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app.py

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+
+from flask import Flask, request, render_template
+import numpy as np
+import pandas as pd
+import sklearn
+import pickle
+
+# importing model
+model = pickle.load(open('modelcr.pkl','rb'))
+sc = pickle.load(open('standscalercr.pkl','rb'))
+ms = pickle.load(open('minmaxscalercr.pkl','rb'))
+
+# creating flask app
+app = Flask(__name__, template_folder="template")
+
+
+@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)

requirements.txt

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+flask
+numpy
+sklearn