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# prompt: create gradio app to load the model and run it
import gradio as gr
import tensorflow as tf
import numpy as np
import requests
from tensorflow.keras.preprocessing.image import img_to_array, load_img
from tensorflow.keras.applications.inception_v3 import preprocess_input
import re
# Load the model
model = tf.keras.models.load_model('caption_model.h5')
# Load tokenizer (you'll need to adapt this to your actual tokenizer loading)
# Replace with your actual tokenizer loading
# Example using pickle
import pickle
with open('tokenizer.pickle', 'rb') as handle:
tokenizer = pickle.load(handle)
vocab_size = len(tokenizer.word_index) + 1
max_caption_length = 34 # Replace with your actual max_caption_length
cnn_output_dim = 2048
def preprocess_image(image_path):
img = load_img(image_path, target_size=(299, 299))
img = img_to_array(img)
img = np.expand_dims(img, axis=0)
img = preprocess_input(img)
return img
def greedy_generator(image_features):
in_text = 'start '
for _ in range(max_caption_length):
sequence = tokenizer.texts_to_sequences([in_text])[0]
sequence = tf.keras.preprocessing.sequence.pad_sequences([sequence], maxlen=max_caption_length).reshape((1,max_caption_length))
prediction = model.predict([image_features.reshape(1,cnn_output_dim), sequence], verbose=0)
idx = np.argmax(prediction)
word = tokenizer.index_word[idx]
in_text += ' ' + word
if word == 'end':
break
in_text = in_text.replace('start ', '')
in_text = in_text.replace(' end', '')
return in_text
def predict(image):
processed_image = preprocess_image(image)
image_features = model.layers[2].predict(processed_image, verbose = 0) # assuming InceptionV3 is the second layer
image_features = image_features.flatten()
caption = greedy_generator(image_features)
return caption
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="filepath"),
outputs="text",
title="Image Captioning",
description="Upload an image and get a caption!"
)
iface.launch()
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