diff --git a/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/.vscode/launch.json b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/.vscode/launch.json
new file mode 100644
index 000000000..17e15f27e
--- /dev/null
+++ b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/.vscode/launch.json
@@ -0,0 +1,15 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Current File",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal"
+        }
+    ]
+}
\ No newline at end of file
diff --git a/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/__pycache__/model.cpython-38.pyc b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/__pycache__/model.cpython-38.pyc
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diff --git a/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/__pycache__/nltk_utils.cpython-38.pyc b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/__pycache__/nltk_utils.cpython-38.pyc
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diff --git a/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/chat.py b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/chat.py
new file mode 100644
index 000000000..831bf15a7
--- /dev/null
+++ b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/chat.py
@@ -0,0 +1,64 @@
+import nltk
+from nltk.stem import WordNetLemmatizer
+lemmatizer = WordNetLemmatizer()
+import pickle
+import numpy as np
+
+from keras.models import load_model
+model = load_model('chatbot_model.h5')
+import json
+import random
+intents = json.loads(open('intents.json').read())
+words = pickle.load(open('words.pkl','rb'))
+classes = pickle.load(open('classes.pkl','rb'))
+
+
+def clean_up_sentence(sentence):
+    sentence_words = nltk.word_tokenize(sentence)
+    sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
+    return sentence_words
+
+# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence
+
+def bow(sentence, words, show_details=True):
+    # tokenize the pattern
+    sentence_words = clean_up_sentence(sentence)
+    # bag of words - matrix of N words, vocabulary matrix
+    bag = [0]*len(words)
+    for s in sentence_words:
+        for i,w in enumerate(words):
+            if w == s:
+                # assign 1 if current word is in the vocabulary position
+                bag[i] = 1
+                if show_details:
+                    print ("found in bag: %s" % w)
+    return(np.array(bag))
+
+def predict_class(sentence, model):
+    # filter out predictions below a threshold
+    p = bow(sentence, words,show_details=False)
+    res = model.predict(np.array([p]))[0]
+    ERROR_THRESHOLD = 0.25
+    results = [[i,r] for i,r in enumerate(res) if r>ERROR_THRESHOLD]
+    # sort by strength of probability
+    results.sort(key=lambda x: x[1], reverse=True)
+    return_list = []
+    for r in results:
+        return_list.append({"intent": classes[r[0]], "probability": str(r[1])})
+    return return_list
+
+def getResponse(ints, intents_json):
+    tag = ints[0]['intent']
+    list_of_intents = intents_json['intents']
+    for i in list_of_intents:
+        if(i['tag']== tag):
+            result = random.choice(i['responses'])
+            break
+    return result
+
+def chatbot_response(msg):
+    ints = predict_class(msg, model)
+    res = getResponse(ints, intents)
+    return res
+
+print(chatbot_response('do i suck at everything'))
\ No newline at end of file
diff --git a/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/chatbot_model.h5 b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/chatbot_model.h5
new file mode 100644
index 000000000..20367d59d
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diff --git a/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/classes.pkl b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/classes.pkl
new file mode 100644
index 000000000..2087a2ed9
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diff --git a/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/intents.json b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/intents.json
new file mode 100644
index 000000000..143a47c18
--- /dev/null
+++ b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/intents.json
@@ -0,0 +1,79 @@
+{"intents": [
+        {"tag": "greeting",
+         "patterns": ["Hi there", "How are you", "Is anyone there?","Hey","Hola", "Hello", "Good day"],
+         "responses": ["Hello, thanks for asking", "Good to see you again", "Hi there, how can I help?"],
+         "context": [""]
+        },
+        {"tag": "goodbye",
+         "patterns": ["Bye", "See you later", "Goodbye", "Nice chatting to you, bye", "Till next time"],
+         "responses": ["See you!", "Have a nice day", "Bye! Come back again soon."],
+         "context": [""]
+        },
+        {"tag": "thanks",
+         "patterns": ["Thanks", "Thank you", "That's helpful", "Awesome, thanks", "Thanks for helping me"],
+         "responses": ["Happy to help!", "Anytime!", "My pleasure"],
+         "context": [""]
+        },
+        {"tag": "noanswer",
+         "patterns": [],
+         "responses": ["Sorry, can't understand you", "Please give me more info", "Not sure I understand"],
+         "context": [""]
+        },
+        {"tag": "options",
+         "patterns": ["How you could help me?", "What you can do?", "What help you provide?", "How you can be helpful?", "What support is offered"],
+         "responses": ["I can guide you through Adverse drug reaction list, Blood pressure tracking, Hospitals and Pharmacies", "Offering support for Adverse drug reaction, Blood pressure, Hospitals and Pharmacies"],
+         "context": [""]
+        },
+        {"tag": "adverse_drug",
+         "patterns": ["How to check Adverse drug reaction?", "Open adverse drugs module", "Give me a list of drugs causing adverse behavior", "List all drugs suitable for patient with adverse reaction", "Which drugs dont have adverse reaction?" ],
+         "responses": ["Navigating to Adverse drug reaction module"],
+         "context": [""]
+        },
+        {"tag": "blood_pressure",
+         "patterns": ["Open blood pressure module", "Task related to blood pressure", "Blood pressure data entry", "I want to log blood pressure results", "Blood pressure data management" ],
+         "responses": ["Navigating to Blood Pressure module"],
+         "context": [""]
+        },
+        {"tag": "blood_pressure_search",
+         "patterns": ["I want to search for blood pressure result history", "Blood pressure for patient", "Load patient blood pressure result", "Show blood pressure results for patient", "Find blood pressure results by ID" ],
+         "responses": ["Please provide Patient ID", "Patient ID?"],
+         "context": ["search_blood_pressure_by_patient_id"]
+        },
+        {"tag": "search_blood_pressure_by_patient_id",
+         "patterns": [],
+         "responses": ["Loading Blood pressure result for Patient"],
+         "context": [""]
+        },
+        {"tag": "pharmacy_search",
+         "patterns": ["Find me a pharmacy", "Find pharmacy", "List of pharmacies nearby", "Locate pharmacy", "Search pharmacy" ],
+         "responses": ["Please provide pharmacy name"],
+         "context": ["search_pharmacy_by_name"]
+        },
+        {"tag": "search_pharmacy_by_name",
+         "patterns": [],
+         "responses": ["Loading pharmacy details"],
+         "context": [""]
+        },
+        {"tag": "hospital_search",
+         "patterns": ["Lookup for hospital", "Searching for hospital to transfer patient", "I want to search hospital data", "Hospital lookup for patient", "Looking up hospital details" ],
+         "responses": ["Please provide hospital name or location"],
+         "context": ["search_hospital_by_params"]
+        },
+        {"tag": "search_hospital_by_params",
+         "patterns": [],
+         "responses": ["Please provide hospital type"],
+         "context": ["search_hospital_by_type"]
+        },
+        {"tag": "search_hospital_by_type",
+         "patterns": [],
+         "responses": ["Loading hospital details"],
+         "context": [""]
+        },
+        {
+        "tag" : "Anxiety",
+        "patterns":["I am feeling left out","I fucking suck at everything.","I dont have any confidence left in myself anymore","Why i dont get what i want"],
+        "responses":["Hey! I am here to listen you out. Please feel free to share everything with me. Everything is gonna be great in you life from now onwards! :)","No, You dont suck at everything. I believe in you! :)","I understand. Everything will be perfectly fine. Just recollect memories where you felt proud of yourselves. :)","Because god thinks that this is not right for you!! The more you will think about it the more you will lose yourself, hence Calm down and take a deep breathe. Everything is gonna be Nice soon!"],
+         "context":[""]
+        }
+   ]
+}
diff --git a/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/model.py b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/model.py
new file mode 100644
index 000000000..8c9fffb51
--- /dev/null
+++ b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/model.py
@@ -0,0 +1,19 @@
+import torch
+import torch.nn as nn
+
+class NeuralNetModels(nn.Module) :
+    def __init__(self, input_size,hidden_size,num_classes):
+        super(NeuralNetModels,self).__init__()
+        self.l1 = nn.Linear(input_size,hidden_size)   #1st Layer with input size and output as hidden size
+        self.l2 = nn.Linear(hidden_size,hidden_size)  #2nd layer with hidden size as input and hidden size as output
+        self.l3 = nn.Linear(hidden_size,num_classes)  #3rd layer with hidden size as input and num of classes as output
+        self.relu = nn.ReLU()                         #Activation 
+
+    def forward(self, x):
+        out = self.l1(x)
+        out = self.relu(out)
+        out = self.l2(out)
+        out = self.relu(out)
+        out = self.l3(out)
+        out = self.relu(out)
+        return out
\ No newline at end of file
diff --git a/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/nltk_utils.py b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/nltk_utils.py
new file mode 100644
index 000000000..f9323048a
--- /dev/null
+++ b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/nltk_utils.py
@@ -0,0 +1,42 @@
+'''
+Preprocessing strategy : 
+1. Tokenization
+2. Lowering and stem
+3. Punctuation Removal
+4. Bag Of Words
+'''
+import enum
+import numpy as np
+import nltk
+
+#nltk.download('punkt')
+from nltk.stem.porter import PorterStemmer
+
+stemmer=PorterStemmer()
+def tokenize(sentence):
+        return nltk.word_tokenize(sentence)   #Tokenization happens here. for e,g, I am girl tokenized to I, am , girl
+
+
+def stem(word):
+    return stemmer.stem(word.lower())
+
+def bag_of_words(tokenized_sentence,all_words):
+    '''
+    tokenized_Sentence=['I','am','saylee']
+    words=['I','am','saylee','hello','code']
+    bag=[1,1,1,0,0]
+
+    '''
+    tokenized_sentence=[stem(w) for w in tokenized_sentence]
+    bag = np.zeros(len(all_words),dtype=np.float32)
+    for index,w in enumerate(all_words):
+        if w in tokenized_sentence:
+            bag[index]=1.0
+
+
+    return bag
+
+tokenized_Sentence=["I","am","Saylee","you"]
+words=["hi","hello","am","I","Saylee","thank","cool"]
+bag=bag_of_words(tokenized_Sentence,words)
+print(bag)
\ No newline at end of file
diff --git a/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/train.py b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/train.py
new file mode 100644
index 000000000..dd5393f21
--- /dev/null
+++ b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/train.py
@@ -0,0 +1,103 @@
+import json
+
+from torch._C import device
+from torch.nn.modules import loss
+from nltk_utils import stem,bag_of_words,tokenize
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+from model import NeuralNetModels
+
+
+
+with open('intents.json','r') as f:
+    intents=json.load(f)
+
+#print(intents)
+all_words=[]
+tags=[]
+tag_pattern_words=[]
+
+for intent in intents['intents']:
+    tag=intent['tag']
+    tags.append(tag)
+    for pattern in intent['patterns']:
+        w=tokenize(pattern)
+        all_words.extend(w)  #didnt use append cuz w is array itself and we dont want array of array in all_words
+        tag_pattern_words.append((w,tag))
+
+ignore_words=['?,','.',',','!']
+all_words=[stem(w) for w in all_words if w not in ignore_words]
+all_words=sorted(set(all_words))  #using set to remove duplicate words
+tags=sorted(set(tags))
+#print(tags)
+
+X_train=[]
+Y_train=[]
+
+for pattern,tag in tag_pattern_words:
+    bag=bag_of_words(pattern,all_words)
+    X_train.append(bag)
+
+    label=tags.index(tag)
+    Y_train.append(label)  #CrossEntropy Loss
+
+X_train=np.array(X_train)
+Y_train=np.array(Y_train)
+
+class ChatDataset(Dataset):
+    def __init__(self):
+        self.n_samples = len(X_train)
+        self.x_data = X_train
+        self.y_data = Y_train
+    
+    def __getitem__(self, idx) :
+        return self.x_data[idx], self.y_data[idx]
+    
+    def __len__(self):
+        return self.n_samples
+    
+#Hyperparameters
+batch_size = 32
+hidden_size = 8
+output_size = len(tags)
+input_size = len(X_train[0])   #or you can say len(all_words)
+learning_rate = 0.001
+num_epochs = 100000
+#print(input_size,len(all_words))
+#print(output_size,tags)
+
+
+dataset = ChatDataset()
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+train_loader = DataLoader(dataset=dataset,batch_size=batch_size,shuffle=True, num_workers=0)
+model = NeuralNetModels(input_size,hidden_size,output_size).to(device)
+#loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate)
+
+
+
+
+for epoch in range(num_epochs) :
+    for (words, labels)in train_loader:
+        words = words.to(device)
+        
+        labels = labels.to(dtype=torch.long)
+        labels = labels.to(device)
+        #call fwd pass
+        outputs = model(words)
+        loss = criterion(outputs, labels)
+
+        #backward and optimizer step
+
+        optimizer.zero_grad()
+        loss.backward()  #to call backward pro
+        optimizer.step()
+
+    if (epoch+1)%100 == 0:
+        print(f'epoch {epoch+1}/{num_epochs},loss={loss.item():.4f}')
+
+
+print(f'final loss, loss={loss.item():.4f}')
diff --git a/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/train_keras.py b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/train_keras.py
new file mode 100644
index 000000000..70d2a067b
--- /dev/null
+++ b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/train_keras.py
@@ -0,0 +1,103 @@
+
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense, Activation, Dropout
+from keras.optimizers import SGD
+import random
+
+import nltk
+nltk.download('punkt')
+nltk.download('wordnet')
+from nltk.stem import WordNetLemmatizer
+lemmatizer = WordNetLemmatizer()
+import json
+import pickle
+
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense, Activation, Dropout
+from keras.optimizers import SGD
+import random
+
+words=[]
+classes = []
+documents = []
+ignore_words = ['.',',','?', '!']
+data_file = open('intents.json').read()
+intents = json.loads(data_file)
+
+
+for intent in intents['intents']:
+    for pattern in intent['patterns']:
+
+        # take each word and tokenize it
+        w = nltk.word_tokenize(pattern)
+        words.extend(w)
+        # adding documents
+        documents.append((w, intent['tag']))
+
+        # adding classes to our class list
+        if intent['tag'] not in classes:
+            classes.append(intent['tag'])
+
+words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
+words = sorted(list(set(words)))
+
+classes = sorted(list(set(classes)))
+
+print (len(documents), "documents")
+
+print (len(classes), "classes", classes)
+
+print (len(words), "unique lemmatized words", words)
+
+
+pickle.dump(words,open('words.pkl','wb'))
+pickle.dump(classes,open('classes.pkl','wb'))
+
+# initializing training data
+training = []
+output_empty = [0] * len(classes)
+for doc in documents:
+    # initializing bag of words
+    bag = []
+    # list of tokenized words for the pattern
+    pattern_words = doc[0]
+    # lemmatize each word - create base word, in attempt to represent related words
+    pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]
+    # create our bag of words array with 1, if word match found in current pattern
+    for w in words:
+        bag.append(1) if w in pattern_words else bag.append(0)
+
+    # output is a '0' for each tag and '1' for current tag (for each pattern)
+    output_row = list(output_empty)
+    output_row[classes.index(doc[1])] = 1
+
+    training.append([bag, output_row])
+# shuffle our features and turn into np.array
+#random.shuffle(training)
+training = np.array(training)
+# create train and test lists. X - patterns, Y - intents
+train_x = list(training[:,0])
+train_y = list(training[:,1])
+print("Training data created")
+
+
+# Create model - 3 layers. First layer 128 neurons, second layer 64 neurons and 3rd output layer contains number of neurons
+# equal to number of intents to predict output intent with softmax
+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'))
+
+# Compile model. Stochastic gradient descent with Nesterov accelerated gradient gives good results for this model
+sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
+model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
+
+#fitting and saving the model
+hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
+model.save('chatbot_model.h5', hist)
+
+print("model created")
diff --git a/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/words.pkl b/Medical_Assistance_Chatbot/Chatbot-main/Chatbot-main/words.pkl
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index 000000000..8b57a9b5d
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