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import streamlit as st
import tensorflow as tf
from PIL import Image
import numpy as np
import json
from tensorflow.keras.applications.vgg16 import VGG16,preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.text import Tokenizer,tokenizer_from_json
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Model
from keras.models import load_model
# Load the .h5 model
model = load_model('image_caption.h5')
with open('tokenizer_config.json', 'r') as f:
tokenizer_config = json.load(f)
tokenizer = tokenizer_from_json(tokenizer_config)
# tokenizer.word_index = eval(tokenizer_config)['word_index']
max_length=35
# Load pre-trained model
vgg_model = VGG16()
vgg_model = Model(inputs=vgg_model.inputs, outputs=vgg_model.layers[-2].output)
# Set Streamlit configurations
st.set_page_config(page_title="Image Captioning App", layout="wide")
# Function to preprocess the input image
def preprocess_image(image):
image = image.convert("RGB")
image = image.resize((224, 224))
image = img_to_array(image)
image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
image = preprocess_input(image)
return image
# Function to make predictions on the input image
def predict(image):
image = preprocess_image(image)
feature = vgg_model.predict(image, verbose=0)
preds = predict_caption(model, feature, tokenizer, max_length)
preds=preds[8:-7]
return preds
def idx_word(integer,tok):
for word,index in tok.word_index.items():
if index== integer:
return word
return None
def predict_caption(model,image,tok,max_len):
in_text="startseq"
for i in range(max_len):
seq=tok.texts_to_sequences([in_text])[0]
seq=pad_sequences([seq],max_len)
yhat = model.predict([image, seq], verbose=0)
yhat = np.argmax(yhat)
word = idx_word(yhat, tok)
if word is None:
break
in_text += " " + word
if word == 'endseq':
break
return in_text
# Streamlit app
def main():
st.title("Image Captioning App")
st.write("Upload an image and the app will predict its class.")
uploaded_image = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
if uploaded_image is not None:
image = Image.open(uploaded_image)
st.image(image, caption='Uploaded Image', use_column_width=True)
st.write("")
if st.button("Generate Caption"):
with st.spinner("Generating..."):
predictions = predict(image)
st.write(f"Top Caption:{predictions}")
# Run the app
if __name__ == "__main__":
main()
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