--- a
+++ b/medic_health_assistant/train_data.py
@@ -0,0 +1,147 @@
+import json
+import os
+import numpy as np
+import nltk
+from nltk import LancasterStemmer, WordNetLemmatizer
+import random
+import pickle
+from tensorflow.keras.layers import Dense, Dropout
+from tensorflow.keras.models import Sequential
+import pandas as pd
+
+stemmer = LancasterStemmer()
+lemmatizer = WordNetLemmatizer()
+
+
+path = "dataset/medical-question-answer-data"
+
+def load_doc(jsonFile):
+    with open(jsonFile) as file:
+        Json_data = json.loads(file.read())
+    return Json_data
+
+# Load the files
+file1 = load_doc(os.path.abspath(os.path.join(path, "ehealthforumQAs.json")))
+file2 = load_doc(os.path.abspath(os.path.join(path, "healthtapQAs.json")))
+file3 = load_doc(os.path.abspath(os.path.join(path, "icliniqQAs.json")))
+file4 = load_doc(os.path.abspath(os.path.join(path, "questionDoctorQAs.json")))
+file5 = load_doc(os.path.abspath(os.path.join(path, "webmdQAs.json")))
+file6 = load_doc(os.path.abspath(os.path.join(path, "medical_intent.json")))
+
+# Select the files to be used for training and concatenate them
+all_Files = [file1, file3, file4, file6]
+
+words = []
+labels = []
+documents = []
+ignore_words = ['?', '!']
+
+for data in all_Files:
+    for intent in data:
+        if len(intent['tags']) == 0:
+            tag = "unspecified"
+        else:     
+            ##Extracting only the first tags as they're the most relevant
+            tag = intent['tags'][0]
+            question = intent["question"]
+            wrds = nltk.word_tokenize(question)
+    
+            words.extend(wrds)
+            documents.append((wrds, tag))
+            
+            if tag not in labels:
+                labels.append(tag)
+
+words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
+words = sorted(list(set(words)))
+
+labels = sorted(list(set(labels)))
+
+print (len(documents), "documents")
+
+print (len(labels), "labels", labels)
+
+print (len(words), "unique lemmatized words", words)
+pickle.dump(words, open('words.pkl','wb'))
+pickle.dump(labels, open('labels.pkl','wb'))
+
+training = []
+out_empty = [0 for _ in range(len(labels))]
+
+for doc in documents:
+    bag = []
+    
+    pattern_words = doc[0]
+    pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]
+
+    for w in words:
+        bag.append(1) if w in pattern_words else bag.append(0)
+        
+
+    output_row = out_empty[:]
+    output_row[labels.index(doc[1])] = 1
+
+    training.append([bag, output_row])
+
+random.shuffle(training)
+training = np.array(training)
+
+
+train_x = list(training[:,0])
+train_y = list(training[:,1])
+print("Training data created")
+
+
+model = Sequential()
+model.add(Dense(64, input_shape=(len(train_x[0]),), activation='relu'))
+model.add(Dense(128, activation='relu'))
+model.add(Dropout(0.2))
+model.add(Dense(64, activation='relu'))
+model.add(Dropout(0.2))
+model.add(Dense(len(train_y[0]), activation='softmax'))
+
+model.compile(loss='categorical_crossentropy', optimizer="adam", metrics=['accuracy'])
+model.summary()
+
+hist = model.fit(np.array(train_x), np.array(train_y), epochs=100, batch_size=5, verbose=1)
+
+model.save('chatbot_model.hdf5')
+
+
+from tensorflow.keras.models import load_model
+
+model = load_model("chatbot_model.hdf5")
+
+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
+
+
+# In[26]:
+
+
+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))
+
+
+def classify_local(sentence):
+    ERROR_THRESHOLD = 0.25
+    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((labels[r[0]], str(r[1])))
+
+    return return_list