-
Notifications
You must be signed in to change notification settings - Fork 2
/
app.py
62 lines (52 loc) · 1.54 KB
/
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
from __future__ import division, print_function
# coding=utf-8
import sys
import os
import glob
import re
import numpy as np
import torch
from PIL import Image
import albumentations as aug
from efficientnet_pytorch import EfficientNet
# Flask utils
from flask import Flask, redirect, url_for, request, render_template
from werkzeug.utils import secure_filename
from gevent.pywsgi import WSGIServer
# Define a flask app
app = Flask(__name__)
model = torch.load("best.pth")
model.eval()
def model_predict(file, model):
image = Image.open(file).convert('RGB')
image = np.array(image)
transforms = aug.Compose([
aug.Resize(224,224),
aug.Normalize((0.485, 0.456, 0.406),(0.229, 0.224, 0.225),max_pixel_value=255.0,always_apply=True),
])
image = transforms(image=image)["image"]
image = np.transpose(image, (2, 0, 1)).astype(np.float32)
image = torch.tensor([image], dtype=torch.float)
preds = model(image)
probs = preds.detach().numpy()[0]
probs = np.exp(probs)/np.sum(np.exp(probs))
return probs
@app.route('/')
def index():
# Main page
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def upload():
# Get the file from post request
f = request.files['file']
labs=['Cat','Dog']
# Make prediction
probs = model_predict(f, model)
# result = labs[preds]
probs = ["%.8f" % x for x in probs]
outs = {}
for i in range(len(labs)):
outs[labs[i]]=probs[i]
return outs
if __name__ == '__main__':
app.run(debug=True)