.',
+    'Rabi': 'Rabi season spans from October to March, during the winter cropping season, with crops like wheat and barley.',
+    'Summer': 'Summer season is from April to June, suitable for crops that need warmer temperatures.',
+    'Winter': 'Winter cropping season occurs from November to February, including cold-weather crops.',
+    'Whole Year': 'Crops can be grown throughout the year, without seasonal limitations.',
+    'Autumn': 'Autumn season, from September to November, accommodates crops suited to a post-monsoon environment.'
+}
+
+@app.route('/')
+def home():
+    return render_template('index.html', states=unique_states, crops=unique_crops, seasons=season_descriptions.keys())
+
+@app.route('/filter_districts', methods=['POST'])
+def filter_districts():
+    state = request.form.get('state')
+    filtered_districts = data[data['State'] == state]['District'].unique()
+    return jsonify({'districts': list(filtered_districts)})
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    state = request.form.get('state')
+    district = request.form.get('district')
+    crop_year = int(request.form.get('crop_year'))
+    crop = request.form.get('crop')
+    area = float(request.form.get('area'))
+
+    input_data = pd.DataFrame({
+        'State': [state],
+        'District': [district],
+        'Crop_Year': [crop_year],
+        'Crop': [crop],
+        'Area': [area]
+    })
+
+    input_data['State'] = input_data['State'].astype('category')
+    input_data['District'] = input_data['District'].astype('category')
+    input_data['Crop'] = input_data['Crop'].astype('category')
+
+    predicted_season = lgb_model.predict(input_data)[0]
+
+    # Debug: print the predicted season to console
+    print(f"Predicted Season: {predicted_season}")
+
+    # Ensure the predicted season is treated as a string for matching
+    predicted_season_str = str(predicted_season)  # Ensure it's a stri
+
+    # Check if the predicted season is in the descriptions
+    if predicted_season in season_descriptions:
+        season_description = season_descriptions[predicted_season]
+    else:
+        season_description = 'No description available'
+
+    return render_template('index.html', states=unique_states, crops=unique_crops, seasons=season_descriptions.keys(),
+                           predicted_season=predicted_season, season_description=season_description)
+
+if __name__ == '__main__':
+    app.run(port=7860,host='0.0.0.0')