app.py

import streamlit as st
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
import torch

# Load the saved model and tokenizer
@st.cache_resource
def load_model():
    model_path = "bert_qa_model"
    model = AutoModelForQuestionAnswering.from_pretrained(model_path)
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    return model, tokenizer

model, tokenizer = load_model()

def answer_question(context, question):
    # Tokenize input
    inputs = tokenizer(question, context, return_tensors="pt")
    
    # Get model output
    with torch.no_grad():
        outputs = model(**inputs)
    
    # Get start and end logits
    answer_start = torch.argmax(outputs.start_logits)
    answer_end = torch.argmax(outputs.end_logits) + 1
    
    # Decode the answer
    answer = tokenizer.decode(inputs["input_ids"][0][answer_start:answer_end])
    
    return answer

# Streamlit app
st.title("Question Answering System")

st.write("Enter a context and a question, and the model will provide an answer based on the context.")

context = st.text_area("Context", height=200)
question = st.text_input("Question")

if st.button("Get Answer"):
    if context and question:
        answer = answer_question(context, question)
        st.success(f"Answer: {answer}")
    else:
        st.error("Please provide both context and question.")

st.markdown("---")
st.write("Powered by Hugging Face Transformers and Streamlit")