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from flask import Flask,request,render_template
import numpy as np
import pandas
import sklearn
import pickle
# importing model
model = pickle.load(open('model.pkl','rb'))
sc = pickle.load(open('standscaler.pkl','rb'))
ms = pickle.load(open('minmaxscaler.pkl','rb'))
# creating flask app
app = Flask(__name__)
@app.route('/')
def index():
return render_template("index.html")
@app.route("/predict",methods=['POST'])
def predict():
N = request.form['Nitrogen']
P = request.form['Phosporus']
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]]
result = "{} is the best crop to be cultivated right there".format(crop)
else:
result = "Sorry, we could not determine the best crop to be cultivated with the provided data."
return render_template('index.html',result = result)
# python main
if __name__ == "__main__":
app.run(debug=True) |