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import streamlit as st
import transformers
from transformers import pipeline
# Load models
text_classification = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
ques_ans = pipeline("question-answering", model="deepset/roberta-base-squad2")
summarization = pipeline("summarization", model="facebook/bart-large-cnn")
st.title("NLP Task Reading ")
# Task selector
task = st.radio("Select NLP Task", ("Sentiment Analysis", "Question & Answer", "Summarization"))
# Sentiment Analysis
if task == "Sentiment Analysis":
st.subheader("Sentiment Analysis")
user_text = st.text_input("Enter text:")
if user_text:
prediction = text_classification(user_text)[0]
confidence_percentage = prediction["score"] * 100
label = prediction["label"]
statement = f"The model is {confidence_percentage:.2f}% confident that the sentiment is **{label}**."
st.write(statement)
# Question Answering
elif task == "Question & Answer":
st.subheader("Question & Answer")
question = st.text_input("Question:")
context = st.text_area("Context:")
if question and context:
result = ques_ans(question=question, context=context)
answer = result["answer"]
confidence = result["score"] * 100
st.write(f"The answer is: **{answer}**")
st.write(f"The model is {confidence:.2f}% confident in this answer.")
# Summarization
elif task == "Summarization":
st.subheader("Summarization")
text_to_summarize = st.text_area("Enter text to summarize:")
if text_to_summarize:
summary = summarization(text_to_summarize)[0]["summary_text"]
st.write(f"**Summary:** {summary}")
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