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| import streamlit as st | |
| import pickle | |
| st.header("Image Captioner") | |
| st.markdown("Building the framework may take upto a minute. Please be patient. Thank you!") | |
| features=pickle.load(open("features.pkl","rb")) | |
| all_captions=pickle.load(open("all_captions.pkl","rb")) | |
| from tensorflow.keras.preprocessing.text import Tokenizer | |
| tokenizer = Tokenizer() | |
| tokenizer.fit_on_texts(all_captions) | |
| vocab_size = len(tokenizer.word_index) + 1 | |
| max_length = max(len(caption.split()) for caption in all_captions) | |
| from tensorflow import keras | |
| model = keras.models.load_model("best_model.h5") | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| def idx_to_word(integer, tokenizer): | |
| for word, index in tokenizer.word_index.items(): | |
| if index == integer: | |
| return word | |
| return None | |
| # generate caption for an image | |
| import numpy as np | |
| def predict_caption(model, image, tokenizer, max_length): | |
| # add start tag for generation process | |
| in_text = 'startseq' | |
| # iterate over the max length of sequence | |
| for i in range(max_length): | |
| # encode input sequence | |
| sequence = tokenizer.texts_to_sequences([in_text])[0] | |
| # pad the sequence | |
| sequence = pad_sequences([sequence], max_length) | |
| # 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 | |
| in_text += " " + word | |
| if word == 'endseq': | |
| break | |
| return in_text | |
| from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input | |
| from tensorflow.keras.models import Model | |
| from gtts import gTTS | |
| from io import BytesIO | |
| sound_file = BytesIO() | |
| vgg_model = VGG16() | |
| vgg_model = Model(inputs=vgg_model.inputs, outputs=vgg_model.layers[-2].output) | |
| from tensorflow.keras.preprocessing.image import img_to_array | |
| from PIL import Image | |
| uploaded_image=st.file_uploader("Upload image to be captioned",type=["jpg","png","jpeg",]) | |
| image_path="bushman.jpeg" | |
| if(uploaded_image!=None): | |
| display_image=Image.open(uploaded_image) | |
| st.image(display_image) | |
| if st.button("Caption"): | |
| st.text("Please be patient...") | |
| display_image=display_image.resize((224,224)) | |
| image = img_to_array(display_image) | |
| image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2])) | |
| image = preprocess_input(image) | |
| feature = vgg_model.predict(image, verbose=0) | |
| final=predict_caption(model, feature, tokenizer, max_length) | |
| final_output=((" ").join(final.split(" ")[1:len(final.split(" "))-1])) | |
| tts = gTTS(final_output, lang='en') | |
| tts.write_to_fp(sound_file) | |
| st.text("Output:") | |
| st.text(final_output) | |
| st.audio(sound_file) |