app.py

import gradio as gr
import random
import torch
from transformers import AutoConfig, AutoTokenizer, AutoModelWithLMHead
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from itertools import chain

import os 

import tempfile
from typing import Optional
from TTS.config import load_config
import numpy as np
from TTS.utils.manage import ModelManager
from TTS.utils.synthesizer import Synthesizer

#emotion_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-emotion")
#emotion_model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-emotion")

def get_emotion(text):
  input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')
  output = model.generate(input_ids=input_ids,max_length=2)
  dec = [tokenizer.decode(ids) for ids in output]
  label = dec[0]
  return label.split()[1]

    
config = AutoConfig.from_pretrained('gorkemgoknar/gpt2chatbotenglish')
model = GPT2LMHeadModel.from_pretrained('gorkemgoknar/gpt2chatbotenglish', config=config)

tokenizer = GPT2Tokenizer.from_pretrained('gorkemgoknar/gpt2chatbotenglish')
tokenizer.model_max_length = 1024



#Dynamic Temperature 
#See experiment https://www.linkedin.com/pulse/ai-goes-job-interview-g%25C3%25B6rkem-g%25C3%25B6knar

base_temperature = 1.3
dynamic_temperature_range = 0.15

rand_range = random.uniform(-1 * dynamic_temperature_range , dynamic_temperature_range )
temperature = base_temperature  + rand_range

SPECIAL_TOKENS = ["<bos>", "<eos>", "<speaker1>", "<speaker2>", "<pad>"]

#See document for experiment https://www.linkedin.com/pulse/ai-goes-job-interview-g%C3%B6rkem-g%C3%B6knar/

def get_chat_response(name,history=[], input_txt = "Hello , what is your name?"):
  
  ai_history = history.copy()

  #ai_history.append(input_txt)
  ai_history_e = [tokenizer.encode(e) for e in ai_history]

  personality = "My name is " + name

  bos, eos, speaker1, speaker2 = tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS[:-1])
  
  #persona first, history next, input text must be at the end
  #[[bos, persona] , [history] , [input]]
  sequence = [[bos]  + tokenizer.encode(personality)] + ai_history_e  + [tokenizer.encode(input_txt)]
  ##[[bos, persona] , [speaker1 .., speakser2 .., speaker1 ... speaker2 ... , [input]]
  sequence = [sequence[0]] + [[speaker2 if (len(sequence)-i) % 2 else speaker1] + s for i, s in enumerate(sequence[1:])]
  
  sequence = list(chain(*sequence))

  #bot_input_ids = tokenizer.encode(personality + tokenizer.eos_token + input_txt + tokenizer.eos_token  , return_tensors='pt')
  sequence_len = len(sequence)

  #optimum response and speed
  chat_history_ids = model.generate(
          torch.tensor(sequence).unsqueeze(0), max_length=50,
          pad_token_id=tokenizer.eos_token_id,  
          no_repeat_ngram_size=3,       
          do_sample=True, 
          top_k=60, 
          top_p=0.8,
          temperature = 1.3
      )
  out_str = tokenizer.decode(chat_history_ids[0][sequence_len:],  skip_special_tokens=True)
  #out_str = tokenizer.decode(chat_history_ids[:, sequence.shape[-1]:][0], skip_special_tokens=False)
  return out_str
  
##you can use anyone from below
'''
| Macleod | Moran | Brenda | Ramirez | Peter Parker | Quentin Beck | Andy 
| Red | Norton | Willard | Chief | Chef | Kilgore | Kurtz | Westley | Buttercup 
| Vizzini | Fezzik | Inigo | Man In Black | Taylor | Zira | Zaius | Cornelius 
| Bud | Lindsey | Hippy | Erin | Ed | George | Donna | Trinity | Agent Smith 
| Morpheus | Neo | Tank | Meryl | Truman | Marlon | Christof | Stromboli | Bumstead 
| Schreber | Walker | Korben | Cornelius | Loc Rhod | Anakin | Obi-Wan | Palpatine 
| Padme | Superman | Luthor | Dude | Walter | Donny | Maude | General | Starkiller 
| Indiana | Willie | Short Round | John | Sarah | Terminator | Miller | Sarge | Reiben 
| Jackson | Upham | Chuckie | Will | Lambeau | Sean | Skylar | Saavik | Spock 
| Kirk | Bones | Khan | Kirk | Spock | Sybok | Scotty | Bourne | Pamela | Abbott 
| Nicky | Marshall | Korshunov | Troy | Vig | Archie Gates | Doc | Interrogator 
| Ellie | Ted | Peter | Drumlin | Joss | Macready | Childs | Nicholas | Conrad 
| Feingold | Christine | Adam | Barbara | Delia | Lydia | Cathy | Charles | Otho 
| Schaefer | Han | Luke | Leia | Threepio | Vader | Yoda | Lando | Elaine | Striker 
| Dr. Rumack | Kramer | David | Saavik | Kirk | Kruge | Holden | Deckard | Rachael 
| Batty | Sebastian | Sam | Frodo | Pippin | Gandalf | Kay | Edwards | Laurel 
| Edgar | Zed | Jay | Malloy | Plissken | Steve Rogers | Tony Stark | Scott Lang 
| Bruce Banner | Bruce | Edward | Two-Face | Batman | Chase | Alfred | Dick 
| Riddler | Din Djarin | Greef Karga | Kuiil | Ig-11 | Cara Dune | Peli Motto 
| Toro Calican | Ripley | Meredith | Dickie | Marge | Peter | Lambert | Kane 
| Dallas | Ripley | Ash | Parker | Threepio | Luke | Leia | Ben | Han | Common Bob 
| Common Alice | Jack | Tyler | Marla | Dana | Stantz | Venkman | Spengler | Louis 
| Fry | Johns | Riddick | Kirk | Decker | Spock | "Ilia | Indy | Belloq | Marion 
| Brother | Allnut | Rose | Qui-Gon | Jar Jar
'''

MODEL_NAME= "tts_models/multilingual/multi-dataset/your_tts"



def greet(character,your_voice,message,history):
  
  #gradios set_state/get_state had problems on embedded html!
  history = history or {"character": character, "message_history" : [] }
  #gradios set_state/get_state does not persist session for now using global
  #global history
  
  if history["character"] != character:
    #switching character
    history = {"character": character, "message_history" : [] }


  response = get_chat_response(character,history=history["message_history"],input_txt=message)
  os.system('tts --text "'+response+'" --model_name tts_models/multilingual/multi-dataset/your_tts --speaker_wav '+speaker_wav+' --language_idx "en"') 

  history["message_history"].append((message, response))

  #emotion = get_emotion(response)
  
  html = "<div class='chatbot'>"
  for user_msg, resp_msg in history["message_history"]:
      html += f"<div class='user_msg'>You: {user_msg}</div>"
      html += f"<div class='resp_msg'>{character}: {resp_msg}</div>"
  html += "</div>"

  return "tts_output.wav", html,history
  
  

personality_choices = ["Gandalf", "Riddick", "Macleod", "Morpheus", "Neo","Spock","Vader","Indy"]

examples= ["Gandalf", "What is your name?"]

css="""
    .chatbox {display:flex;flex-direction:column}
    .user_msg, .resp_msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%}
    .user_msg {background-color:cornflowerblue;color:white;align-self:start}
    .resp_msg {background-color:lightgray;align-self:self-end}
"""


#some selected ones are in for demo use
personality_choices = ["Gandalf", "Riddick", "Macleod", "Morpheus", "Neo","Spock","Vader","Indy", "Ig-11","Threepio","Tony Stark","Batman","Vizzini"]
title = "Metayazar - Movie Chatbot"
description = "Chat with your favorite movie characters. This space demo has simple interface and simple history as gradio's state did not work need to make a global history, you will have different responses as same gradio machine will be used! Test it out in metayazar.com/chatbot for more movie/character options and history memorized."
article = "<p style='text-align: center'><a href='https://www.linkedin.com/pulse/ai-goes-job-interview-g%C3%B6rkem-g%C3%B6knar/' target='_blank'>AI Goes to Job Interview</a> | <a href='https://www.metayazar.com/' target='_blank'>Metayazar AI Writer</a>  |<a href='https://www.linkedin.com/in/goknar/' target='_blank'>Görkem Göknar</a></p>"

#History not implemented in this demo, use metayazar.com/chatbot for a movie and character dropdown chat interface
##interface = gr.Interface(fn=greet, inputs=[gr.inputs.Dropdown(personality_choices) ,"text"], title=title, description=description, outputs="text")

examples=[['Gandalf','Hello','dragon.wav']]

history =   {"character": "None", "message_history" : [] }
interface= gr.Interface(fn=greet, 
                        inputs=[gr.inputs.Dropdown(personality_choices),gr.inputs.Audio(source="microphone", type="filepath") ,"text", "state"], 
                        outputs=[gr.outputs.Audio(type="file"),"html","state"],      
                        css=css, title=title, description=description,article=article )
      
      
if __name__ == "__main__":
    interface.launch()