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File size: 1,942 Bytes
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FastAPI service exposing POST /extract.
Run locally:
uvicorn api:app --reload --port 8000
Example:
curl -X POST http://localhost:8000/extract \
-H "Content-Type: application/json" \
-d '{"text": "Lace mermaid wedding dress with long sleeves and scalloped hem"}'
"""
import os
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from typing import List
from predict import predict_ensemble, predict_ml
from rules_extractor import extract_attributes_rules
MODEL_PATH = os.path.join(os.path.dirname(__file__), "model.joblib")
app = FastAPI(
title="Product Attribute Extraction API",
description="Converts unstructured product/fashion descriptions into structured attributes.",
version="1.0.0",
)
class ExtractRequest(BaseModel):
text: str = Field(..., min_length=1, description="Raw product description text")
mode: str = Field(
"ensemble",
description="Extraction mode: 'ensemble' (default, rules+ml), 'ml', or 'rules'",
)
class ExtractResponse(BaseModel):
text: str
attributes: dict
mode: str
from fastapi.responses import RedirectResponse
@app.get("/")
def read_root():
return RedirectResponse(url="/docs")
@app.get("/health")
def health():
return {"status": "ok"}
@app.post("/extract", response_model=ExtractResponse)
def extract(req: ExtractRequest):
text = req.text.strip()
if not text:
raise HTTPException(status_code=400, detail="text must not be empty")
if req.mode == "rules":
attrs = extract_attributes_rules(text)
elif req.mode == "ml":
attrs = predict_ml(text, model_path=MODEL_PATH)
elif req.mode == "ensemble":
attrs = predict_ensemble(text, model_path=MODEL_PATH)
else:
raise HTTPException(status_code=400, detail="mode must be one of: ensemble, ml, rules")
return ExtractResponse(text=text, attributes=attrs, mode=req.mode)
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