Update app.py

app.py

@@ -5,22 +5,20 @@ from typing import Any, List
 from transformers import BertTokenizer, BertConfig, TFBertModel
 
 MODEL_DIR = os.environ.get("MODEL_DIR", "/app/bert_tf")
-PORT = int(os.environ.get("PORT", "7860"))  # HF Spaces sets PORT=7860
+PORT = int(os.environ.get("PORT", "7860"))
 
-# --- Load BioBERT (TF checkpoint converted by HF loader) ---
 tok  = BertTokenizer(vocab_file=f"{MODEL_DIR}/vocab.txt", do_lower_case=True)
 cfg  = BertConfig.from_json_file(f"{MODEL_DIR}/config.json")
 model= TFBertModel.from_pretrained(MODEL_DIR, from_tf=True, config=cfg)
 
 def encode(texts: List[str]):
     ins  = tok(texts, padding=True, truncation=True, return_tensors="tf", max_length=512)
-    outs = model(ins)[0]  # [batch, seq, hidden]
+    outs = model(ins)[0]
     mask = tf.cast(tf.expand_dims(ins["attention_mask"], -1), tf.float32)
-    mean = tf.reduce_sum(outs * mask, axis=1) / tf.maximum(tf.reduce_sum(mask, axis=1), 1.0)
+    mean = tf.reduce_sum(outs*mask, axis=1) / tf.maximum(tf.reduce_sum(mask, axis=1), 1.0)
     return tf.linalg.l2_normalize(mean, axis=1).numpy().tolist()
 
-# Warmup to reduce first request latency (still a cold-start, but faster)
-_ = encode(["warmup biobert embeddings"])
+_ = encode(["warmup"])
 
 app = FastAPI()
 
@@ -35,8 +33,5 @@ def health():
 def embeddings(req: EmbReq):
     texts = req.input if isinstance(req.input, list) else [req.input]
     vecs  = encode(texts)
-    return {
-        "object": "list",
-        "model" : "biobert-tf-emb",
-        "data"  : [{"object":"embedding","index":i,"embedding":v} for i, v in enumerate(vecs)]
-    }
+    return {"object":"list","model":"biobert-tf-emb",
+            "data":[{"object":"embedding","index":i,"embedding":v} for i,v in enumerate(vecs)]}