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(allow_output_mutation=True) 
def get_summarizer():
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
    return summarizer

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_model   = get_sentiment_model()
summarizer = get_summarizer()
answer_geerator = get_qa_model()

#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)
        if st.button("Get the Sentiment of the Review"):
            sentiment = get_sentiment(review)
            st.write(sentiment)
            
        if st.button("Summarize the review"):
            summary = summarizer(review, max_length=130, min_length=30, do_sample=False)
            st.write(summary)
        
        if st.button("Find the key topic"):
            QA_input = {'question': 'what is the review about?',
                        'context': review}
            answer = answer_geerator (QA_input)
            st.write(answer)