Create app.py

app.py

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+import os, tensorflow as tf
+from fastapi import FastAPI
+from pydantic import BaseModel
+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
+
+# --- 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]
+    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)
+    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"])
+
+app = FastAPI()
+
+class EmbReq(BaseModel):
+    input: Any
+
+@app.get("/health")
+def health():
+    return {"ok": True}
+
+@app.post("/v1/embeddings")
+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)]
+    }