--- a
+++ b/demo deployment/Deployment/demo.py
@@ -0,0 +1,97 @@
+import pandas as pd
+import numpy as np
+from flask import Flask, request, render_template
+import pickle
+import nltk
+from nltk.corpus import wordnet as wn 
+from nltk.stem import WordNetLemmatizer 
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+import string
+import re
+
+app = Flask(__name__)
+model = pickle.load(open('bestmodel.pkl', 'rb'))
+pca_model = pickle.load(open('word2vec.pkl', 'rb'))
+w2v_model = pickle.load(open('w2v_model.pkl', 'rb'))
+stopwords_list = set(stopwords.words("english"))
+
+def input_process(data):
+    input_clean = phrase_cleanse(data)
+    w2v_model = pickle.load(open('w2v_model.pkl', 'rb'))
+    input_clean = [input_clean.split(" ")]
+    processed_input = word_avg_vect(input_clean, w2v_model, 100)
+    pca_model = pickle.load(open('word2vec.pkl', 'rb')) 
+    test = pca_model.transform(processed_input)
+    return test
+
+def phrase_cleanse(phrase):
+    #Tokenize and divide phrase into separate words
+    token_words = word_tokenize(phrase)
+    
+    # Convert all texts to lower cases
+    words_step1 = []
+    for word_1 in token_words:
+        words_step1.append(word_1.lower())
+    
+    #Clear all punctuation
+    words_step2 = [] 
+    for word_2 in words_step1:
+        word_cleaned = re.sub(r'[^\w\s]','',word_2)
+        words_step2.append(word_cleaned)
+    
+    #Clean the text list
+    words_step3 = []
+    for word_3 in words_step2:
+        # check if every characters are alphbets
+        if word_3.isalpha():
+            # get rid of stop words
+            if word_3 not in list(stopwords_list):
+                words_step3.append(word_3)
+            else:
+                continue
+    
+    #Lemmatization - group different forms of same word which has more than 2 characters into one word
+    lem = nltk.stem.WordNetLemmatizer()
+    lem_list = []
+    for word_4 in words_step3:
+        if(len(word_4) > 2):
+            lem_list.append(lem.lemmatize(word_4))
+    
+    join_text = " ".join(lem_list)
+    
+    return join_text
+
+def word_avg_vect(data, model, num_features):
+    words = set(model.wv.index_to_key)
+    X_vect = np.array([np.array([model.wv[i] for i in s if i in words]) for s in data])
+    X_vect_avg = []
+    for v in X_vect:
+        if v.size:
+            X_vect_avg.append(v.mean(axis = 0))
+        else:
+            X_vect_avg.append(np.zeros(num_features, dtype = float))
+
+    df_vect_avg = pd.DataFrame(X_vect_avg)
+    return df_vect_avg
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/predict',methods=['POST'])
+def predict():
+    '''
+    For rendering results on HTML GUI
+    '''
+    input =  request.form['medical diagonisis']
+    final_features = input_process(input)
+    prediction = model.predict(final_features)
+
+    output = prediction[0]
+
+    return render_template('index.html', prediction_text='Our predicted diagnosis is {}'.format(output))
+
+
+if __name__ == "__main__":
+    app.run(debug=True)
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