import os 
#os.system('pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.9/index.html')

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)