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from flask import Flask,request,render_template
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import numpy as np
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import pandas
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import sklearn
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import pickle
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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'))
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app = Flask(__name__)
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@app.route('/')
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def index():
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return render_template("index.html")
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@app.route("/predict",methods=['POST'])
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def predict():
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N = request.form['Nitrogen']
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P = request.form['Phosporus']
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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']
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feature_list = [N, P, K, temp, humidity, ph, rainfall]
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single_pred = np.array(feature_list).reshape(1, -1)
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scaled_features = ms.transform(single_pred)
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final_features = sc.transform(scaled_features)
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prediction = model.predict(final_features)
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crop_dict = {1: "Rice", 2: "Maize", 3: "Jute", 4: "Cotton", 5: "Coconut", 6: "Papaya", 7: "Orange",
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8: "Apple", 9: "Muskmelon", 10: "Watermelon", 11: "Grapes", 12: "Mango", 13: "Banana",
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14: "Pomegranate", 15: "Lentil", 16: "Blackgram", 17: "Mungbean", 18: "Mothbeans",
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19: "Pigeonpeas", 20: "Kidneybeans", 21: "Chickpea", 22: "Coffee"}
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if prediction[0] in crop_dict:
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crop = crop_dict[prediction[0]]
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result = "{} is the best crop to be cultivated right there".format(crop)
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else:
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result = "Sorry, we could not determine the best crop to be cultivated with the provided data."
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return render_template('index.html',result = result)
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if __name__ == "__main__":
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app.run(debug=True) |
