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import gradio as gr
import pickle as pkl
from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
from tensorflow.keras.models import Model
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Feature Extracting model
vgg_model = VGG16()
vgg_model.trainable = False
img_model = Model(inputs=vgg_model.input,
outputs=vgg_model.layers[-2].output)
# Caption genartion model
model = tf.keras.models.load_model('caption_genaration_model.h5')
# load Tokenizer
with open('tokenizer.pkl','rb') as f:
tokenizer = pkl.load(f)
# convert index to word from prediction
def index_to_word(word_idx):
return tokenizer.index_word[word_idx]
# Resize layer
resize_img = tf.keras.layers.Resizing(height=224, width=224)
# Preprocces input Image
def img_preprocces(img):
img = tf.expand_dims(img,axis=0)
resized_image = resize_img(img)
img = preprocess_input(resized_image)
feature = vgg_model.predict(img,verbose=False)
return feature
def genarate_caption(img):
seq_in = 'startseq'
feature_img = img_preprocces(img)
for _ in range(30):
# Tokenization & Padding
seq_in_sequence = tokenizer.texts_to_sequences([seq_in])[0]
seq_in_padded = pad_sequences([seq_in_sequence], padding='post',maxlen=30)
# Predict next word
y_hat = model.predict([feature_img,seq_in_padded],verbose=False)
word_index = y_hat.argmax(axis=1)
predicted_word = index_to_word(word_index[0])
if predicted_word == 'endseq':
break
seq_in = seq_in + ' ' + predicted_word
return seq_in[9:]
app = gr.Interface(
fn=genarate_caption,
inputs=['image'],
outputs=['text']
)
app.launch() |