--- a
+++ b/medic_health_assistant/app.py
@@ -0,0 +1,81 @@
+from tensorflow.keras.models import load_model
+from flask import Flask, jsonify, request
+from flask_cors import CORS
+import pickle
+import numpy as np
+import nltk
+from nltk import LancasterStemmer
+import pandas as pd
+
+stemmer = LancasterStemmer()
+
+app = Flask(__name__)
+CORS(app)
+
+model = load_model("chatbot_model.hdf5")
+
+with open('labels.pkl', 'rb') as f:
+    labels = pickle.load(f)
+
+with open('words.pkl', 'rb') as f:
+    words = pickle.load(f)
+
+
+def clean_up_sentence(sentence):
+    sentence_words = nltk.word_tokenize(sentence)
+    sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]
+    return sentence_words
+
+def bow(sentence, words, show_details=True):
+    sentence_words = clean_up_sentence(sentence)
+    bag = [0]*len(words)
+    for s in sentence_words:
+        for i,w in enumerate(words):
+            if w == s:
+                bag[i] = 1
+                if show_details:
+                    print ("found in bag: %s" % w)
+
+    return(np.array(bag))
+
+@app.route('/')
+def home_endpoint():
+    return 'Hello there, welcome to Team Medic Minds'
+
+
+@app.route('/api/predict', methods=['GET', 'POST'])
+def classify():
+    ERROR_THRESHOLD = 0.25
+
+    sentence = request.json['sentence']
+    input_data = pd.DataFrame([bow(sentence, words)], dtype=float, index=['input']).to_numpy()
+    results = model.predict([input_data])[0]
+    results = [[i, r] for i, r in enumerate(results) if r > ERROR_THRESHOLD]
+    results.sort(key=lambda x: x[1], reverse=True)
+    return_list = []
+    for r in results:
+        return_list.append({"intent": labels[r[0]], "probability": str(r[1])})
+
+    response = jsonify(return_list)
+    return response
+
+if __name__ == '__main__':
+
+    app.run(host='0.0.0.0', port=5000)
+
+
+'''
+def get_prediction():
+    sentence = request.json['sentence']
+    
+    if flask.request.method == 'POST':
+        data = flask.request.json  # Get data posted as a json
+        if data == None:
+            data = flask.request.args
+
+        input = data.get('data')
+        prediction = predictStringInput(chatbot_model,input)
+
+    return prediction
+
+'''
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