Update utils/specialist_predictor.py

utils/specialist_predictor.py

@@ -1,17 +1,18 @@
-from sentence_transformers import SentenceTransformer, util
-import torch
-import joblib
-
-# Load model components once
-bundle = joblib.load("semantic_specialist_model.pkl")
-model = SentenceTransformer(bundle["model_name"])
-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
+from sentence_transformers import SentenceTransformer, util
+import torch
+import joblib
+
+# Load model components once
+bundle = joblib.load("semantic_specialist_model.pkl")
+model = SentenceTransformer("./models/all-MiniLM-L6-v2")
+
+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