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app.py
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app.py
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import io
from pickletools import read_uint1
from unittest import result
from torchvision import models
import json
from flask import Flask, jsonify, request
from flask import make_response
import torchvision.transforms as transforms
import torch
from PIL import Image
import os
import requests
import logging
app = Flask(__name__)
# yolo model 불러오기
model = torch.hub.load('ultralytics/yolov5', 'custom', path='./best.pt')
# Node.js 서버 주소 저장
node_url = "Node.js 서버IP주소/ingredients"
headers = {"Content-Type": "application/json; charset=utf-8"}
# Node.js 서버에 YOLO결과값을 보내는 Post 함수
def send_data_node(node_url, data):
res = requests.post(node_url, headers=headers, data=data)
# 이미지를 저장하는 함수
def save_image(file):
file.save('./temp/'+ file.filename)
# 기본 URL
@app.route('/')
def web():
return "Lungnaha's flask test page"
# POST 통신으로 들어오는 이미지를 저장하고 모델로 추론하는 과정
@app.route('/predict', methods=['POST'])
def predict():
logging.info("predict")
if request.method == 'POST':
file = request.files.get('file')
logging.info(file)
save_image(file)
train_img = './temp/' + file.filename
temp = model(train_img)
result = temp.pandas().xyxy[0]['name'].to_json(orient="records")
name = file.filename
logging.info(type(result))
result = json.loads(result)
logging.info(type(result))
M = dict(zip(range(1, len(result) + 1), result))
M = json.dumps(M)
logging.info(M)
send_data_node(node_url, M)
logging.info(type(M))
return M
if __name__=="__main__":
app.run(host="0.0.0.0",debug=True)