| from sentence_transformers import SentenceTransformer, util | |
| import torch | |
| import joblib | |
| import os | |
| os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface" | |
| # Load model components once | |
| bundle = joblib.load("semantic_specialist_model.pkl") | |
| local_model_path = "models/all-MiniLM-L6-v2" | |
| model = SentenceTransformer(local_model_path) | |
| known_embeddings = bundle["known_embeddings"] | |
| symptom_specialist_pairs = bundle["symptom_specialist_pairs"] | |
| def predict_specialist(symptom_text: str): | |
| input_embedding = model.encode(symptom_text, convert_to_tensor=True) | |
| similarities = util.pytorch_cos_sim(input_embedding, known_embeddings)[0] | |
| top_idx = similarities.argmax().item() | |
| specialist = symptom_specialist_pairs[top_idx][1] | |
| score = similarities[top_idx].item() | |
| return specialist, score | |