app.py

import streamlit as st
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
import pandas as pd

# Load the Question Answering model
@st.cache_resource
def load_qa_pipeline():
    """Load the QA pipeline with deepset/roberta-base-squad2 model."""
    model_name = "deepset/roberta-base-squad2"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForQuestionAnswering.from_pretrained(model_name)
    return pipeline("question-answering", model=model, tokenizer=tokenizer)

qa_pipeline = load_qa_pipeline()

# Load SOP Dataset
@st.cache_data
def load_sop_dataset():
    """Load SOP dataset from CSV."""
    return pd.read_csv("dataset.csv")  # Ensure this file is uploaded to your Hugging Face Space

dataset = load_sop_dataset()

# Utility function to find the most relevant context
def find_best_context(question, dataset):
    """Find the single best context for a given question."""
    best_score = 0
    best_context = None

    for index, row in dataset.iterrows():
        # Access the 'text' column in the row
        context_text = row['text']
        # Simple heuristic: Count the number of overlapping words
        overlap = len(set(question.lower().split()) & set(context_text.lower().split()))
        if overlap > best_score:
            best_score = overlap
            best_context = context_text

    return best_context

# Streamlit UI
st.title("SOP Question Answering AI")
st.markdown("Ask any question about Standard Operating Procedures:")

# User input
question = st.text_area("Enter your question:", "")

# Generate answer
if st.button("Get Answer"):
    if question:
        with st.spinner("Finding the best context..."):
            # Automatically find the most relevant context
            context = find_best_context(question, dataset)

            if context:
                with st.spinner("Answering your question..."):
                    result = qa_pipeline(question=question, context=context)
                    st.success("Answer:")
                    st.write(result["answer"])
                    st.write("Confidence Score:", result["score"])
            else:
                st.warning("No relevant context found. Please try rephrasing your question.")
    else:
        st.warning("Please enter a question.")