add project files

Dockerfile

@@ -0,0 +1,16 @@
+# 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"]

README.md

@@ -1,8 +1,8 @@
 ---
 title: Sbert Embedding
-emoji: 🏃
-colorFrom: purple
-colorTo: yellow
+emoji: 🦀
+colorFrom: green
+colorTo: pink
 sdk: docker
 pinned: false
 ---

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+from typing import List
+
+# Initialize the model
+model = SentenceTransformer("PartAI/Tooka-SBERT")
+
+# Create the FastAPI app
+app = FastAPI()
+
+# Pydantic model for input data
+class TextInput(BaseModel):
+    sentences: List[str]
+
+@app.get('/')
+def index():
+    return {'message': 'Sentence embedding API.'}
+
+# Endpoint to get embeddings
+@app.post("/get_embeddings")
+async def get_embeddings(input_data: TextInput):
+    # Get embeddings for the input sentences
+    embeddings = model.encode(input_data.sentences)
+    return {"embeddings": embeddings.tolist()}
+
+# To run the app, save this code to a file, and then run `uvicorn filename:app --reload`

requirements.txt

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+fastapi
+uvicorn[standard]
+sentence_transformers

test.py

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+import requests
+import json
+
+url = "https://diginext-sbert-embedding.hf.space/get_embeddings"
+
+payload = json.dumps({
+  "sentences": [
+    "همه چی خوبه؟"
+  ]
+})
+headers = {
+  'accept': 'application/json',
+  'Content-Type': 'application/json'
+}
+
+response = requests.request("POST", url, headers=headers, data=payload)
+
+print(response.text)