app.py

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

model = pickle.load(open('crop_recommendation.pkl', 'rb'))
sc = pickle.load(open('standardscaler.pkl', 'rb'))
mx = pickle.load(open('minmaxscaler.pkl', 'rb'))

app = Flask(__name__)

@app.route('/')
def index():
    return render_template("index.html")

@app.route('/home')
def home():
    return render_template("home.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)

    mx_features = mx.transform(single_pred)
    sc_mx_features = sc.transform(mx_features)
    prediction = model.predict(sc_mx_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"
    }

    crop = crop_dict.get(prediction[0], "Unknown crop")
    result = f"{crop} is the best crop to be cultivated right there" if crop != "Unknown crop" else "Could not determine the best crop."
    
    return render_template('home.html', result=result)

if __name__ == "__main__":
    app.run(debug=True)