Spaces:
Runtime error
Runtime error
File size: 2,192 Bytes
b58c818 1256a85 b58c818 ab5688d 9fec945 ab5688d cd3df30 ab5688d bd84a02 9fec945 0d3f45a bd84a02 0d3f45a e7f3263 bd84a02 87d90ac ce7b644 870d09a ce7b644 0d3f45a ab5688d b1dfc1e ebd8105 b1dfc1e 22f90c0 d2f7d16 22f90c0 ebd8105 22f90c0 62eea5b c50246f 22f90c0 ce7b644 c50246f 22f90c0 c50246f 22f90c0 4f75d26 ce7b644 4f75d26 2b90304 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
import transformers
import streamlit as st
from transformers import AutoTokenizer, AutoModelWithLMHead
from transformers import pipeline
sentiment_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-imdb-sentiment")
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_summarizer_model():
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
return summarizer
def get_sentiment(text):
input_ids = sentiment_tokenizer .encode(text + '</s>', return_tensors='pt')
output = sentiment_extractor.generate(input_ids=input_ids,max_length=2)
dec = [sentiment_tokenizer.decode(ids) for ids in output]
label = dec[0]
return label
def get_qa_model():
model_name = "deepset/roberta-base-squad2"
qa_pipeline = pipeline('question-answering', model=model_name, tokenizer=model_name)
return qa_pipeline
sentiment_extractor = get_sentiment_model()
summarizer = get_summarizer_model()
answer_generator = get_qa_model()
st.header("Review Analyzer")
#action = st.sidebar.selectbox("Pick an Action", ["Analyse a Review","Generate an Article","Create an Image"])
#if action == "Analyse a Review":
st.subheader("Paste/write a review here..")
review = st.text_area("")
if review:
start_sentiment_analysis = st.button("Get the Sentiment of the Review")
start_summarizing = st.button("Summarize the review")
start_topic_extraction = st.button("Find the key topic")
if start_sentiment_analysis:
sentiment = get_sentiment(review)
st.write(sentiment)
if start_summarizing:
summary = summarizer(review, max_length=130, min_length=30, do_sample=False)
st.write(summary)
if start_topic_extraction:
QA_input = {'question': 'what is the review about?',
'context': review}
answer = answer_generator(QA_input)
st.write(answer)
|