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,
)
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
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())
# caption=generate_caption()
new_model = ImageCaptioningModel(cnn_model=cnn_model, encoder=encoder, decoder=decoder, image_aug=image_augmentation)
# Call the model with some dummy input data to create its variables
dummy_img = tf.zeros((1, 229, 229, 3))
dummy_seq = tf.zeros((1, 5, 512))
new_model.cnn_model(dummy_img)
new_model.encoder(dummy_seq)
new_model.decoder(dummy_seq)
# Load the weights
new_model.load_weights('model_weights.h5')
st.success(f'Model Loaded!', icon="✅")
# st.success(f'Reported EER for the selected model {reported_eer}%')
with st.spinner("Getting prediction..."):
# 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()