| from flask import Flask, request, jsonify
|
| import numpy as np
|
| import sklearn
|
| import pickle
|
|
|
| model = pickle.load(open('model.pkl', 'rb'))
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| sc = pickle.load(open('standscaler.pkl', 'rb'))
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| ms = pickle.load(open('minmaxscaler.pkl', 'rb'))
|
| application = Flask(__name__)
|
|
|
| @application.route('/')
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| def ok():
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| return "Running!"
|
|
|
| @application.route('/pred', methods=['POST'])
|
| def predict():
|
| N = request.form['Nitrogen']
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| P = request.form['Phosphorus']
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| K = request.form['Potassium']
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| temp = request.form['Temperature']
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| humidity = request.form['Humidity']
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| ph = request.form['Ph']
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| rainfall = request.form['Rainfall']
|
|
|
| feature_list = [N, P, K, temp, humidity, ph, rainfall]
|
| single_pred = np.array(feature_list).reshape(1, -1)
|
|
|
| scaled_features = ms.transform(single_pred)
|
| final_features = sc.transform(scaled_features)
|
| prediction = model.predict(final_features)
|
|
|
| crop_dict = {1: "Rice", 2: "Maize", 3: "Jute", 4: "Cotton", 5: "Coconut", 6: "Papaya", 7: "Orange",
|
| 8: "Apple", 9: "Muskmelon", 10: "Watermelon", 11: "Grapes", 12: "Mango", 13: "Banana",
|
| 14: "Pomegranate", 15: "Lentil", 16: "Blackgram", 17: "Mungbean", 18: "Mothbeans",
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| 19: "Pigeonpeas", 20: "Kidneybeans", 21: "Chickpea", 22: "Coffee"}
|
|
|
|
|
| if prediction[0] in crop_dict:
|
| crop = crop_dict[prediction[0]]
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| else:
|
| crop = 'NOT able to recommend'
|
| return jsonify(crop)
|
|
|
|
|
|
|
| if __name__ == "__main__":
|
| application.run(host='0.0.0.0', port=5000, debug=True)
|
|
|
