File size: 3,920 Bytes
331d69f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | import os
os.environ['MPLCONFIGDIR'] = '/tmp'
import streamlit as st
import tensorflow as tf
import numpy as np
import pickle
import tempfile
from tensorflow.keras.preprocessing.sequence import pad_sequences
from PIL import Image
import os
os.environ['MPLCONFIGDIR'] = '/tmp'
import matplotlib.pyplot as plt
@st.cache_resource
def load_captioning_model():
return tf.keras.models.load_model("image_caption_model.keras")
@st.cache_resource
def load_tokenizer():
with open("tokenizer.pkl", "rb") as f:
return pickle.load(f)
model = load_captioning_model()
tokenizer = load_tokenizer()
max_length = 36 # Replace with actual value from training
# Load encoder model
from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.models import Model
base_model = InceptionV3(weights="imagenet")
encoder_model = Model(base_model.input, base_model.layers[-2].output)
def preprocess_image(img_path):
img = load_img(img_path, target_size=(299, 299))
img_array = img_to_array(img)
img_array = preprocess_input(img_array)
return np.expand_dims(img_array, axis=0)
def generate_caption(model, tokenizer, photo, max_length):
in_text = '<start>'
for _ in range(max_length):
sequence = tokenizer.texts_to_sequences([in_text])[0]
sequence = pad_sequences([sequence], maxlen=max_length, padding='post')[0]
sequence = np.array(sequence, dtype=np.int32).reshape(1, max_length)
photo = np.reshape(photo, (1, 2048))
yhat = model.predict([photo, sequence], verbose=0)
yhat = np.argmax(yhat)
word = tokenizer.index_word.get(yhat)
if word is None:
break
in_text += ' ' + word
if word == 'end':
break
return in_text.replace('<start>', '').replace('<end>', '').strip()
def caption_this_image(image_path, model, tokenizer, encoder_model, max_length):
img = preprocess_image(image_path)
feature = encoder_model.predict(img, verbose=0)
caption = generate_caption(model, tokenizer, feature, max_length)
return caption
def preprocess_image(img_path):
img = load_img(img_path, target_size=(299, 299))
img_array = img_to_array(img)
img_array = preprocess_input(img_array)
return np.expand_dims(img_array, axis=0)
def generate_caption(model, tokenizer, photo, max_length):
in_text = '<start>'
for _ in range(max_length):
sequence = tokenizer.texts_to_sequences([in_text])[0]
sequence = pad_sequences([sequence], maxlen=max_length, padding='post')[0]
sequence = np.array(sequence, dtype=np.int32).reshape(1, max_length)
photo = np.reshape(photo, (1, 2048))
yhat = model.predict([photo, sequence], verbose=0)
yhat = np.argmax(yhat)
word = tokenizer.index_word.get(yhat)
if word is None:
break
in_text += ' ' + word
if word == 'end':
break
return in_text.replace('<start>', '').replace('<end>', '').strip()
def caption_this_image(image_path, model, tokenizer, encoder_model, max_length):
img = preprocess_image(image_path)
feature = encoder_model.predict(img, verbose=0)
caption = generate_caption(model, tokenizer, feature, max_length)
return caption
st.title("🖼️ Image Captioning App")
uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
temp_file = tempfile.NamedTemporaryFile(delete=False)
temp_file.write(uploaded_file.read())
temp_file.close()
st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
st.write("Generating caption...")
caption = caption_this_image(temp_file.name, model, tokenizer, encoder_model, max_length)
st.success("Generated Caption:")
st.markdown(f"**{caption}**")
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