app.py

import streamlit as st
from transformers import pipeline

# Set up Streamlit UI
st.set_page_config(page_title="Text Prompting Demo", layout="centered")
st.title("🤖 Text Prompting using Transformers")

# Add model selector
task_option = st.selectbox(
    "Select Task",
    ("Text Generation", "Text Classification", "Question Answering")
)

# User input
user_input = st.text_area("Enter your input text", height=150)

# Run pipeline based on task
if st.button("Generate Output"):
    if not user_input.strip():
        st.warning("Please enter some text input.")
    else:
        if task_option == "Text Generation":
            generator = pipeline("text-generation", model="gpt2")
            output = generator(user_input, max_length=50, num_return_sequences=1)
            st.subheader("Generated Text")
            st.write(output[0]['generated_text'])

        elif task_option == "Text Classification":
            classifier = pipeline("sentiment-analysis")
            output = classifier(user_input)
            st.subheader("Classification Result")
            st.json(output)

        elif task_option == "Question Answering":
            context = st.text_area("Enter context for question answering", height=150)
            if not context.strip():
                st.warning("Please provide context.")
            else:
                qa = pipeline("question-answering")
                result = qa(question=user_input, context=context)
                st.subheader("Answer")
                st.write(result['answer'])