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# -*- coding: utf-8 -*-
"""Meena_A_Multilingual_Chatbot (1).ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1-IfUcnDUppyMArHonc_iesEcN2gSKU-j
"""
#!pip3 install transformers
#!pip install -q translate
#!pip install polyglot
#!pip install Pyicu
#!pip install Morfessor
#!pip install pycld2
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from translate import Translator
from polyglot.detect import Detector
# model_name = "microsoft/DialoGPT-large"
model_name = "microsoft/DialoGPT-large"
# model_name = "microsoft/DialoGPT-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# # chatting 5 times with nucleus sampling & tweaking temperature
# step=-1
# while(True):
# step+=1
# # take user input
# text = input(">> You:>")
# detected_language=Detector(text,quiet=True).language.code
# translator=Translator(from_lang=detected_language,to_lang="en")
# translated_input=translator.translate(text)
# print(translated_input)
# if text.lower().find("bye")!=-1:
# print(f">> Meena:> Bye Bye!")
# break;
# # encode the input and add end of string token
# input_ids = tokenizer.encode(translated_input+tokenizer.eos_token, return_tensors="pt")
# # concatenate new user input with chat history (if there is)
# bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
# # generate a bot response
# chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id,do_sample=True,top_p=0.9,top_k=50,temperature=0.7,num_beams=5,no_repeat_ngram_size=2)
# #print the output
# output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
# print(output)
# translator=Translator(from_lang="en",to_lang=detected_language)
# translated_output=translator.translate(output)
# print(f">> Meena:> {translated_output}")
#!pip install gradio
import gradio as gr
with gr.Blocks() as meena:
chatbot = gr.Chatbot(label="Meena- A Multilingual Chatbot")
msg = gr.Textbox(label="You")
clear = gr.Button("Clear")
def set(chat_history_ids1):
global chat_history_ids
chat_history_ids=chat_history_ids1
def get():
return chat_history_ids
def set2(step1):
global step
step=step1
def get2():
return step
def generate_text(text,chat_history):
step=-1
if len(chat_history)==0:
step=-1
else:
step=get2()
step+=1
set2(step)
print(step)
if step!=0:
chat_history_ids=get()
if text.isdigit():
detected_language='en'
else:
detected_language=Detector(text,quiet=True).language.code
translator=Translator(from_lang=detected_language,to_lang="en")
translated_input=translator.translate(text)
# encode the input and add end of string token
input_ids=tokenizer.encode(translated_input+tokenizer.eos_token,return_tensors="pt")
# concatenate new user input with chat history (if there is)
bot_input_ids=torch.cat([chat_history_ids,input_ids],dim=-1) if step>0 else input_ids
# generate a bot response
chat_history_ids=model.generate(bot_input_ids,max_length=1000,pad_token_id=tokenizer.eos_token_id,do_sample=True,top_p=0.9,top_k=50,temperature=0.7,num_beams=5,no_repeat_ngram_size=2)
print(chat_history_ids)
set(chat_history_ids)
#print the output
output=tokenizer.decode(chat_history_ids[:,bot_input_ids.shape[-1]:][0],skip_special_tokens=True)
translator=Translator(from_lang="en",to_lang=detected_language)
translated_output=translator.translate(output)
chat_history.append((text,translated_output))
if step==5:
set(-1)
set2(-1)
return "",chat_history
msg.submit(generate_text, [msg, chatbot], [msg, chatbot])
clear.click(lambda: None, None, chatbot, queue=False)
meena.queue().launch() |