Spaces:
Runtime error
Runtime error
File size: 10,126 Bytes
4442007 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
import functools
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.applications import efficientnet
#import efficientnet
from tensorflow.keras.layers import TextVectorization
import matplotlib.pyplot as plt
import cv2
from models import EMBED_DIM, FF_DIM, SEQ_LENGTH, ImageCaptioningModel, TransformerDecoderBlock, TransformerEncoderBlock, get_cnn_model, image_augmentation, vectorization, valid_data, decode_and_resize
def display_UI():
import streamlit as st
from streamlit_option_menu import option_menu
import streamlit.components.v1 as html
import pandas as pd
import numpy as np
from pathlib import Path
# from PIL import Image
st.markdown(""" <style> .appview-container .main .block-container {
max-width: 100%;
padding-top: 1rem;
padding-right: {1}rem;
padding-left: {1}rem;
padding-bottom: {1}rem;
}</style> """, unsafe_allow_html=True)
#Add a logo (optional) in the sidebar
# logo = Image.open(r'C:\Users\13525\Desktop\Insights_Bees_logo.png')
# with st.sidebar:
# choose = option_menu("Forensic Examiner", ["Inspect Media","Comparative Analysis","About", "Contact"],
# icons=['camera fill', 'kanban', 'book','person lines fill'],
# menu_icon="app-indicator", default_index=0,
# styles={
# "container": {"padding": "0 5 5 5 !important", "background-color": "#fafafa"},
# "icon": {"color": "orange", "font-size": "25px"},
# "nav-link": {"font-size": "16px", "text-align": "left", "margin":"0px", "--hover-color": "#eee"},
# "nav-link-selected": {"background-color": "#02ab21"},
# }
# )
#Add the cover image for the cover page. Used a little trick to center the image
st.markdown(""" <style> .font {
font-size:25px ; font-family: 'Cooper Black'; color: #FF9633;}
</style> """, unsafe_allow_html=True)
col1, col2 = st.columns( [0.8, 0.2])
with col1: # To display the header text using css style
st.markdown('<p class="font">Generate Caption of image</p>', unsafe_allow_html=True)
with col2: # To display brand logo
st.image('./logo.png', width=50 )
# model_name = st.selectbox("Select the model...", list (all_models.keys ()))
uploaded_file = st.file_uploader("Choose an Image File", type=[".jpg", ".jpeg", ".png", ".PNG"],
accept_multiple_files=False)
opencv_image= None
if uploaded_file is not None:
with st.spinner('Wait for it...'):
# read image file and store for prediction
# img_file=uploaded_file.read()
file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
opencv_image = cv2.imdecode(file_bytes, 1)
# Now do something with the image! For example, let's display it:
st.image(opencv_image, channels="BGR")
# bytes_data = uploaded_file.getvalue()
# audio_bytes = uploaded_file.read()
# save_folder = './data'
# save_path = Path(save_folder, uploaded_file.name)
# with open(save_path, mode='wb') as w:
# w.write(uploaded_file.getvalue())
st.image(opencv_image, width=400 )
with st.spinner('Loading the model..'):
cnn_model = get_cnn_model()
encoder = TransformerEncoderBlock(embed_dim=EMBED_DIM, dense_dim=FF_DIM, num_heads=1)
decoder = TransformerDecoderBlock(embed_dim=EMBED_DIM, ff_dim=FF_DIM, num_heads=2)
new_model = ImageCaptioningModel(
cnn_model=cnn_model, encoder=encoder, decoder=decoder, image_aug=image_augmentation,
)
new_model.load_weights('model_weights')
st.success(f'Model Loaded!', icon="✅")
# st.success(f'Reported EER for the selected model {reported_eer}%')
with st.spinner("Getting prediction..."):
vocab = vectorization.get_vocabulary()
index_lookup = dict(zip(range(len(vocab)), vocab))
max_decoded_sentence_length = SEQ_LENGTH - 1
valid_images = list(valid_data.keys())
def generate_caption():
# Select a random image from the validation dataset
sample_img = opencv_image #np.random.choice(valid_images)
# Read the image from the disk
cv2.imwrite('./uploaded_image.jpg', sample_img)
sample_img = decode_and_resize('./uploaded_image.jpg')
img = sample_img.numpy().clip(0, 255).astype(np.uint8)
#plt.imshow(img)
#plt.show()
# Pass the image to the CNN
img = tf.expand_dims(sample_img, 0)
img = new_model.cnn_model(img)
# Pass the image features to the Transformer encoder
encoded_img = new_model.encoder(img, training=False)
# Generate the caption using the Transformer decoder
decoded_caption = "<start> "
for i in range(max_decoded_sentence_length):
tokenized_caption = vectorization([decoded_caption])[:, :-1]
mask = tf.math.not_equal(tokenized_caption, 0)
predictions = new_model.decoder(
tokenized_caption, encoded_img, training=False, mask=mask
)
sampled_token_index = np.argmax(predictions[0, i, :])
sampled_token = index_lookup[sampled_token_index]
if sampled_token == " <end>":
break
decoded_caption += " " + sampled_token
decoded_caption = decoded_caption.replace("<start> ", "")
decoded_caption = decoded_caption.replace(" <end>", "").strip()
return decoded_caption
# Check predictions for a few samples
caption=generate_caption()
# print(audio.shape)
if caption:
st.success(caption, icon="✅")
else:
# st.error(f"The Sample is spoof: \n Confidence {(prediction_value) }%", icon="🚨")
st.error(f"Error occured in caption generation", icon="🚨")
# if choose == "Comparative Analysis":
# st.markdown(""" <style> .font {
# font-size:25px ; font-family: 'Cooper Black'; color: #FF9633;}
# </style> """, unsafe_allow_html=True)
# st.markdown('<p class="font">Comparison of Models</p>', unsafe_allow_html=True)
# data_frame = get_data()
# tab1, tab2 = st.tabs(["EER", "min-TDCF"])
# with tab1:
# data_frame["EER ASVS 2019"] = data_frame["EER ASVS 2019"].astype('float64')
# data_frame["EER ASVS 2021"] = data_frame["EER ASVS 2021"].astype('float64')
# data_frame["Cross-dataset 19-21"] = data_frame["Cross-dataset 19-21"].astype('float64')
# data = data_frame[["Model Name","EER ASVS 2019","EER ASVS 2021","Cross-dataset 19-21"]].reset_index(drop=True).melt('Model Name')
# chart=alt.Chart(data).mark_line().encode(
# x='Model Name',
# y='value',
# color='variable'
# )
# st.altair_chart(chart, theme=None, use_container_width=True)
# with tab2:
# data_frame["min-TDCF ASVS 2019"] = data_frame["EER ASVS 2019"].astype('float64')
# data_frame["min-TDCF ASVS 2021"] = data_frame["EER ASVS 2021"].astype('float64')
# data_frame["min-TDCF Cross-dataset"] = data_frame["Cross-dataset 19-21"].astype('float64')
# data = data_frame[["Model Name","min-TDCF ASVS 2019","min-TDCF ASVS 2021","min-TDCF Cross-dataset"]].reset_index(drop=True).melt('Model Name')
# chart=alt.Chart(data).mark_line().encode(
# x='Model Name',
# y='value',
# color='variable'
# )
# st.altair_chart(chart, theme=None, use_container_width=True)
# # Data table
# st.markdown(""" <style> .appview-container .main .block-container {
# max-width: 100%;
# padding-top: {1}rem;
# padding-right: {1}rem;
# padding-left: {1}rem;
# padding-bottom: {1}rem;
# }</style> """, unsafe_allow_html=True)
# st.dataframe(data_frame, use_container_width=True)
# if choose == "About":
# st.markdown(""" <style> .font {
# font-size:35px ; font-family: 'Cooper Black'; color: #FF9633;}
# </style> """, unsafe_allow_html=True)
# st.markdown('<p class="font">About</p>', unsafe_allow_html=True)
# if choose == "Contact":
# st.markdown(""" <style> .font {
# font-size:35px ; font-family: 'Cooper Black'; color: #FF9633;}
# </style> """, unsafe_allow_html=True)
# st.markdown('<p class="font">Contact Us</p>', unsafe_allow_html=True)
# with st.form(key='columns_in_form2',clear_on_submit=True): #set clear_on_submit=True so that the form will be reset/cleared once it's submitted
# #st.write('Please help us improve!')
# Name=st.text_input(label='Please Enter Your Name') #Collect user feedback
# Email=st.text_input(label='Please Enter Your Email') #Collect user feedback
# Message=st.text_input(label='Please Enter Your Message') #Collect user feedback
# submitted = st.form_submit_button('Submit')
# if submitted:
# st.write('Thanks for your contacting us. We will respond to your questions or inquiries as soon as possible!')
display_UI()
|