# from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline # import gradio as grad # import ast # # mdl_name = "deepset/roberta-base-squad2" # mdl_name = "distilbert-base-cased-distilled-squad" # my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name) # def answer_question(question,context): # text= "{"+"'question': '"+question+"','context': '"+context+"'}" # di=ast.literal_eval(text) # response = my_pipeline(di) # return response # grad.Interface(answer_question, inputs=["text","text"], outputs="text").launch() # from transformers import pipeline # import gradio as grad # mdl_name = "Helsinki-NLP/opus-mt-en-zh" # opus_translator = pipeline("translation", model=mdl_name) # def translate(text): # response = opus_translator(text) # return response # grad.Interface(translate, inputs=["text",], outputs="text").launch() # from transformers import pipeline # import gradio as grad # mdl_name = "Helsinki-NLP/opus-mt-en-zh" # opus_translator = pipeline("translation", model=mdl_name) # def translate(text): # response = opus_translator(text) # return response # txt=grad.Textbox(lines=1, label="English", placeholder="English Text here") # out=grad.Textbox(lines=1, label="Chinese") # grad.Interface(translate, inputs=txt, outputs=out).launch() ################################5-6 # from transformers import AutoModel,AutoTokenizer,AutoModelForSeq2SeqLM # import gradio as grad # mdl_name = "Helsinki-NLP/opus-mt-en-fr" # mdl = AutoModelForSeq2SeqLM.from_pretrained(mdl_name) # my_tkn = AutoTokenizer.from_pretrained(mdl_name) # #opus_translator = pipeline("translation", model=mdl_name) # def translate(text): # inputs = my_tkn(text, return_tensors="pt") # trans_output = mdl.generate(**inputs) # response = my_tkn.decode(trans_output[0], skip_special_tokens=True) # #response = opus_translator(text) # return response # txt=grad.Textbox(lines=1, label="English", placeholder="English Text here") # out=grad.Textbox(lines=1, label="French") # grad.Interface(translate, inputs=txt, outputs=out).launch() # from transformers import PegasusForConditionalGeneration, PegasusTokenizer # import gradio as grad # 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) # 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() # from transformers import PegasusForConditionalGeneration, PegasusTokenizer # import gradio as grad # 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") # translated_txt = mdl.generate(**tokens,num_return_sequences=5,max_length=200,temperature=1.5,num_beams=10) # response = pegasus_tkn.batch_decode(translated_txt, 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() # 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() # 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() # 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) # response = gpt2_tensors # 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() 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) response="" #response = gpt2_tensors 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()