--- a
+++ b/app.py
@@ -0,0 +1,43 @@
+from flask import Flask,request, url_for, redirect, render_template
+import pickle
+import numpy as np
+import sklearn
+from sklearn.preprocessing import StandardScaler
+
+app = Flask(__name__)
+
+#model = pickle.load(open('model.pkl','rb'))
+
+filename = 'Lung_Cancer.pkl'
+with open(filename, 'rb') as f:
+    model = pickle.load(f)
+
+@app.route('/')
+def hello_world():
+    return render_template("lung_cancer.html")
+
+#Parameters used for Prediction
+# ['GENDER', 'AGE', 'SMOKING', 'YELLOW_FINGERS', 'ANXIETY',
+#        'PEER_PRESSURE', 'CHRONIC DISEASE', 'FATIGUE ', 'ALLERGY ', 'WHEEZING',
+#        'ALCOHOL CONSUMING', 'COUGHING', 'SHORTNESS OF BREATH',
+#        'SWALLOWING DIFFICULTY', 'CHEST PAIN', 'LUNG_CANCER']
+
+@app.route('/predict',methods=['POST', 'GET'])
+def predict():
+        if request.method == 'POST':
+            int_features = [int(x) for x in request.form.values()]
+            final = np.reshape(int_features, (1, -1))
+            print(int_features)  #Checking Inputs Successfully Added
+            print(final) #Reshaping into numpy array for Prediction
+            prediction = model.predict(final)
+            print(prediction) # Checking the Prediction Value
+            output = prediction
+            if output == 0:
+                return render_template('lung_cancer.html', pred='Person Has Lung Cancer {}'.format(output))
+            else:
+                return render_template('lung_cancer.html', pred='Person Does Not Got Lung Cancer {}'.format(output))
+        else:
+            return render_template('lung_cancer.html')
+
+if __name__ == '__main__':
+    app.run(debug=True)