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import tensorflow as tf
tf.__version__
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tensorflow.keras.applications import vgg16
#from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Input, Dense, Flatten
from tensorflow.keras.utils import load_img, img_to_array
from sklearn.metrics import confusion_matrix
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tensorflow.keras.applications import vgg16
#from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Input, Dense, Flatten
from tensorflow.keras.utils import load_img, img_to_array
from sklearn.metrics import confusion_matrix
vgg16_model = vgg16.VGG16(include_top=False, input_shape=(224,224,3), weights='imagenet')
vgg16_model.trainable = False
model = Sequential()
model.add(vgg16_model)
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['Accuracy'])
model.load_weights("cp.ckpt")
import gradio as gr
import numpy as np
from PIL import Image
def sepia(input_img_path):
img = load_img(input_img_path,target_size=(224,224))
img = img_to_array(img)
img = img / 255
img = img.reshape(1,224,224,3)
p = (model.predict(img)>=0.5).astype(int)[0][0]
if p==0:
return "Men"
else:
return "women"
demo = gr.Interface(fn=sepia,inputs= gr.Image(type="filepath",height=700,width=600),outputs="text",
examples=[ ["0008.jpg"],['0059.jpg'],['0d0408eb-b0f2-454b-b4cf-1bd9d48ab3a5.jpg'],
['20240609_175821.jpg'],['20240609_180301.jpg'],['20240609_181603.jpg'],
['20240609_180539.jpg'],['5ac73492-f665-48a1-a9c9-320bd7dfa1b6.jpg']])
demo.launch() |