deploy

Dockerfile

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+# Use official Python base image
+FROM python:3.11-slim
+
+# Set the working directory
+WORKDIR /app
+
+# Copy all files to the container
+COPY . /app
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose the port (Hugging Face uses port 7860 by default)
+EXPOSE 7860
+
+# Run the FastAPI server using uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI
+from routes.sms_routes import router as sms_router
+
+# Initialize FastAPI app
+app = FastAPI
+
+# Include the router for SMS endpoints
+app.include_router(sms_router)
+
+@app.get("/")
+def home():
+    
+    return {"message": "Welcome to embedded model"}

models/model.py

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+from sentence_transformers import SentenceTransformer
+
+# Load the pre-trained model once at startup
+model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)

requirements.txt

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+fastapi
+uvicorn
+sentence-transformers
+torch
+scikit-learn
+numpy
+pandas
+openpyxl

routes/sms_routes.py

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+from fastapi import APIRouter, HTTPException
+from schema.pydantic_schema import SMSRequest, EmbeddingsResponse, EmbeddingResponse
+from service.embedding_service import generate_embeddings
+
+# Initialize Router
+router = APIRouter()
+
+@router.post("/get_embeddings/", response_model=EmbeddingsResponse)
+async def get_embeddings(sms_request: SMSRequest):
+    # Check if the input list is not empty
+    if not sms_request.messages:
+        raise HTTPException(status_code=400, detail="No messages provided.")
+
+    # Generate embeddings
+    embeddings = generate_embeddings(sms_request.messages)
+
+    # Structure the response
+    response = [
+        EmbeddingResponse(message=msg, embedding=emb) 
+        for msg, emb in zip(sms_request.messages, embeddings)
+    ]
+
+    return {"embeddings": response}

schema/pydantic_schema.py

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+from pydantic import BaseModel
+from typing import List
+
+# Request schema
+class SMSRequest(BaseModel):
+    messages: List[str]
+
+from pydantic import BaseModel
+from typing import List
+
+# Response schema for embedding vectors
+class EmbeddingResponse(BaseModel):
+    message: str
+    embedding: List[float]
+
+# Final response containing all embeddings
+class EmbeddingsResponse(BaseModel):
+    embeddings: List[EmbeddingResponse]