import streamlit as st 
from transformers import pipeline

st.title("NLP pipeling")

def text_classificer():
    text_classification = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
    st.title("Text Classification")
    
    text=st.text_input("Enter the text :")
    if st.button("Classife"):
        output=text_classification(text)
        st.write(output[0]["label"])

def text_summarizer():
    text_summary = pipeline("summarization", model="facebook/bart-large-cnn")
    st.title("Text Summarizer")
    
    text=st.text_input("Enter the text")
    if st.button("summarised"):
        st.write(text_summary(text)[0]['summary_text'])
        
def text_generator():
    text_generat= pipeline("text-generation") 
    st.title("Text Generation")
    
    text=st.text_input("Enter the text")
    if st.button("generate"):
        result=text_generat(text)
        st.write(result[0]["generated_text"])
def name_enity():
    name_enity=pipeline("ner")#, model="dbmdz/bert-large-cased-finetuned-conll03-english", grouped_entities=True)
    st.title("Name Enity")
    
    text=st.text_input("Enter the text")
    if st.button("submit"):
        st.write(name_enity(text)[0]["word"])
    
def question_answer():
    question_answering = pipeline("question-answering", model="google-bert/bert-large-uncased-whole-word-masking-finetuned-squad")
    st.title("Question & Answers")
    content=st.text_input("Enter the Content")
    ques=st.text_input("Enter the Question ")
    if st.button("submit"):
        result=question_answering({"question": ques,"context": content})
        st.write(result["answer"])
        
def code_generator():
    st.title("Code Generator")
    code_generation = pipeline("text-generation", model="Salesforce/codegen-350M-mono")
    
    text=st.text_input("Enter the text")
    if st.button("submit"):
        st.write(code_generation(text)[0]) #["generated_text"]
    
        
file_type=st.sidebar.radio("Select a page:",('Text Classification',"Text Summarizer","Text Generator",'Name Enity','Question-Answer'))# Code Generator"

if file_type=='Text Classification':
    text_classificer()
elif file_type=="Text Summarizer":
    text_summarizer()
elif file_type=="Text Generator":
    text_generator()
elif file_type=='Name Enity':
    name_enity()
elif file_type=='Question-Answer':
    question_answer()
# elif file_type=="Code Generator":
#     code_generator()
else:
    st.write(file_type)