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Create app_bu.py
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app_bu.py
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import os
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#os.system('pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.9/index.html')
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import transformers
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import streamlit as st
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from transformers import AutoTokenizer, AutoModelWithLMHead
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from transformers import pipeline
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sentiment_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-imdb-sentiment")
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def load_text_gen_model():
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generator = pipeline("text-generation", model="gpt2-medium")
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return generator
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@st.cache
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def get_sentiment_model():
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sentiment_model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-imdb-sentiment")
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return sentiment_model
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def get_summarizer_model():
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summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
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return summarizer
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def get_sentiment(text):
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input_ids = sentiment_tokenizer .encode(text + '</s>', return_tensors='pt')
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output = sentiment_extractor.generate(input_ids=input_ids,max_length=2)
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dec = [sentiment_tokenizer.decode(ids) for ids in output]
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label = dec[0]
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return label
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def get_qa_model():
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model_name = "deepset/roberta-base-squad2"
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qa_pipeline = pipeline('question-answering', model=model_name, tokenizer=model_name)
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return qa_pipeline
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sentiment_extractor = get_sentiment_model()
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summarizer = get_summarizer_model()
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answer_generator = get_qa_model()
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st.header("Review Analyzer")
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#action = st.sidebar.selectbox("Pick an Action", ["Analyse a Review","Generate an Article","Create an Image"])
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#if action == "Analyse a Review":
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st.subheader("Paste/write a review here..")
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review = st.text_area("")
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if review:
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start_sentiment_analysis = st.button("Get the Sentiment of the Review")
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start_summarizing = st.button("Summarize the review")
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start_topic_extraction = st.button("Find the key topic")
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if start_sentiment_analysis:
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sentiment = get_sentiment(review)
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st.write(sentiment)
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if start_summarizing:
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summary = summarizer(review, max_length=130, min_length=30, do_sample=False)
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st.write(summary)
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if start_topic_extraction:
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QA_input = {'question': 'what is the review about?',
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'context': review}
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answer = answer_generator(QA_input)
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st.write(answer)
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