application.py

from flask import Flask, request, jsonify
import numpy as np
import sklearn
import pickle

model = pickle.load(open('model.pkl', 'rb'))
sc = pickle.load(open('standscaler.pkl', 'rb'))
ms = pickle.load(open('minmaxscaler.pkl', 'rb'))
application = Flask(__name__)

@application.route('/')
def ok():
    return "Running!"
    
@application.route('/pred', methods=['POST'])
def predict():
    N = request.form['Nitrogen']
    P = request.form['Phosphorus']
    K = request.form['Potassium']
    temp = request.form['Temperature']
    humidity = request.form['Humidity']
    ph = request.form['Ph']
    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",
                 19: "Pigeonpeas", 20: "Kidneybeans", 21: "Chickpea", 22: "Coffee"}

   
    if prediction[0] in crop_dict:
        crop = crop_dict[prediction[0]]
    else:
        crop = 'NOT able to recommend'
    return jsonify(crop)


# python main
if __name__ == "__main__":
    application.run(host='0.0.0.0', port=5000, debug=True)