app.py

from transformers import AutoModelForQuestionAnswering, AutoModelForSeq2SeqLM, AutoTokenizer, PegasusForConditionalGeneration, PegasusTokenizer, pipeline
import gradio as grad
import ast

# mdl_name = "deepset/roberta-base-squad2"
# my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name)

# model_translate_name = 'danhsf/m2m100_418M-finetuned-kde4-en-to-pt_BR'
# model_translate = AutoModelForSeq2SeqLM.from_pretrained(model_translate_name)
# model_translate_token = AutoTokenizer.from_pretrained(model_translate_name)
# translate_pipeline = pipeline('translation', model=model_translate_name)

def answer_question(question,context):
    text= "{"+"'question': '"+question+"','context': '"+context+"'}"
    di=ast.literal_eval(text)
    response = my_pipeline(di)
    print('response', response)
    return response
#grad.Interface(answer_question, inputs=["text","text"], outputs="text").launch()


def translate(text):
    inputs = model_translate_token(text, return_tensor='pt')
    translate_output = model_translate.generate(**inputs)
    response = model_translate_token(translate_output[0], skip_special_tokens=True)
    #response = translate_pipeline(text)
    return response
# grad.Interface(translate, inputs=['text',], outputs='text').launch()


# mdl_name = "google/pegasus-xsum"
# pegasus_tkn = PegasusTokenizer.from_pretrained(mdl_name)
# mdl = PegasusForConditionalGeneration.from_pretrained(mdl_name)

def summarize(text):
    tokens = pegasus_tkn(text, truncation=True, padding="longest", return_tensors="pt")
    txt_summary = mdl.generate(**tokens, num_return_sequences=5, max_length=200, temperature=1.5,num_beams=10)
    response = pegasus_tkn.batch_decode(txt_summary, skip_special_tokens=True)
    return response
    
# txt=grad.Textbox(lines=10, label="English", placeholder="English Text here")
# out=grad.Textbox(lines=10, label="Summary")
# grad.Interface(summarize, inputs=txt, outputs=out).launch()

# ZeroShotClassification using pipeline
# from transformers import pipeline
# import gradio as grad
# zero_shot_classifier = pipeline("zero-shot-classification")
def classify(text,labels):
    classifer_labels = labels.split(",")
    #["software", "politics", "love", "movies", "emergency", "advertisment","sports"]
    response = zero_shot_classifier(text,classifer_labels)
    return response
# txt=grad.Textbox(lines=1, label="English", placeholder="text to be classified")
# labels=grad.Textbox(lines=1, label="Labels", placeholder="comma separated labels")
# out=grad.Textbox(lines=1, label="Classification")
# grad.Interface(classify, inputs=[txt,labels], outputs=out).launch()

# Text classification using BartForSequenceClassification
# from transformers import BartForSequenceClassification, BartTokenizer
# import gradio as grad
# bart_tkn = BartTokenizer.from_pretrained('facebook/bart-large-mnli')
# mdl = BartForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
def classify(text,label):
    tkn_ids = bart_tkn.encode(text, label, return_tensors='pt')
    tkn_lgts = mdl(tkn_ids)[0]
    entail_contra_tkn_lgts = tkn_lgts[:,[0,2]]
    probab = entail_contra_tkn_lgts.softmax(dim=1)
    response =  probab[:,1].item() * 100
    return response
# txt=grad.Textbox(lines=1, label="English", placeholder="text to be classified")
# labels=grad.Textbox(lines=1, label="Label", placeholder="Input a Label")
# out=grad.Textbox(lines=1, label="Probablity of label being true is")
# grad.Interface(classify, inputs=[txt,labels], outputs=out).launch()

# GPT2
from transformers import GPT2LMHeadModel,GPT2Tokenizer
import gradio as grad
mdl = GPT2LMHeadModel.from_pretrained('gpt2')
gpt2_tkn=GPT2Tokenizer.from_pretrained('gpt2')
def generate(starting_text):
    tkn_ids = gpt2_tkn.encode(starting_text, return_tensors = 'pt')
    gpt2_tensors = mdl.generate(tkn_ids,max_length=100,no_repeat_ngram_size=True,num_beams=3,do_sample=True,temperatue=0.1)
    # response = gpt2_tensors
    response=""
    for i, x in enumerate(gpt2_tensors):
       response=response+f"{i}: {gpt2_tkn.decode(x, skip_special_tokens=True)}"
    return response

txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
out=grad.Textbox(lines=1, label="Generated Tensors")
grad.Interface(generate, inputs=txt, outputs=out).launch()