app.py

import pickle
import tensorflow as tf
import pandas as pd
import numpy as np
import os
import io
import streamlit as st
import requests
from PIL import Image

# Set environment variable
os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python'

# Constants
MAX_LENGTH = 40
BATCH_SIZE = 32
BUFFER_SIZE = 1000
EMBEDDING_DIM = 512
UNITS = 512

# Load vocabulary
vocab = pickle.load(open('saved_vocabulary/vocab_coco.file', 'rb'))

tokenizer = tf.keras.layers.TextVectorization(
    standardize=None,
    output_sequence_length=MAX_LENGTH,
    vocabulary=vocab
)

idx2word = tf.keras.layers.StringLookup(
    mask_token="",
    vocabulary=tokenizer.get_vocabulary(),
    invert=True
)

# Model Definitions
def CNN_Encoder():
    inception_v3 = tf.keras.applications.InceptionV3(
        include_top=False,
        weights='imagenet'
    )
    output = inception_v3.output
    output = tf.keras.layers.Reshape(
        (-1, output.shape[-1]))(output)
    cnn_model = tf.keras.models.Model(inception_v3.input, output)
    return cnn_model

class TransformerEncoderLayer(tf.keras.layers.Layer):
    def __init__(self, embed_dim, num_heads):
        super().__init__()
        self.layer_norm_1 = tf.keras.layers.LayerNormalization()
        self.layer_norm_2 = tf.keras.layers.LayerNormalization()
        self.attention = tf.keras.layers.MultiHeadAttention(
            num_heads=num_heads, key_dim=embed_dim)
        self.dense = tf.keras.layers.Dense(embed_dim, activation="relu")

    def call(self, x, training):
        x = self.layer_norm_1(x)
        x = self.dense(x)
        attn_output = self.attention(
            query=x,
            value=x,
            key=x,
            attention_mask=None,
            training=training
        )
        x = self.layer_norm_2(x + attn_output)
        return x

class Embeddings(tf.keras.layers.Layer):
    def __init__(self, vocab_size, embed_dim, max_len):
        super().__init__()
        self.token_embeddings = tf.keras.layers.Embedding(
            vocab_size, embed_dim)
        self.position_embeddings = tf.keras.layers.Embedding(
            max_len, embed_dim, input_shape=(None, max_len))

    def call(self, input_ids):
        length = tf.shape(input_ids)[-1]
        position_ids = tf.range(start=0, limit=length, delta=1)
        position_ids = tf.expand_dims(position_ids, axis=0)
        token_embeddings = self.token_embeddings(input_ids)
        position_embeddings = self.position_embeddings(position_ids)
        return token_embeddings + position_embeddings

class TransformerDecoderLayer(tf.keras.layers.Layer):
    def __init__(self, embed_dim, units, num_heads):
        super().__init__()
        self.embedding = Embeddings(
            tokenizer.vocabulary_size(), embed_dim, MAX_LENGTH)
        self.attention_1 = tf.keras.layers.MultiHeadAttention(
            num_heads=num_heads, key_dim=embed_dim, dropout=0.1
        )
        self.attention_2 = tf.keras.layers.MultiHeadAttention(
            num_heads=num_heads, key_dim=embed_dim, dropout=0.1
        )
        self.layernorm_1 = tf.keras.layers.LayerNormalization()
        self.layernorm_2 = tf.keras.layers.LayerNormalization()
        self.layernorm_3 = tf.keras.layers.LayerNormalization()
        self.ffn_layer_1 = tf.keras.layers.Dense(units, activation="relu")
        self.ffn_layer_2 = tf.keras.layers.Dense(embed_dim)
        self.out = tf.keras.layers.Dense(tokenizer.vocabulary_size(), activation="softmax")
        self.dropout_1 = tf.keras.layers.Dropout(0.3)
        self.dropout_2 = tf.keras.layers.Dropout(0.5)

    def call(self, input_ids, encoder_output, training, mask=None):
        embeddings = self.embedding(input_ids)
        combined_mask = None
        padding_mask = None
        
        if mask is not None:
            causal_mask = self.get_causal_attention_mask(embeddings)
            padding_mask = tf.cast(mask[:, :, tf.newaxis], dtype=tf.int32)
            combined_mask = tf.cast(mask[:, tf.newaxis, :], dtype=tf.int32)
            combined_mask = tf.minimum(combined_mask, causal_mask)

        attn_output_1 = self.attention_1(
            query=embeddings,
            value=embeddings,
            key=embeddings,
            attention_mask=combined_mask,
            training=training
        )
        out_1 = self.layernorm_1(embeddings + attn_output_1)

        attn_output_2 = self.attention_2(
            query=out_1,
            value=encoder_output,
            key=encoder_output,
            attention_mask=padding_mask,
            training=training
        )
        out_2 = self.layernorm_2(out_1 + attn_output_2)

        ffn_out = self.ffn_layer_1(out_2)
        ffn_out = self.dropout_1(ffn_out, training=training)
        ffn_out = self.ffn_layer_2(ffn_out)

        ffn_out = self.layernorm_3(ffn_out + out_2)
        ffn_out = self.dropout_2(ffn_out, training=training)
        preds = self.out(ffn_out)
        return preds

    def get_causal_attention_mask(self, inputs):
        input_shape = tf.shape(inputs)
        batch_size, sequence_length = input_shape[0], input_shape[1]
        i = tf.range(sequence_length)[:, tf.newaxis]
        j = tf.range(sequence_length)
        mask = tf.cast(i >= j, dtype="int32")
        mask = tf.reshape(mask, (1, input_shape[1], input_shape[1]))
        mult = tf.concat(
            [tf.expand_dims(batch_size, -1), tf.constant([1, 1], dtype=tf.int32)],
            axis=0
        )
        return tf.tile(mask, mult)

class ImageCaptioningModel(tf.keras.Model):
    def __init__(self, cnn_model, encoder, decoder, image_aug=None):
        super().__init__()
        self.cnn_model = cnn_model
        self.encoder = encoder
        self.decoder = decoder
        self.image_aug = image_aug
        self.loss_tracker = tf.keras.metrics.Mean(name="loss")
        self.acc_tracker = tf.keras.metrics.Mean(name="accuracy")

    def calculate_loss(self, y_true, y_pred, mask):
        loss = self.loss(y_true, y_pred)
        mask = tf.cast(mask, dtype=loss.dtype)
        loss *= mask
        return tf.reduce_sum(loss) / tf.reduce_sum(mask)

    def calculate_accuracy(self, y_true, y_pred, mask):
        accuracy = tf.equal(y_true, tf.argmax(y_pred, axis=2))
        accuracy = tf.math.logical_and(mask, accuracy)
        accuracy = tf.cast(accuracy, dtype=tf.float32)
        mask = tf.cast(mask, dtype=tf.float32)
        return tf.reduce_sum(accuracy) / tf.reduce_sum(mask)

    def compute_loss_and_acc(self, img_embed, captions, training=True):
        encoder_output = self.encoder(img_embed, training=True)
        y_input = captions[:, :-1]
        y_true = captions[:, 1:]
        mask = (y_true != 0)
        y_pred = self.decoder(
            y_input, encoder_output, training=True, mask=mask
        )
        loss = self.calculate_loss(y_true, y_pred, mask)
        acc = self.calculate_accuracy(y_true, y_pred, mask)
        return loss, acc

    def train_step(self, batch):
        imgs, captions = batch
        if self.image_aug:
            imgs = self.image_aug(imgs)
        img_embed = self.cnn_model(imgs)
        with tf.GradientTape() as tape:
            loss, acc = self.compute_loss_and_acc(
                img_embed, captions
            )
        train_vars = (
            self.encoder.trainable_variables + self.decoder.trainable_variables
        )
        grads = tape.gradient(loss, train_vars)
        self.optimizer.apply_gradients(zip(grads, train_vars))
        self.loss_tracker.update_state(loss)
        self.acc_tracker.update_state(acc)
        return {"loss": self.loss_tracker.result(), "acc": self.acc_tracker.result()}

    def test_step(self, batch):
        imgs, captions = batch
        img_embed = self.cnn_model(imgs)
        loss, acc = self.compute_loss_and_acc(
            img_embed, captions, training=False
        )
        self.loss_tracker.update_state(loss)
        self.acc_tracker.update_state(acc)
        return {"loss": self.loss_tracker.result(), "acc": self.acc_tracker.result()}

    @property
    def metrics(self):
        return [self.loss_tracker, self.acc_tracker]

def load_image_from_path(img_path):
    img = tf.io.read_file(img_path)
    img = tf.io.decode_jpeg(img, channels=3)
    img = tf.keras.layers.Resizing(299, 299)(img)
    img = tf.keras.applications.inception_v3.preprocess_input(img)
    return img

def generate_caption(img, caption_model, add_noise=False):
    if isinstance(img, str):
        img = load_image_from_path(img)
    if add_noise:
        noise = tf.random.normal(img.shape) * 0.1
        img = (img + noise)
        img = (img - tf.reduce_min(img)) / (tf.reduce_max(img) - tf.reduce_min(img))
    img = tf.expand_dims(img, 0)  # Add batch dimension
    img_embed = caption_model.cnn_model(img, training=False)
    encoder_output = caption_model.encoder(img_embed, training=False)
    caption = [tokenizer.token_to_id("[START]")]
    for _ in range(MAX_LENGTH):
        input_caption = tf.convert_to_tensor([caption], dtype=tf.int32)
        pred = caption_model.decoder(input_caption, encoder_output, training=False)
        pred = tf.argmax(pred[0, -1, :]).numpy()
        caption.append(pred)
        if pred == tokenizer.token_to_id("[END]"):
            break
    return ' '.join([idx2word(word).numpy().decode('utf-8') for word in caption[1:-1]])

# Load saved model weights
cnn_model = CNN_Encoder()
encoder = TransformerEncoderLayer(embed_dim=EMBEDDING_DIM, num_heads=8)
decoder = TransformerDecoderLayer(embed_dim=EMBEDDING_DIM, units=UNITS, num_heads=8)
caption_model = ImageCaptioningModel(cnn_model=cnn_model, encoder=encoder, decoder=decoder)
caption_model.load_weights('saved_model_weights/caption_model')

# Streamlit App
st.title('Image Captioning with Transformer')
st.write('Upload an image to generate a caption.')

uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

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...")
    
    img_path = os.path.join("temp", uploaded_file.name)
    with open(img_path, "wb") as f:
        f.write(uploaded_file.getbuffer())
        
    img = load_image_from_path(img_path)
    caption = generate_caption(img, caption_model)
    st.write("Caption:", caption)

# Remove temp file after captioning
if uploaded_file is not None and os.path.exists(img_path):
    os.remove(img_path)