deploy

Dockerfile

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+# Use an official Python base image
+FROM python:3.10-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy the requirements file
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the entire app to the container
+COPY . .
+
+# Expose FastAPI's default port
+EXPOSE 7860
+
+# Run FastAPI with Uvicorn
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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+---
+title: Embeddings RestAPI
+emoji: 🏆
+colorFrom: blue
+colorTo: gray
+sdk: docker
+pinned: false
+---

main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+import numpy as np
+
+# Initialize FastAPI app
+app = FastAPI()
+
+from sentence_transformers import SentenceTransformer
+
+embedding_model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
+
+
+# Define request body structure
+class TextRequest(BaseModel):
+    text: str
+
+# Define response structure
+class EmbeddingResponse(BaseModel):
+    dimensions: int
+    embedding: list[float]
+
+# Create API endpoint
+@app.post("/get_embedding", response_model=EmbeddingResponse)
+async def get_embedding(request: TextRequest):
+    # Generate embedding
+    embedding = embedding_model.encode([request.text])[0]  # Extract first item
+
+    # Convert to list and return response
+    return {"dimensions": len(embedding), "embedding": embedding.tolist()}
+
+# Run the API using: uvicorn app:app --host 0.0.0.0 --port 8000

requirements.txt

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+fastapi==0.100.0
+uvicorn==0.22.0
+pandas==1.5.3
+openpyxl==3.1.2
+scikit-learn==1.2.2
+sentence-transformers==2.2.2
+numpy==1.23.5
+pydantic==1.10.8
+torch==2.0.1  # Required for sentence-transformers