app.py

# -*- 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

def generate_text(text):
  step=-1
  while(True):
      step+=1
      detected_language=Detector(text,quiet=True).language.code
      translator=Translator(from_lang=detected_language,to_lang="en")
      translated_input=translator.translate(text)
      
      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)
      
      translator=Translator(from_lang="en",to_lang=detected_language)
      translated_output=translator.translate(output)
    
      return translated_output

output_text=gr.Textbox()
gr.Interface(generate_text,"textbox",output_text,title="Meena",
             description="Meena- A Multilingual Chatbot").launch(debug=False)

#!gradio deploy