Add training script for chatbot

MedicalBot/training-bot/train_chatbot.py

@@ -18,3 +18,60 @@
 
 with open('intents.json') as json_file:
     intents = json.load(json_file)
+
+
+words=[]
+classes=[]
+documents=[]
+ignore_letters=['?','!','.',',']
+
+for intent in intents['intents']:
+  for pattern in intent['patterns']:
+    word_list=nltk.word_tokenize(pattern)
+    words.extend(word_list)
+    documents.append((word_list,intent['tag']))
+    if intent['tag'] not in classes:
+      classes.append(intent['tag'])
+
+
+words =[lemmatizer.lemmatize(word) for word in words if word not in ignore_letters]
+words = sorted(set(words))
+classes=sorted(set(classes))
+pickle.dump(words,open('words.pkl','wb'))
+pickle.dump(classes,open('classes.pkl','wb'))
+
+
+
+training=[]
+output_empty=[0]*len(classes)
+
+for document in documents:
+  bag=[]
+  word_patterns=document[0]
+  words = [lemmatizer.lemmatize(word) for word in words if word and word not in ignore_letters]
+  for word in words:
+    bag.append(1) if word in word_patterns else bag.append(0)
+
+  output_row=list(output_empty)
+  output_row[classes.index(document[1])]=1
+  training.append([bag,output_row])
+
+random.shuffle(training)
+training=np.array(training)
+
+train_x=list(training[:,0])
+train_y=list(training[:,1])
+model=Sequential()
+model.add(Dense(128,input_shape=(len(train_x[0]),),activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(64,activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(len(train_y[0]),activation='softmax'))
+
+sgd=SGD(lr=0.01,decay=1e-6,momentum=0.9,nesterov=True)
+model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])
+hist = model.fit(np.array(train_x),np.array(train_y),epochs=200,batch_size=5,verbose=1)
+model.save('chatbotmodel.h5', hist)
+print('Training Done')
+
+