app.py

import streamlit as st
from datasets import load_dataset
from haystack import Pipeline
from haystack.components.readers import ExtractiveReader
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.document_stores.in_memory import InMemoryDocumentStore

from utils import get_unique_docs


# Load the dataset
@st.cache_data(show_spinner=False)
def load_documents():
    """
    Load the documents from the dataset considering only unique documents.
    Returns:
    - documents: list of dictionaries with the documents.
    """
    unique_docs = set()
    dataset_name = "PedroCJardim/QASports"
    dataset_split = "basketball"
    st.caption(f'Fetching "{dataset_name}" dataset')
    # build the dataset
    dataset = load_dataset(dataset_name, name=dataset_split)
    docs_validation = get_unique_docs(dataset["validation"], unique_docs)
    docs_train = get_unique_docs(dataset["train"], unique_docs)
    docs_test = get_unique_docs(dataset["test"], unique_docs)
    documents = docs_validation + docs_train + docs_test
    return documents


@st.cache_resource(show_spinner=False)
def get_document_store(documents):
    """
    Index the files in the document store.
    Args:
    - files: list of dictionaries with the documents.
    """
    # Create in memory database
    st.caption(f"Building the Document Store")
    document_store = InMemoryDocumentStore()
    document_store.write_documents(documents=documents)
    return document_store


@st.cache_resource(show_spinner=False)
def get_question_pipeline(_doc_store):
    """
    Create the pipeline with the retriever and reader components.
    Args:
    - doc_store: instance of the document store.
    Returns:
    - pipe: instance of the pipeline.
    """
    st.caption(f"Building the Question Answering pipeline")
    # Create the retriever and reader
    retriever = InMemoryBM25Retriever(document_store=_doc_store)
    reader = ExtractiveReader(model="deepset/roberta-base-squad2")
    reader.warm_up()
    # Create the pipeline
    pipe = Pipeline()
    pipe.add_component(instance=retriever, name="retriever")
    pipe.add_component(instance=reader, name="reader")
    pipe.connect("retriever.documents", "reader.documents")
    return pipe


def search(pipeline, question: str):
    """
    Search for the answer to a question in the documents.
    Args:
    - pipeline: instance of the pipeline.
    - question: string with the question.
    Returns:
    - answer: dictionary with the answer.
    """
    # Get the answers
    top_k = 3
    answer = pipeline.run(
        data={
            "retriever": {"query": question, "top_k": 10},
            "reader": {"query": question, "top_k": top_k},
        }
    )
    max_k = min(top_k, len(answer["reader"]["answers"]))
    return answer["reader"]["answers"][0:max_k]


# Loading status
with st.status(
    "Downloading dataset...", expanded=st.session_state.get("expanded", True)
) as status:
    documents = load_documents()
    status.update(label="Indexing documents...")
    doc_store = get_document_store(documents)
    status.update(label="Creating pipeline...")
    pipe = get_question_pipeline(doc_store)
    status.update(
        label="Download and indexing complete!", state="complete", expanded=False
    )
    st.session_state["expanded"] = False

st.subheader("🏀 HoopMind Basketball Wiki", divider="rainbow")
st.caption(
    """Welcome to **HoopMind**!  
    This AI answers basketball questions using the QASports dataset — the first large sports question answering dataset.  
    It includes real info on players, teams, and matches from basketball, soccer, and football, with over **1.5M Q&A pairs** across **54k+ documents**."""
)

if user_query := st.text_input(
    label="Ask HoopMind anything about Basketball! 🧠",
    placeholder="Who is Kobe Bryant?",
):
    # Get the answers
    with st.spinner("Thinking... 🏀"):
        try:
            answer = search(pipe, user_query)
            for idx, ans in enumerate(answer):
                st.info(
                    f"""
                    **Answer {idx+1}:** "{ans.data}"  
                    🔥 Score: {ans.score:0.4f}  
                    📖 Document: "{ans.document.meta["title"]}"  
                    🔗 URL: {ans.document.meta["url"]}
                """
                )
                with st.expander("See details", expanded=False):
                    st.write(ans)
                st.divider()
        except Exception:
            st.error("❌ HoopMind couldn’t find an answer for that one...")