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"""
backend.py
----------
This module handles the core processing of clinical queries. It retrieves relevant PubMed abstracts
via the retrieval pipeline and then uses BioGPT to generate a clinical answer. Designed for speed and clarity.
"""
from transformers import pipeline
from retrieval import get_relevant_pubmed_docs
# 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 retrieved documents (used for knowledge graph visualization).
docs_cache = {}
def process_medical_query(query: str):
"""
Processes a clinical query by:
1. Retrieving relevant PubMed abstracts.
2. Generating an answer using BioGPT.
Returns:
- final_answer: The generated answer.
- sub_questions: (Empty list; previously used for self-critique but omitted for speed.)
- initial_answer: The same as final_answer in this streamlined version.
- critique: (Empty string; omitted for performance.)
"""
# Retrieve relevant documents using the retrieval pipeline.
relevant_docs = get_relevant_pubmed_docs(query)
docs_cache[query] = relevant_docs
if not relevant_docs:
return ("No documents found for this query.", [], "", "")
# Combine abstracts into a context.
context_text = "\n\n".join(relevant_docs)
prompt = f"Question: {query}\nContext: {context_text}\nAnswer:"
try:
generation = qa_pipeline(prompt, max_new_tokens=100, truncation=True)
except Exception as e:
generation = None
print(f"[ERROR] BioGPT generation failed: {e}")
if generation and isinstance(generation, list):
answer = generation[0]["generated_text"]
else:
answer = "No answer found."
# Return the answer along with empty placeholders for sub-questions and critique.
return answer, [], answer, ""
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