Lazarina Stoy lazarinastoy

## pytrends-ultimateguide.ipynb

      
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## relatedqueries.ipynb

      
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                lazarinastoy
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              October 28, 2021 20:43
            
              
                relatedqueries.ipynb
              
          
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## streamlit_demo.py
import torch
import streamlit as st
from transformers import BartTokenizer, BartForConditionalGeneration
from transformers import T5Tokenizer, T5ForConditionalGeneration

st.title('Text Summarization Demo')
st.markdown('Using BART and T5 transformer model')

model = st.selectbox('Select the model', ('BART', 'T5'))

## article_generation.py
#Import streamlit library
import streamlit as st

st.title("Medium Article Generator") #set title of website
starting_sentence = st.text_input(label='Enter Starting Sentence') #set input and label

#Function to run model and generate new text
@st.cache
def generate():
    import gpt_2_simple as gpt2

## title_optimizer.py
import streamlit as st
from ludwig.api import LudwigModel
import pandas as pd

st.cache(show_spinner=False)
def load_model():
  #Update with the path to the Ludwig trained model
  model = LudwigModel.load("results/experiment_run_1/model/")
  return model
	import torch
	import streamlit as st
	from transformers import BartTokenizer, BartForConditionalGeneration
	from transformers import T5Tokenizer, T5ForConditionalGeneration

	st.title('Text Summarization Demo')
	st.markdown('Using BART and T5 transformer model')

	model = st.selectbox('Select the model', ('BART', 'T5'))
	#Import streamlit library
	import streamlit as st

	st.title("Medium Article Generator") #set title of website
	starting_sentence = st.text_input(label='Enter Starting Sentence') #set input and label

	#Function to run model and generate new text
	@st.cache
	def generate():
	import gpt_2_simple as gpt2
	import streamlit as st
	from ludwig.api import LudwigModel
	import pandas as pd

	st.cache(show_spinner=False)
	def load_model():
	#Update with the path to the Ludwig trained model
	model = LudwigModel.load("results/experiment_run_1/model/")
	return model