Add application file

Dockerfile

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+# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+from typing import List
+
+# Load the pre-trained sentence transformer model
+bge_small_model = SentenceTransformer('BAAI/bge-small-en-v1.5', device="cpu")
+all_mp_net_model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2', device="cpu")
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Request body model
+class TextInput(BaseModel):
+    text: List[str]  # List of sentences or text data
+    model_name: str
+
+# Route to calculate embeddings
+@app.post("/get-embedding/")
+async def get_embedding(input: TextInput):
+    # Generate embeddings using the sentence transformer model
+    if input.model_name == "BM":
+        embeddings = all_mp_net_model.encode(input.text)
+    else:
+        embeddings = bge_small_model.encode(input.text)
+    return {"embeddings": embeddings.tolist()}

requirements.txt

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+fastapi
+uvicorn[standard]
+sentence_transformers