prahalya/Multi-Task-Analysis

1import streamlit as st2import transformers3from transformers import pipeline4 5# Load models6text_classification = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")7ques_ans = pipeline("question-answering", model="deepset/roberta-base-squad2")8summarization = pipeline("summarization", model="facebook/bart-large-cnn")9 10st.title("NLP Task Reading ")11 12# Task selector13task = st.radio("Select NLP Task", ("Sentiment Analysis", "Question & Answer", "Summarization"))14 15# Sentiment Analysis16if task == "Sentiment Analysis":17    st.subheader("Sentiment Analysis")18    user_text = st.text_input("Enter text:")19    if user_text:20        prediction = text_classification(user_text)[0]21        confidence_percentage = prediction["score"] * 10022        label = prediction["label"]23        statement = f"The model is {confidence_percentage:.2f}% confident that the sentiment is **{label}**."24        st.write(statement)25 26# Question Answering27elif task == "Question & Answer":28    st.subheader("Question & Answer")29    question = st.text_input("Question:")30    context = st.text_area("Context:")31    if question and context:32        result = ques_ans(question=question, context=context)33        answer = result["answer"]34        confidence = result["score"] * 10035        st.write(f"The answer is: **{answer}**")36        st.write(f"The model is {confidence:.2f}% confident in this answer.")37 38# Summarization39elif task == "Summarization":40    st.subheader("Summarization")41    text_to_summarize = st.text_area("Enter text to summarize:")42    if text_to_summarize:43        summary = summarization(text_to_summarize)[0]["summary_text"]44        st.write(f"**Summary:** {summary}")45 46