backend.py

from transformers import pipeline
from retrieval import get_relevant_pubmed_docs
from mini_ladder import generate_sub_questions, self_critique_and_refine

# Use Microsoft BioGPT-Large-PubMedQA for generation
MODEL_NAME = "microsoft/BioGPT-Large-PubMedQA"
qa_pipeline = pipeline("text-generation", model=MODEL_NAME)

# In-memory cache for documents (used for graph generation)
docs_cache = {}

def process_medical_query(query: str):
    """
    Processes the query in four steps:
    1. Generate sub-questions.
    2. Retrieve relevant PubMed documents.
    3. Generate an initial answer.
    4. Self-critique and refine the answer.
    """
    # Step 1: Generate sub-questions (naively)
    sub_questions = generate_sub_questions(query)

    # Step 2: Retrieve relevant documents via PubMed and Chroma
    relevant_docs = get_relevant_pubmed_docs(query)
    docs_cache[query] = relevant_docs

    if not relevant_docs:
        return ("No documents found for this query.", sub_questions, "", "")

    # Step 3: Generate an initial answer
    context_text = "\n\n".join(relevant_docs)
    prompt = f"Question: {query}\nContext: {context_text}\nAnswer:"
    initial_gen = qa_pipeline(prompt, max_new_tokens=100, truncation=True)
    if initial_gen and isinstance(initial_gen, list):
        initial_answer = initial_gen[0]["generated_text"]
    else:
        initial_answer = "No answer found."

    # Step 4: Self-critique and refine the answer
    final_answer, critique = self_critique_and_refine(query, initial_answer, relevant_docs)

    return (final_answer, sub_questions, initial_answer, critique)