app.py

import gradio as gr
import random
import os
import copy
import torch
from huggingface_hub import login
from transformers import pipeline
from transformers import GPT2Tokenizer, GPT2LMHeadModel,set_seed
from transformers import AutoTokenizer, AutoModelWithLMHead,AutoModelForSeq2SeqLM
import datetime
import nltk
nltk.download('stopwords')
nltk.download('punctuation')
nltk.download('punkt')
from rake_nltk import Rake

login(os.environ["HF_TOKEN"])


#https://huggingface.co/facebook/opt-1.3b
#generator = pipeline('text-generation', model="microsoft/DialoGPT-medium")

# dt stores the current date and time
dt = datetime.datetime.now()
print(dt)
print("loading models")
tokenizer = GPT2Tokenizer.from_pretrained('microsoft/DialoGPT-medium')
original_model = GPT2LMHeadModel.from_pretrained('microsoft/DialoGPT-medium')
untethered_model = GPT2LMHeadModel.from_pretrained('zmbfeng/untethered_20240225_epochs_500')
question_generation_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
question_generation_model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
paraphrase_tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws")
paraphrase_model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws")

# tokenizer = GPT2Tokenizer.from_pretrained('microsoft/DialoGPT-medium',cache_dir="G:\My Drive\Avatar\language_models_windows")
# original_model = GPT2LMHeadModel.from_pretrained('microsoft/DialoGPT-medium',cache_dir="G:\My Drive\Avatar\language_models_windows")
# untethered_model = GPT2LMHeadModel.from_pretrained('zmbfeng/untethered_20240225_epochs_500',cache_dir="G:\My Drive\Avatar\language_models_windows")
# question_generation_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap",cache_dir="G:\\My Drive\\Avatar\\language_models_windows")
# question_generation_model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap",cache_dir="G:\\My Drive\\Avatar\\language_models_windows")
# paraphrase_tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws",cache_dir="G:\\My Drive\\Avatar\\language_models_windows")
# paraphrase_model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws",cache_dir="G:\\My Drive\\Avatar\\language_models_windows")

# tokenizer = GPT2Tokenizer.from_pretrained('microsoft/DialoGPT-medium',cache_dir="C:\\Users\\zmbfeng\\Google Drive\\language_models_windows")
# original_model = GPT2LMHeadModel.from_pretrained('microsoft/DialoGPT-medium',cache_dir="C:\\Users\\zmbfeng\\Google Drive\\Avatar\\language_models_windows")
# untethered_model = GPT2LMHeadModel.from_pretrained('zmbfeng/untethered_20240225_epochs_500',cache_dir="C:\\Users\\zmbfeng\\Google Drive\\Avatar\\language_models_windows")
# question_generation_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap",cache_dir="C:\\Users\\zmbfeng\\Google Drive\\Avatar\\language_models_windows")
# question_generation_model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap",cache_dir="C:\\Users\\zmbfeng\\Google Drive\\Avatar\\language_models_windows")
# paraphrase_tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws",cache_dir="C:\\Users\\zmbfeng\\Google Drive\\Avatar\\language_models_windows")
# paraphrase_model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws",cache_dir="C:\\Users\\zmbfeng\\Google Drive\\Avatar\\language_models_windows")
default_temperature=0.01
default_seed=43
def create_response_question_generation(input_str, max_length=64):
    input_text = "answer: %s  context: %s </s>" % (input_str, input_str)
    print(f"create question input_text={input_text}")
    features = question_generation_tokenizer([input_text], return_tensors='pt')

    output = question_generation_model.generate(input_ids=features['input_ids'],
                                                attention_mask=features['attention_mask'],
                                                max_length=max_length)

    return question_generation_tokenizer.decode(output[0])


def create_response(input_str,
                             temperature,
                             seed,
                             model_name):
  print("input_str="+input_str)
  print("model_name="+str(model_name))
  print("temperature" + str(temperature))
  seed=int(seed)
  print("seed" + str(seed))
  input_ids = tokenizer.encode(input_str + tokenizer.eos_token, return_tensors="pt")


   #= encoded["input_ids"]
  #attention_mask = encoded["attention_mask"]

  if seed != -1:
    set_seed(seed)
  if model_name == "original_model":
    output = original_model.generate(input_ids, max_length=100, temperature=temperature, pad_token_id=tokenizer.eos_token_id)

  #elif model_name == "untethered_model":
  else:
    output = original_model.generate(input_ids, max_length=100, temperature=temperature, pad_token_id=tokenizer.eos_token_id )

  outputs = model_name+" generated <br>"
  sentence = tokenizer.decode(output[0], skip_special_tokens=True)
  outputs = outputs + sentence+  "<br/>"

  return outputs


common_examples_string="<br/>Sample Inputs:<br/>What is death?<br/>One of the best teachers in all of life turns out to be what?<br/>what is your most meaningful relationship?<br/>What actually gives life meaning?<br/>"

interface_original = gr.Interface(fn=create_response_question_generation,
    title="Question Generation",
    description="Enter a statment like Paris is the captial of France",
    inputs=[
    gr.Textbox(label="input text here", lines=3),
        gr.Number(
            label="max length",
                value=64),
    ],
    outputs="html"
    )



interface_untethered_model = gr.Interface(fn=create_response,
    title="untethered model",
    description="language model fine tuned with'The Untethered Soul' chapter 17"+common_examples_string,
    #examples=examples,
    inputs=[
   gr.Textbox(label="input text here", lines=3),
        gr.Number(
            label="temperature (decimal) controls the creativity or randomness of the output. A higher temperature" +
                  " (e.g., 1.6) results in more diverse and creative output, while a lower temperature (e.g., 0.02)" +
                  " makes the output more deterministic and focused",
            value=default_temperature),
        gr.Number(
            label="seed (integer) random seed, set to -1 to use a random seed everytime",
                value=default_seed),
        gr.Textbox(label="model", lines=3, value="untethered_model",visible=False)
    ],
    outputs="html"
    )



demo = gr.TabbedInterface([interface_original, interface_untethered_model], ["Original", "Untethered"])

demo.launch()