I´m trying to make a webapp where you can upload and image and the use that image as an input to a classification model. So far I have been able to upload the image and copy that image below but I haven´t been able to process that image and use it as an input to run my model. Do I need to upload the image first to a server in production and then open it again to be processed?
import os
import json
import pandas as pd
import plotly.express as px
from fastai.vision.all import *
from fastai.vision.widgets import *
from PIL import Image
from dash.exceptions import PreventUpdate
import dash
from dash.dependencies import Input, Output, State
import dash_core_components as dcc
import dash_html_components as html
import base64
app = dash.Dash(__name__)
model = load_learner('export.pkl', cpu=True)
app.layout = html.Div([
# Body
html.Div([
html.Div([
dcc.Upload(
id='upload-image',
children=html.Div([
'Drag and Drop',
]),
style={
'width': '100%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin': '10px'
},
# Allow multiple files to be uploaded
multiple=True
),
html.Div(id='output-image-upload', ),
# html.Button("Submit",id='button', ),
html.Div(id='output-container-button',
children=html.Div([
'Prediction',
]), ),
], className='six columns',
)
])
])
def parse_contents(contents, filename, date):
return html.Div([
html.Img(src=contents, style={'height': '50%', 'width': '50%'}),
])
### To extract uploaded image
def extract(lst):
return lst[0],
###
@app.callback(Output('output-image-upload', 'children'),
Input('upload-image', 'contents'),
State('upload-image', 'filename'),
State('upload-image', 'last_modified'))
def update_output(list_of_contents, list_of_names, list_of_dates):
if list_of_contents is not None:
children = [
parse_contents(c, n, d) for c, n, d in
zip(list_of_contents, list_of_names, list_of_dates)]
return children
###########
@app.callback(Output('output-container-button', 'children'),
Input('output-image-upload', 'children'), )
def resizethis(image):
if image is None:
raise PreventUpdate
else:
img = extract(image)
#basewidth = 128
#wpercent = (basewidth / float(img.size[0]))
#hsize = int((float(img.size[1]) * float(wpercent)))
#img128 = img.resize((basewidth, hsize), Image.ANTIALIAS)
#prediction = model.predict(img)
#return prediction
return img
if __name__ == '__main__':
app.run_server(debug=True)
