app.py

# -*- coding: utf-8 -*-
"""app.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1Z_cMyllUfHf2lYtUtdS1ggVMpLCLg0-j
"""
import gradio as gr
###########  1  ###########


#intents.json --> nltk_utils.py -->  model.py --> train.ipynb --> chat.ipynb 
import numpy as np
import nltk
nltk.download('punkt')
from nltk.stem.porter import PorterStemmer
stemmer = PorterStemmer()

def tokenize(sentence):
    """
    split sentence into array of words/tokens
    a token can be a word or punctuation character, or number
    """
    return nltk.word_tokenize(sentence)

# print(tokenize('Hello how are you'))

def stem(word):
    """
    stemming = find the root form of the word
    examples:
    words = ["organize", "organizes", "organizing"]
    words = [stem(w) for w in words]
    -> ["organ", "organ", "organ"]
    """
    return stemmer.stem(word.lower())

# print(stem('organize'))

def bag_of_words(tokenized_sentence, words):
    """
    return bag of words array:
    1 for each known word that exists in the sentence, 0 otherwise
    example:
    sentence = ["hello", "how", "are", "you"]
    words = ["hi", "hello", "I", "you", "bye", "thank", "cool"]
    bog   = [  0 ,    1 ,    0 ,   1 ,    0 ,    0 ,      0]
    """
    # stem each word
    sentence_words = [stem(word) for word in tokenized_sentence]
    # initialize bag with 0 for each word
    bag = np.zeros(len(words), dtype=np.float32)
    for idx, w in enumerate(words):
        if w in sentence_words: 
            bag[idx] = 1

    return bag

# print(bag_of_words('Hello how are you', 'hi'))

###########  2  ###########

import torch
import torch.nn as nn


class NeuralNet(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(NeuralNet, self).__init__()
        self.l1 = nn.Linear(input_size, hidden_size) 
        self.l2 = nn.Linear(hidden_size, hidden_size) 
        self.l3 = nn.Linear(hidden_size, num_classes)
        self.relu = nn.ReLU()
    
    def forward(self, x):
        out = self.l1(x)
        out = self.relu(out)
        out = self.l2(out)
        out = self.relu(out)
        out = self.l3(out)
        # no activation and no softmax at the end
        return out

###########  3  ###########
import numpy as np
import random
import json

import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader

#2. Loading our JSON Data
#from google.colab import drive #commented
#drive.mount('/content/drive')  #commented

# Commented out IPython magic to ensure Python compatibility.
# %cd '/content/drive/My Drive/Colab Notebooks/NLP/ChatBot/'

#path = '/content/drive/My Drive/Colab Notebooks/NLP/ChatBot/intents.json'

#!pwd

import json
path = 'intents.json'
with open(path, 'r') as f:
    intents = json.load(f)

# print(intents)

# Commented out IPython magic to ensure Python compatibility.
# %cd '/content/drive/My Drive/Colab Notebooks/NLP/ChatBot/intents.json'

# Commented out IPython magic to ensure Python compatibility.
# %pwd

#!ls

import nltk
nltk.download('punkt')

#from nltk_utils import bag_of_words, tokenize, stem

all_words = []
tags = []
xy = []
# loop through each sentence in our intents patterns
for intent in intents['intents']:
    tag = intent['tag']
    # add to tag list
    tags.append(tag)
    for pattern in intent['patterns']:
        # tokenize each word in the sentence
        w = tokenize(pattern)
        # add to our words list
        all_words.extend(w)
        # add to xy pair
        xy.append((w, tag))

# stem and lower each word
# ignore_words = ['?', '.', '!']
ignore_words = ['(',')','-',':',',',"'s",'!',':',"'","''",'--','.',':','?',';''[',']','``','o','’','“','”','”','[',';']
all_words = [stem(w) for w in all_words if w not in ignore_words]
# remove duplicates and sort
all_words = sorted(set(all_words))
tags = sorted(set(tags))

#print(len(xy), "patterns") #commented
#print(len(tags), "tags:", tags) #commented
#print(len(all_words), "unique stemmed words:", all_words) #commented

# create training data
X_train = []
y_train = []
for (pattern_sentence, tag) in xy:
    # X: bag of words for each pattern_sentence
    bag = bag_of_words(pattern_sentence, all_words)
    X_train.append(bag)
    # y: PyTorch CrossEntropyLoss needs only class labels, not one-hot
    label = tags.index(tag)
    y_train.append(label)

X_train = np.array(X_train)
y_train = np.array(y_train)

# Hyper-parameters 
num_epochs = 1000
batch_size = 8
learning_rate = 0.001
input_size = len(X_train[0])
hidden_size = 8
output_size = len(tags)
#print(input_size, output_size) #commented

class ChatDataset(Dataset):

    def __init__(self):
        self.n_samples = len(X_train)
        self.x_data = X_train
        self.y_data = y_train

    # support indexing such that dataset[i] can be used to get i-th sample
    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]

    # we can call len(dataset) to return the size
    def __len__(self):
        return self.n_samples

import torch
import torch.nn as nn

#from model import NeuralNet

dataset = ChatDataset()
train_loader = DataLoader(dataset=dataset,batch_size=batch_size,shuffle=True,num_workers=2)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = NeuralNet(input_size, hidden_size, output_size).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# Train the model
for epoch in range(num_epochs):
    for (words, labels) in train_loader:
        words = words.to(device)
        labels = labels.to(dtype=torch.long).to(device)
        
        # Forward pass
        outputs = model(words)
        # if y would be one-hot, we must apply
        # labels = torch.max(labels, 1)[1]
        loss = criterion(outputs, labels)
        
        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
    #if (epoch+1) % 100 == 0:
        #print (f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')


#print(f'final loss: {loss.item():.4f}')#commented

data = {
"model_state": model.state_dict(),
"input_size": input_size,
"hidden_size": hidden_size,
"output_size": output_size,
"all_words": all_words,
"tags": tags
}

FILE = "data.pth"
torch.save(data, FILE)

#print(f'training complete. file saved to {FILE}') #commented


import random
import string # to process standard python strings

import warnings # Hide the warnings
warnings.filterwarnings('ignore')

import torch

import nltk
nltk.download('punkt')

#from google.colab import drive  #commented
#drive.mount("/content/drive")  #commented

# Commented out IPython magic to ensure Python compatibility.
# %cd "/content/drive/My Drive/Colab Notebooks/NLP/ChatBot/"
# !ls

import random
import json

import torch

#from model import NeuralNet
#from nltk_utils import bag_of_words, tokenize

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

with open('intents.json', 'r') as json_data:
    intents = json.load(json_data)

FILE = "data.pth"
data = torch.load(FILE, map_location=torch.device('cpu'))

input_size = data["input_size"]
hidden_size = data["hidden_size"]
output_size = data["output_size"]
all_words = data['all_words']
tags = data['tags']
model_state = data["model_state"]

model = NeuralNet(input_size, hidden_size, output_size).to(device)
model.load_state_dict(model_state)
model.eval()

bot_name = "WeASK"

###removed
from transformers import MBartForConditionalGeneration, MBart50Tokenizer

#def download_model():


#model, tokenizer = download_model()



################################
def download_model():
    model_name = "facebook/mbart-large-50-many-to-many-mmt"
    model = MBartForConditionalGeneration.from_pretrained(model_name)
    tokenizer = MBart50Tokenizer.from_pretrained(model_name)
    return model, tokenizer

model, tokenizer = download_model()

def get_response(input_text):
    model_inputs = tokenizer(input_text, return_tensors="pt")
    generated_tokens = model.generate(**model_inputs,forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
    translation= tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
    
    #string2=" ".join(map(str,translation ))




    #print("Let's chat! (type 'quit' to exit)")
    #while True:
    # sentence = "do you use credit cards?"
        #try:
            #sentence= input("You: ")
            #if sentence== "Quit":
                #break
        #except EOFError as e:
            #print(end="")
    #if sentence== "quit":
        #break

    sentence= tokenize(translation)
    X = bag_of_words(sentence, all_words)
    X = X.reshape(1, X.shape[0])
    X = torch.from_numpy(X).to(device)

    output = model(X)
    _, predicted = torch.max(output, dim=1)

    tag = tags[predicted.item()]

    probs = torch.softmax(output, dim=1)
    prob = probs[0][predicted.item()]
    if prob.item() > 0.75:
        for intent in intents['intents']:
            if tag == intent["tag"]:
                return random.choice(intent['responses'])
    else:
        return "I do not understand..."



#def get_chatbot(sentence):
  
  #return classifier(sentence)

title = "WeASK: ChatBOT"
description = "Ask your query here"
chatbot_demo = gr.Interface(fn=get_response, inputs = 'text',outputs='text',title = title, description = description)
chatbot_demo.launch()