Spaces:
Runtime error
Runtime error
File size: 3,339 Bytes
938c946 73cca1b f697910 73cca1b 5507b4f 5e6b109 73cca1b 938c946 73cca1b 5507b4f 9f0de8c 938c946 5507b4f 9f0de8c 73cca1b 5507b4f 938c946 5507b4f 938c946 5507b4f 7db86dd 5507b4f a381f7b |
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 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
### ------------------------------- ###
### libraries ###
### ------------------------------- ###
from tensorflow.keras.models import load_model
import gradio as gr # remove later
import numpy as np
import os
from yattag import Doc
# import h5py # remove later
### -------------------------------- ###
### model loading ###
### -------------------------------- ###
model = load_model('model.h5') # single file model from colab
labels = ['please upload categories.txt' for i in range(10)] # placeholder
## --------------------------------- ###
### reading: categories.txt ###
### -------------------------------- ###
if os.path.isfile("categories.txt"):
# open info.txt in read mode
categories = open("categories.txt", "r")
labels = categories.readline().split()
print(labels)
## --------------------------------- ###
### reading: info.txt ###
### -------------------------------- ###
# placeholders in case info.txt does not exist
placeholder = "please create an info.txt to customize this text"
title = bkgd = data_collection = priv_cons = bias_cons = ident_cons = img_src = membs = placeholder
description = "An AI project created by [name], [name], and [name]"
# check if info.txt is present
if os.path.isfile("info.txt"):
# open info.txt in read mode
info = open("info.txt", "r")
# each line to a string
title = info.readline()
bkgd = info.readline()
data_collection = info.readline()
priv_cons = info.readline()
bias_cons = info.readline()
ident_cons = info.readline()
img_src = info.readline()
membs = info.readline()
# close file
info.close()
# use yattag library to generate html
doc, tag, text, line = Doc().ttl()
# create html based on info.txt
with tag('div'):
with tag('div', klass='my-div'):
line('h2', 'Project Background')
line('p', bkgd)
with tag('div', klass='my-div'):
line('h2', 'Data Collection')
line('p', data_collection)
with tag('div', klass='my-div'):
line('h2', 'Ethical Considerations')
with tag('ul'):
line('li', priv_cons)
line('li', bias_cons)
line('li', ident_cons)
with tag('div', klass='my-div'):
line('h2', 'Our Team')
line('p', membs)
doc.stag('img', src=img_src)
my_css = '''
.my-div {
border: 2px solid black;
text-align: center;
margin: 10px;
padding: 5%;
}
ul {
display: inline-block;
text-align: left;
}
img {
display: block;
margin: auto;
}
.description {
text-align: center;
}
'''
### ------------------------------- ###
### interface creation ###
### ------------------------------- ###
def preprocess(image):
image = np.array(image) / 255
image = np.expand_dims(image, axis=0)
return image
def predict_image(image):
pred = model.predict(preprocess(image))
results = {}
for row in pred:
for idx, item in enumerate(row):
results[labels[idx]] = float(item)
return results
image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here")
label = gr.outputs.Label(num_top_classes=len(labels))
gr.Interface(fn=predict_image, inputs=image, outputs=label, capture_session=True, article=doc.getvalue(), css=my_css, theme='huggingface', title=title, allow_flagging=False, description=description).launch(debug=True) |