Update app.py

app.py

@@ -1,67 +1,68 @@
-import numpy as np
-from flask import Flask, request, jsonify, render_template
-import joblib
-
-# Initialize Flask app
-app = Flask(__name__)
-
-# Load the trained Random Forest model
-rf_model = joblib.load('random_forest_model.pkl')
-
-# Encoder classes
-soil_type_classes = ['Alluvial Soil', 'Black Soil', 'Clay Soil', 'Red Soil']
-crop_classes = ['All vegetables Tea Coffee Rubber Coconut Cashew Avocado',
-                'Cotton Blackgram Oilseeds Pigeonpea',
-                'Cotton Jowar Pigeonpea Blackgram',
-                'Cotton Rice Pigeonpea Blackgram Sunflower',
-                'Cotton Sorghum CerealCrops Blackgram',
-                'Cotton Sugarcane Pigeonpea Sorghum',
-                'Pearlmillet Basil Blackgram Sorghum',
-                'Pearlmillet Maize Pigeonpea Greengram Garlic',
-                'Pearlmillet Ragi Groundnut Potato All vegetables',
-                'Soybean Pigeonpea Millets Greengram',
-                'Soybean Pigeonpea Maize Sorghum']
-
-# Route for the home page
-@app.route('/')
-def index():
-    return render_template('index.html', soil_types=soil_type_classes)
-
-# Route to handle the prediction
-@app.route('/predict', methods=['POST'])
-def predict():
-    try:
-        # Get form data
-        soil_type = request.form.get('soil_type')
-        soil_depth = float(request.form.get('soil_depth'))
-        ph = float(request.form.get('ph'))
-        bulk_density = float(request.form.get('bulk_density'))
-        ec = float(request.form.get('ec'))
-        organic_carbon = float(request.form.get('organic_carbon'))
-        soil_moisture_retention = float(request.form.get('soil_moisture_retention'))
-        available_water_capacity = float(request.form.get('available_water_capacity'))
-        infiltration_rate = float(request.form.get('infiltration_rate'))
-        clay_percentage = float(request.form.get('clay_percentage'))
-
-        # Encode soil type
-        soil_type_encoded = soil_type_classes.index(soil_type)
-
-        # Create feature array
-        features = np.array([[soil_type_encoded, soil_depth, ph, bulk_density, ec, organic_carbon,
-                              soil_moisture_retention, available_water_capacity, infiltration_rate, clay_percentage]])
-
-        # Make prediction
-        predicted_crop_index = rf_model.predict(features)[0]
-        predicted_crop = crop_classes[predicted_crop_index]
-
-        # Split the crops into separate columns (based on spaces)
-        predicted_crop_list = predicted_crop.split()
-
-        # Return the predicted crops as a list to the front-end
-        return jsonify({'predicted_crop_list': predicted_crop_list})
-
-    except Exception as e:
-        return jsonify({'error': str(e)})
-
-if __name__ == '__main__':
-    app.run(port=7861,host='0.0.0.0')
+import numpy as np
+from flask import Flask, request, jsonify, render_template
+import joblib
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Load the trained Random Forest model
+rf_model = joblib.load('random_forest_model.pkl')
+
+# Encoder classes
+soil_type_classes = ['Alluvial Soil', 'Black Soil', 'Clay Soil', 'Red Soil']
+crop_classes = ['All vegetables Tea Coffee Rubber Coconut Cashew Avocado',
+                'Cotton Blackgram Oilseeds Pigeonpea',
+                'Cotton Jowar Pigeonpea Blackgram',
+                'Cotton Rice Pigeonpea Blackgram Sunflower',
+                'Cotton Sorghum CerealCrops Blackgram',
+                'Cotton Sugarcane Pigeonpea Sorghum',
+                'Pearlmillet Basil Blackgram Sorghum',
+                'Pearlmillet Maize Pigeonpea Greengram Garlic',
+                'Pearlmillet Ragi Groundnut Potato All vegetables',
+                'Soybean Pigeonpea Millets Greengram',
+                'Soybean Pigeonpea Maize Sorghum']
+
+# Route for the home page
+@app.route('/')
+def index():
+    return render_template('index.html', soil_types=soil_type_classes)
+
+# Route to handle the prediction
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Get form data
+        soil_type = request.form.get('soil_type')
+        soil_depth = float(request.form.get('soil_depth'))
+        ph = float(request.form.get('ph'))
+        bulk_density = float(request.form.get('bulk_density'))
+        ec = float(request.form.get('ec'))
+        organic_carbon = float(request.form.get('organic_carbon'))
+        soil_moisture_retention = float(request.form.get('soil_moisture_retention'))
+        available_water_capacity = float(request.form.get('available_water_capacity'))
+        infiltration_rate = float(request.form.get('infiltration_rate'))
+        clay_percentage = float(request.form.get('clay_percentage'))
+
+        # Encode soil type
+        soil_type_encoded = soil_type_classes.index(soil_type)
+
+        # Create feature array
+        features = np.array([[soil_type_encoded, soil_depth, ph, bulk_density, ec, organic_carbon,
+                              soil_moisture_retention, available_water_capacity, infiltration_rate, clay_percentage]])
+
+        # Make prediction
+        predicted_crop_index = rf_model.predict(features)[0]
+        predicted_crop = crop_classes[predicted_crop_index]
+
+        # Split the crops into separate columns (based on spaces)
+        predicted_crop_list = predicted_crop.split()
+
+        # Return the predicted crops as a list to the front-end
+        return jsonify({'predicted_crop_list': predicted_crop_list})
+
+    except Exception as e:
+        return jsonify({'error': str(e)})
+
+if __name__ == '__main__':
+    port = int(os.environ.get("PORT", 7860))
+    app.run(host="0.0.0.0", port=port)