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from fastapi import FastAPI, File, UploadFile, Form |
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import uvicorn |
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import torch |
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import nltk |
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nltk.download("stopwords") |
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import numpy as np |
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from typing import List |
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from inference import inference |
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from main_folder.code_base.utils import CFG |
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TKN_PATH= ["bert-base-uncased"] |
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IMG_SIZE = 256 |
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BATCH_SIZE = 32 |
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img = True |
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CFG.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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app = FastAPI(title="shopee-test-app") |
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@app.get("/") |
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async def root(): |
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return {"status": "ok", "message": "Space is running"} |
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@app.post("/predict") |
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async def predict_image(files: List[UploadFile] = File(...), |
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texts: List[str] = Form(...)): |
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li, lt= [], [] |
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for file, text in zip(files, texts): |
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contents = await file.read() |
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li.append(contents) |
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lt.append(text) |
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res = inference(li=li, |
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lt=lt, |
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IMG_SIZE=IMG_SIZE, |
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TKN_PATH=TKN_PATH, |
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BATCH_SIZE=BATCH_SIZE |
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) |
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msg = "products matched" if res else "products not matched" |
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return {"message" : f"{msg}"} |
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if __name__ == "__main__": |
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uvicorn.run("app:app", host="0.0.0.0", port=7860) |
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