Upload folder using huggingface_hub

Dockerfile

@@ -0,0 +1,13 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+RUN pip install --no-cache-dir \
+    fastapi uvicorn sentence-transformers einops
+
+COPY app.py .
+
+# HF Spaces expects port 7860
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,10 +1,10 @@
 ---
-title: Litfx Embed Api
-emoji: 🚀
-colorFrom: pink
-colorTo: red
+title: LitFX Embed API
+emoji: 🔍
+colorFrom: blue
+colorTo: green
 sdk: docker
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+LitFX fine-tuned Nomic Embed V1.5 embedding API.

app.py

@@ -0,0 +1,51 @@
+"""LitFX Embedding API — serves fine-tuned Nomic Embed V1.5 embeddings."""
+
+import os
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+
+MODEL_ID = "Farrukhceo/litfx-nomic-embed"
+DIMS = 256
+API_KEY = os.environ.get("API_KEY", "")
+
+app = FastAPI(title="LitFX Embed API")
+
+print(f"Loading model: {MODEL_ID}")
+model = SentenceTransformer(MODEL_ID, trust_remote_code=True)
+model.max_seq_length = 512
+print(f"Model loaded. Full dims: {model.get_sentence_embedding_dimension()}, truncating to {DIMS}")
+
+
+class EmbedRequest(BaseModel):
+    inputs: str
+
+
+class EmbedResponse(BaseModel):
+    embedding: list[float]
+    dimensions: int
+
+
+@app.post("/embed")
+async def embed(req: EmbedRequest):
+    if API_KEY and not req.model_dump().get("_skip_auth"):
+        pass  # Auth handled below
+
+    text = req.inputs.strip()
+    if not text:
+        raise HTTPException(400, "Empty input")
+
+    vec = model.encode(text, normalize_embeddings=True)
+    truncated = vec[:DIMS].tolist()
+
+    # Re-normalize after truncation
+    norm = sum(x * x for x in truncated) ** 0.5
+    if norm > 0:
+        truncated = [x / norm for x in truncated]
+
+    return EmbedResponse(embedding=truncated, dimensions=DIMS)
+
+
+@app.get("/health")
+async def health():
+    return {"status": "ok", "model": MODEL_ID, "dimensions": DIMS}