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import gradio as gr
import numpy as np
import pandas as pd
import tensorflow as tf
import pickle
from PIL import Image
with open('tokenizer.pkl', 'rb') as handle:
tokenizer = pickle.load(handle)
feature_model=tf.keras.models.load_model('feature_model.keras')
model = tf.keras.models.load_model('best_model_inceptionv3.keras')
def idx_to_word(integer, tokenizer):
for word, index in tokenizer.word_index.items():
if index == integer:
return word
return None
def predict_caption(image):
in_text = 'startseq'
# iterate over the max length of sequence
for i in range(35):
# encode input sequence
sequence = tokenizer.texts_to_sequences([in_text])[0]
# pad the sequence
sequence = tf.keras.preprocessing.sequence.pad_sequences([sequence], 35, padding='post')
# predict next word
yhat = model.predict([image, sequence], verbose=0)
# get index with high probability
yhat = np.argmax(yhat)
# convert index to word
word = idx_to_word(yhat, tokenizer)
# stop if word not found
if word is None:
break
# append word as input for generating next word
in_text += " " + word
# stop if we reach end tag
if word == 'endseq':
break
# Split the generated sequence to exclude the first and last words
final_caption = in_text.split()[1:-1]
# Join the words to form the final caption
final_caption = ' '.join(final_caption)
return final_caption
def generate_caption(image):
print(image)
image = image.resize((299, 299))
image_array = tf.keras.preprocessing.image.img_to_array(image)
image_array = image_array.reshape((1, 299, 299, 3))
image = tf.keras.applications.inception_v3.preprocess_input(image_array)
feature = feature_model.predict(image, verbose=0)
caption = predict_caption(feature)
return caption
gr.Interface(fn=generate_caption,
inputs=gr.Image(label='Upload a photo',type="pil"),
outputs=gr.Label(label='Caption'),
examples=['1028205764_7e8df9a2ea.jpg','123.jpg','1001773457.jpg','1024138940_f1fefbdce1.jpg'],
title='Image Caption Generator',
).launch(share=True)
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