Update backend.py

backend.py

@@ -1,42 +1,47 @@
 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
+# 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)
+# In-memory cache for documents (used later in the knowledge graph).
 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.
+    Processes a clinical query in two steps:
+    
+    1. Retrieve relevant PubMed abstracts using a retrieval pipeline.
+    2. Generate an answer using BioGPT, leveraging the retrieved abstracts as context.
+    
+    Returns:
+        - The generated answer.
+        - An empty list for sub-questions (omitted for speed).
+        - The initial answer (same as final in this simplified pipeline).
+        - An empty critique (omitted).
+    
+    Designed for clinical use with clear, concise responses.
     """
-    # Step 1: Generate sub-questions (naively)
-    sub_questions = generate_sub_questions(query)
-
-    # Step 2: Retrieve relevant documents via PubMed and Chroma
+    # 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, "", "")
+        return ("No documents found for this query.", [], "", "")
 
-    # Step 3: Generate an initial answer
+    # Combine the retrieved abstracts as context.
     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)
+    # Generate an answer using BioGPT.
+    generation = qa_pipeline(prompt, max_new_tokens=100, truncation=True)
+    if generation and isinstance(generation, list):
+        answer = generation[0]["generated_text"]
+    else:
+        answer = "No answer found."
 
-    return (final_answer, sub_questions, initial_answer, critique)
+    # In this streamlined pipeline, we return the same answer for all outputs.
+    sub_questions = []  # No sub-questions generated.
+    critique = ""       # No self-critique performed.
+    return answer, sub_questions, answer, critique