<gh_stars>0
import datetime

import dash
from dash.dependencies import Input, Output, State
import dash_core_components as dcc
import dash_html_components as html

import PIL.Image as Image
from io import BytesIO
import base64


import torch
import torchvision
from torchvision import datasets, models, transforms
import numpy as np
import pandas as pd

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

device = torch.device('cpu')
model = torch.load('model_conv.pth', map_location=device)

labels = np.array(open("class.txt").read().splitlines())

preprocess = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

inv_normalize = transforms.Normalize(
   mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],
   std=[1/0.229, 1/0.224, 1/0.225]
)

app.layout = html.Div([
    html.Div([
        html.H2('The Flower Classifier'),
        html.Strong('This application will attempt to classify your picture into 6 different species of flowers. Drag your image file into the below box to classify. This app (and repo) is intended to demonstrate how to load a saved tensorflow model for image classification and use the model in an interactive Dash application.'),
    ]),

    dcc.Upload(
        id='upload-image',
        children=html.Div([
            'Drag and Drop or ',
            html.A('Select Files')
        ]),
        style={
            'width': '100%',
            'height': '100px',
            'lineHeight': '100px',
            'borderWidth': '1px',
            'borderStyle': 'dashed',
            'borderRadius': '5px',
            'textAlign': 'center',
            'margin': '10px'
        },
        # Allow multiple files to be uploaded
        multiple=True
    ),

    html.Div(id='output-image-upload'),
])


def parse_contents(contents, filename, date):
    #convert uploaded image file in Pillow image file
    encoded_image = contents.split(",")[1]
    decoded_image = base64.b64decode(encoded_image)
    bytes_image = BytesIO(decoded_image)
    image = Image.open(bytes_image).convert('RGB')


    img = preprocess(image)
    original_image = inv_normalize(img)
    original_image1 = np.transpose(original_image.squeeze().detach().numpy(), (1,2,0))
    htmlimg = Image.fromarray((original_image1 * 255).astype(np.uint8))
    img = img.unsqueeze(0)
    pred = model(img)

    print(pred.detach().numpy())

    df = pd.DataFrame({'class':labels, 'probability':pred[0].detach().numpy()})
    
    return html.Div([
        # HTML images accept base64 encoded strings in the same format
        # that is supplied by the upload
        html.Img(src=htmlimg),
        html.Hr(),
        generate_table(df.sort_values(['probability'], ascending=[False]))
    ])

def generate_table(dataframe, max_rows=10):
    return html.Table([
        html.Thead(
            html.Tr([html.Th(col) for col in dataframe.columns])
        ),
        html.Tbody([
            html.Tr([
                html.Td(dataframe.iloc[i][col]) for col in dataframe.columns
            ]) for i in range(min(len(dataframe), max_rows))
        ])
    ])

@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


if __name__ == '__main__':
    app.run_server(debug=True)

import datetime

import dash
from dash.dependencies import Input, Output, State
import dash_core_components as dcc
import dash_html_components as html

import PIL.Image as Image
from io import BytesIO
import base64


import torch
import torchvision
from torchvision import datasets, models, transforms
import numpy as np
import pandas as pd

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

device = torch.device('cpu')
model = torch.load('model_conv.pth', map_location=device)

labels = np.array(open("class.txt").read().splitlines())

preprocess = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

inv_normalize = transforms.Normalize(
   mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],
   std=[1/0.229, 1/0.224, 1/0.225]
)

app.layout = html.Div([
    html.Div([
        html.H2('The Flower Classifier'),
        html.Strong('This application will attempt to classify your picture into 6 different species of flowers. Drag your image file into the below box to classify. This app (and repo) is intended to demonstrate how to load a saved tensorflow model for image classification and use the model in an interactive Dash application.'),
    ]),

    dcc.Upload(
        id='upload-image',
        children=html.Div([
            'Drag and Drop or ',
            html.A('Select Files')
        ]),
        style={
            'width': '100%',
            'height': '100px',
            'lineHeight': '100px',
            'borderWidth': '1px',
            'borderStyle': 'dashed',
            'borderRadius': '5px',
            'textAlign': 'center',
            'margin': '10px'
        },
        # Allow multiple files to be uploaded
        multiple=True
    ),

    html.Div(id='output-image-upload'),
])


def parse_contents(contents, filename, date):
    #convert uploaded image file in Pillow image file
    encoded_image = contents.split(",")[1]
    decoded_image = base64.b64decode(encoded_image)
    bytes_image = BytesIO(decoded_image)
    image = Image.open(bytes_image).convert('RGB')


    img = preprocess(image)
    original_image = inv_normalize(img)
    original_image1 = np.transpose(original_image.squeeze().detach().numpy(), (1,2,0))
    htmlimg = Image.fromarray((original_image1 * 255).astype(np.uint8))
    img = img.unsqueeze(0)
    pred = model(img)

    print(pred.detach().numpy())

    df = pd.DataFrame({'class':labels, 'probability':pred[0].detach().numpy()})
    
    return html.Div([
        # HTML images accept base64 encoded strings in the same format
        # that is supplied by the upload
        html.Img(src=htmlimg),
        html.Hr(),
        generate_table(df.sort_values(['probability'], ascending=[False]))
    ])

def generate_table(dataframe, max_rows=10):
    return html.Table([
        html.Thead(
            html.Tr([html.Th(col) for col in dataframe.columns])
        ),
        html.Tbody([
            html.Tr([
                html.Td(dataframe.iloc[i][col]) for col in dataframe.columns
            ]) for i in range(min(len(dataframe), max_rows))
        ])
    ])

@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


if __name__ == '__main__':
    app.run_server(debug=True)
# Allow multiple files to be uploaded #convert uploaded image file in Pillow image file # HTML images accept base64 encoded strings in the same format # that is supplied by the upload