Create app.py

app.py

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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}")
+