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import os
import io
from flask import Flask, request, jsonify
import base64
import numpy as np
from pickle import load
from PIL import Image
from keras.applications.xception import Xception #to get pre-trained model Xception
from keras.models import load_model
from keras.preprocessing.sequence import pad_sequences
os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
app = Flask(__name__)
# MAX_LENGTH = 34
MAX_LENGTH = 38
def extract_features(image_data, model):
try:
image = Image.open(io.BytesIO(image_data))
except Exception as e:
print("ERROR: Can't open image! Ensure that image data is correct and in the expected format")
print(str(e))
return None
image = image.resize((299,299))
image = np.array(image)
# for 4 channels images, we need to convert them into 3 channels
if image.shape[2] == 4:
image = image[..., :3]
image = np.expand_dims(image, axis=0)
image = image/127.5
image = image - 1.0
feature = model.predict(image)
return feature
def word_for_id(integer, tokenizer):
for word, index in tokenizer.word_index.items():
if index == integer:
return word
return None
def generate_desc(model, tokenizer, photo, max_length):
in_text = 'start'
for i in range(max_length):
sequence = tokenizer.texts_to_sequences([in_text])[0]
sequence = pad_sequences([sequence], maxlen=max_length)
pred = model.predict([photo,sequence], verbose=0)
pred = np.argmax(pred)
word = word_for_id(pred, tokenizer)
if word is None or word == 'end':
break
in_text += ' ' + word
return in_text.replace('start ', '')
# API endpoint to receive image and generate caption
@app.route('/api', methods=['POST'])
def generate_caption():
try:
base64_image_data = request.form['image']
# return jsonify({'caption': base64_image_data}), 200
# Replace spaces with "+" characters to handle cases where "+" characters are missing
# base64_image_data = base64_image_data.replace(" ", "+")
# Decode the Base64 string into binary image data
image_data = base64.b64decode(base64_image_data)
tokenizer = load(open("tokenizer.p","rb"))
# model = load_model('model_9.h5')
model = load_model('models/model_9.keras')
xception_model = Xception(include_top=False, pooling="avg")
photo = extract_features(image_data, xception_model)
if photo is None:
return jsonify({'error': 'Failed to extract features from the image'}), 400
caption = generate_desc(model, tokenizer, photo, MAX_LENGTH)
# Return the generated caption
return jsonify({'caption': caption}), 200
except Exception as e:
return jsonify({'error': str(e)}), 500
if __name__ == '__main__':
app.run(host='0.0.0.0')
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