created It20229948 back end

app final.py

+import joblib
+from flask import Flask,request,render_template
+import numpy as np
+import pickle
+# # Load your machine learning model
+model = joblib.load('finalmodel.joblib')
+# importing model
+#model = pickle.load(open('finalmodel.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']
+
+
+    feature_list = [N, P, K, temp, humidity, ph]
+    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: "AT 354",
+         2: "BG 250",
+         3: "BG 352",
+         4: 'Mottaikaruppan',
+         5: 'Suwandel'
+     }
+
+    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)
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