app/models.py

# app/models.py
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
import torch

# Load a domain-specific model (example: MatSciBERT for materials text)
MATS_BERT_MODEL = "m3rg-iitd/matscibert"  # adjust model name as needed
tokenizer = AutoTokenizer.from_pretrained(MATS_BERT_MODEL)
model = AutoModelForTokenClassification.from_pretrained(MATS_BERT_MODEL)

# Create a pipeline for token classification (NER, relation extraction)
ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

def extract_entities(text: str):
    """
    Process text using a domain-specific BERT model to extract entities.
    """
    results = ner_pipeline(text)
    # Format the output as a list of (entity, score, start, end)
    entities = [{"entity": r["entity_group"], "word": r["word"], "score": r["score"]} for r in results]
    return entities

def answer_question(query: str):
    """
    For demonstration, we use a simple approach.
    In practice, you may combine a retrieval step with a Q&A model.
    """
    # For example purposes, we simulate an answer by echoing the query.
    # Replace this with your domain-specific Q&A logic.
    return f"Simulated answer for query: '{query}'"
# Model loading and inference functions