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import streamlit as st
from PIL import Image
import tensorflow as tf
import numpy as np
import pickle
# Load the pre-trained model
@st.cache(allow_output_mutation=True)
def load_model():
model = tf.keras.models.load_model('model.h5')
return model
# Load the tokenizer
@st.cache(allow_output_mutation=True)
def load_tokenizer():
with open('tokenizer.pkl', 'rb') as handle:
tokenizer = pickle.load(handle)
return tokenizer
model = load_model()
tokenizer = load_tokenizer()
# Function to preprocess the image
def preprocess_image(image):
image = image.resize((299, 299)) # Resize to the input size of the model
image = np.array(image) / 255.0 # Normalize
image = np.expand_dims(image, axis=0) # Add batch dimension
return image
# Function to generate caption
def generate_caption(image):
image = preprocess_image(image)
predictions = model.predict(image)
predicted_caption = tokenizer.sequences_to_texts(predictions.argmax(axis=-1))
return predicted_caption[0]
# Streamlit app
st.title("Image Captioning App")
st.write("Upload an image to generate a caption")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image.', use_column_width=True)
st.write("")
st.write("Generating caption...")
caption = generate_caption(image)
st.write(caption)
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