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import transformers
import streamlit as st
from transformers import AutoTokenizer, AutoModelWithLMHead
from transformers import pipeline
#tokenizer = AutoTokenizer.from_pretrained("gpt2-medium")
sentiment_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-imdb-sentiment")
@st.cache
def load_model(model_name):
model = AutoModelWithLMHead.from_pretrained(model_name)
return model
def load_text_gen_model():
generator = pipeline("text-generation", model="gpt2-medium")
return generator
@st.cache
def get_sentiment_model():
sentiment_model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-imdb-sentiment")
return sentiment_model
def get_sentiment(text):
input_ids = sentiment_tokenizer .encode(text + '</s>', return_tensors='pt')
output = sentiment_model.generate(input_ids=input_ids,max_length=2)
dec = [sentiment_tokenizer.decode(ids) for ids in output]
label = dec[0]
return label
@st.cache
def get_summarizer():
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
return summarizer
sentiment_model = get_sentiment_model()
summarizer = get_summarizer()
text_generator = load_text_gen_model()
action = st.sidebar.selectbox("Pick an Action", ["Analyse a Review","Generate an Article","Create an Image"])
if action == "Analyse a Review":
review = st.text_area("Paste the review here..")
if review:
#res = text_generator( prompt, max_length=100, temperature=0.7)
#st.write(res)
sentiment = get_sentiment(review)
st.write(sentiment)
if st.button("Summarize the review"):
summary = summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False)
st.write(summary)
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