app.py

import streamlit as st
from transformers import pipeline, BartTokenizer, BartForConditionalGeneration

# Replace with your Hugging Face model repository path for QnA
model_repo_path_qna = 'ASaboor/Bart_Therapy'

# Load the model and tokenizer for QnA
model_qna = BartForConditionalGeneration.from_pretrained(model_repo_path_qna)
tokenizer_qna = BartTokenizer.from_pretrained(model_repo_path_qna)

# Initialize the QnA pipeline
qna_pipeline = pipeline('question-answering', model=model_qna, tokenizer=tokenizer_qna)

# Streamlit app layout
st.set_page_config(page_title="QnA App", page_icon=":memo:", layout="wide")

st.title("Question and Answer App")
st.write("""
    This app uses a fine-tuned BART model to answer questions based on the provided context.
    Enter the context and your question below, then click "Get Answer" to see the result.
""")

# User input for QnA
context_input = st.text_area("Enter context for QnA", height=300, placeholder="Paste your context here...")
question_input = st.text_input("Enter question", placeholder="Type your question here...")

# Generate the answer
if st.button("Get Answer"):
    if context_input and question_input:
        with st.spinner("Generating answer..."):
            try:
                # Generate answer
                answer = qna_pipeline({'context': context_input, 'question': question_input})
                
                # Display answer
                st.subheader("Answer")
                st.write(answer['answer'])
            except Exception as e:
                st.error(f"An error occurred during QnA: {e}")
    else:
        st.warning("Please enter both context and question for QnA.")