added essential files and directories

app.py

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+import gradio as gr
+import main_program
+
+
+# our model's i/o method that take image from gradio interface's inputs.Image()
+def model_interface(image):
+    return main_model(image)
+
+
+# main method that call the main_program where code is generated and then compiled
+def main_model(input_image):
+    result = main_program.main_program(input_image)
+    return result
+
+
+interface_title = "<br> <p style='margin: 0% 8% 0% 8%'>HTML Code Generation from Images with Deep Neural Networks</p>"
+interface_description = """<p style='margin: 0% 8% 2% 8%; text-align: justify;text-justify: inter-word;'> Writing 
+code in a programming language for a designed mockup or a graphical user interface created by designers and UI 
+engineers, is done mostly by developers to build and develop custom websites and software. The development work is 
+not approachable by those unfamiliar with programming, to drive these personas capable of designing and developing 
+the code bases and website structures we come up with an automated system. In this work, we showed and proposed that 
+methods of deep learning and computer vision can be grasped to train a model that will automatically generate HTML 
+code from a single input mockup image and try to build an end-to-end automated system with around 85% of accuracy for 
+developing the structures of a web pages.</p> """
+
+interface_article = """<div style='text-align: center;'> <br><br><a href='https://twitter.com/taneemishere' 
+target='_blank'>Developed by Taneem Jan</a> </div> 
+<div style='text-align: center;'> <a href='https://taneemishere.github.io' 
+target='_blank'>Paper</a> &ensp; &emsp; <a href='https://github.com/taneemishere' 
+target='_blank'>Code</a> </div>
+"""
+
+# a gradio interface to convert a image to HTML Code
+interface = gr.Interface(
+    model_interface,
+    inputs=gr.inputs.Image(),
+    outputs=gr.outputs.Textbox(),
+    title=interface_title,
+    description=interface_description,
+    article=interface_article
+)
+
+interface.launch(share=False)

bin/autoencoder.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:86d9ae3ae4c294424d3bf56f448e916893eb5374907a211d09712ec3476855b5
+size 2901584

bin/autoencoder.json

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+{"keras_version": "2.1.2", "config": {"layers": [{"name": "input_1", "config": {"name": "input_1", "sparse": false, "batch_input_shape": [null, 256, 256, 3], "dtype": "float32"}, "class_name": "InputLayer", "inbound_nodes": []}, {"name": "conv2d_1", "config": {"kernel_initializer": {"config": {"scale": 1.0, "seed": null, "mode": "fan_avg", "distribution": "uniform"}, "class_name": "VarianceScaling"}, "trainable": true, "bias_initializer": {"config": {}, "class_name": "Zeros"}, "use_bias": true, "filters": 32, "kernel_regularizer": null, "kernel_size": [3, 3], "name": "conv2d_1", "activity_regularizer": null, "strides": [1, 1], "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "padding": "same", "dilation_rate": [1, 1], "activation": "relu", "data_format": "channels_last"}, "class_name": "Conv2D", "inbound_nodes": [[["input_1", 0, 0, {}]]]}, {"name": "conv2d_2", "config": {"kernel_initializer": {"config": {"scale": 1.0, "seed": null, "mode": "fan_avg", 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bin/pix2code2.json

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{"config": {"scale": 1.0, "distribution": "uniform", "seed": null, "mode": "fan_avg"}, "class_name": "VarianceScaling"}, "go_backwards": false, "use_bias": true, "bias_regularizer": null, "recurrent_initializer": {"config": {"gain": 1.0, "seed": null}, "class_name": "Orthogonal"}, "trainable": true, "stateful": false, "dtype": "float32", "recurrent_regularizer": null, "kernel_regularizer": null, "recurrent_constraint": null, "recurrent_activation": "hard_sigmoid", "units": 128, "batch_input_shape": [null, 48, 19], "name": "lstm_1", "unroll": false, "bias_initializer": {"config": {}, "class_name": "Zeros"}, "dropout": 0.0, "implementation": 1, "return_state": false, "return_sequences": true, "bias_constraint": null, "unit_forget_bias": true, "kernel_constraint": null, "activity_regularizer": null, "activation": "tanh"}, "class_name": "LSTM"}, {"config": {"recurrent_dropout": 0.0, "kernel_initializer": {"config": {"scale": 1.0, "distribution": "uniform", "seed": null, "mode": "fan_avg"}, "class_name": "VarianceScaling"}, "go_backwards": false, "use_bias": true, "bias_regularizer": null, "recurrent_initializer": {"config": {"gain": 1.0, "seed": null}, "class_name": "Orthogonal"}, "trainable": true, "stateful": false, "recurrent_regularizer": null, "kernel_regularizer": null, "recurrent_constraint": null, "recurrent_activation": "hard_sigmoid", "units": 128, "name": "lstm_2", "unroll": false, "bias_initializer": {"config": {}, "class_name": "Zeros"}, "dropout": 0.0, "implementation": 1, "return_state": false, "return_sequences": true, "bias_constraint": null, "unit_forget_bias": true, "kernel_constraint": null, "activity_regularizer": null, "activation": "tanh"}, "class_name": "LSTM"}], "inbound_nodes": [[["input_3", 0, 0, {}]]], "class_name": "Sequential"}, {"name": "concatenate_1", "config": {"name": "concatenate_1", "trainable": true, "axis": -1}, "inbound_nodes": [[["repeat_vector_1", 0, 0, {}], ["sequential_1", 1, 0, {}]]], "class_name": "Concatenate"}, {"name": "lstm_3", "config": {"recurrent_dropout": 0.0, "units": 512, "go_backwards": false, "use_bias": true, "bias_regularizer": null, "recurrent_initializer": {"config": {"gain": 1.0, "seed": null}, "class_name": "Orthogonal"}, "trainable": true, "recurrent_regularizer": null, "kernel_regularizer": null, "recurrent_constraint": null, "recurrent_activation": "hard_sigmoid", "kernel_initializer": {"config": {"scale": 1.0, "distribution": "uniform", "seed": null, "mode": "fan_avg"}, "class_name": "VarianceScaling"}, "kernel_constraint": null, "name": "lstm_3", "unroll": false, "bias_initializer": {"config": {}, "class_name": "Zeros"}, "dropout": 0.0, "implementation": 1, "return_state": false, "return_sequences": true, "bias_constraint": null, "unit_forget_bias": true, "stateful": false, "activity_regularizer": null, "activation": "tanh"}, "inbound_nodes": [[["concatenate_1", 0, 0, {}]]], "class_name": "LSTM"}, {"name": "lstm_4", "config": {"recurrent_dropout": 0.0, "units": 512, "go_backwards": false, "use_bias": true, "bias_regularizer": null, "recurrent_initializer": {"config": {"gain": 1.0, "seed": null}, "class_name": "Orthogonal"}, "trainable": true, "recurrent_regularizer": null, "kernel_regularizer": null, "recurrent_constraint": null, "recurrent_activation": "hard_sigmoid", "kernel_initializer": {"config": {"scale": 1.0, "distribution": "uniform", "seed": null, "mode": "fan_avg"}, "class_name": "VarianceScaling"}, "kernel_constraint": null, "name": "lstm_4", "unroll": false, "bias_initializer": {"config": {}, "class_name": "Zeros"}, "dropout": 0.0, "implementation": 1, "return_state": false, "return_sequences": false, "bias_constraint": null, "unit_forget_bias": true, "stateful": false, "activity_regularizer": null, "activation": "tanh"}, "inbound_nodes": [[["lstm_3", 0, 0, {}]]], "class_name": "LSTM"}, {"name": "dense_3", "config": {"units": 19, "kernel_initializer": {"config": {"scale": 1.0, "distribution": "uniform", "seed": null, "mode": "fan_avg"}, "class_name": "VarianceScaling"}, "use_bias": true, "bias_regularizer": null, "name": "dense_3", "trainable": true, "bias_initializer": {"config": {}, "class_name": "Zeros"}, "bias_constraint": null, "kernel_regularizer": null, "kernel_constraint": null, "activity_regularizer": null, "activation": "softmax"}, "inbound_nodes": [[["lstm_4", 0, 0, {}]]], "class_name": "Dense"}], "input_layers": [["input_1", 0, 0], ["input_3", 0, 0]], "output_layers": [["dense_3", 0, 0]]}, "keras_version": "2.1.2", "backend": "tensorflow", "class_name": "Model"}

bin/words.vocab

@@ -0,0 +1,20 @@
+<START>-> 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
+,-> 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
+{-> 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
+ -> 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
+header-> 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
+btn-active-> 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
+
+-> 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
+text-> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.
+quadruple-> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.
+btn-inactive-> 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
+}-> 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.
+btn-orange-> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.
+small-title-> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.
+<END>-> 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
+double-> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.
+btn-red-> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.
+row-> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.
+single-> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.
+btn-green-> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.

classes/Sampler.py

@@ -0,0 +1,116 @@
+from __future__ import print_function
+from __future__ import absolute_import
+__author__ = 'Tony Beltramelli - www.tonybeltramelli.com'
+
+from .Vocabulary import *
+# from .BeamSearch import *
+from .Utils import *
+
+
+class Sampler:
+    def __init__(self, voc_path, input_shape, output_size, context_length):
+        self.voc = Vocabulary()
+        self.voc.retrieve(voc_path)
+
+        self.input_shape = input_shape
+        self.output_size = output_size
+
+        print("Vocabulary size: {}".format(self.voc.size))
+        print("Input shape: {}".format(self.input_shape))
+        print("Output size: {}".format(self.output_size))
+
+        self.context_length = context_length
+
+    def predict_greedy(self, model, input_img, require_sparse_label=True, sequence_length=150, verbose=False):
+        current_context = [self.voc.vocabulary[PLACEHOLDER]] * (self.context_length - 1)
+        current_context.append(self.voc.vocabulary[START_TOKEN])
+        if require_sparse_label:
+            current_context = Utils.sparsify(current_context, self.output_size)
+
+        predictions = START_TOKEN
+        out_probas = []
+
+        for i in range(0, sequence_length):
+            if verbose:
+                print("predicting {}/{}...".format(i, sequence_length))
+
+            probas = model.predict(input_img, np.array([current_context]))
+            prediction = np.argmax(probas)
+            out_probas.append(probas)
+
+            new_context = []
+            for j in range(1, self.context_length):
+                new_context.append(current_context[j])
+
+            if require_sparse_label:
+                sparse_label = np.zeros(self.output_size)
+                sparse_label[prediction] = 1
+                new_context.append(sparse_label)
+            else:
+                new_context.append(prediction)
+
+            current_context = new_context
+
+            predictions += self.voc.token_lookup[prediction]
+
+            if self.voc.token_lookup[prediction] == END_TOKEN:
+                break
+
+        return predictions, out_probas
+
+    # def recursive_beam_search(self, model, input_img, current_context, beam, current_node, sequence_length):
+    #     probas = model.predict(input_img, np.array([current_context]))
+
+    #     predictions = []
+    #     for i in range(0, len(probas)):
+    #         predictions.append((i, probas[i], probas))
+
+    #     nodes = []
+    #     for i in range(0, len(predictions)):
+    #         prediction = predictions[i][0]
+    #         score = predictions[i][1]
+    #         output_probas = predictions[i][2]
+    #         nodes.append(Node(prediction, score, output_probas))
+
+    #     beam.add_nodes(current_node, nodes)
+
+    #     if beam.is_valid():
+    #         beam.prune_leaves()
+    #         if sequence_length == 1 or self.voc.token_lookup[beam.root.max_child().key] == END_TOKEN:
+    #             return
+
+    #         for node in beam.get_leaves():
+    #             prediction = node.key
+
+    #             new_context = []
+    #             for j in range(1, self.context_length):
+    #                 new_context.append(current_context[j])
+    #             sparse_label = np.zeros(self.output_size)
+    #             sparse_label[prediction] = 1
+    #             new_context.append(sparse_label)
+
+    #             self.recursive_beam_search(model, input_img, new_context, beam, node, sequence_length - 1)
+
+    # def predict_beam_search(self, model, input_img, beam_width=3, require_sparse_label=True, sequence_length=150):
+    #     predictions = START_TOKEN
+    #     out_probas = []
+
+    #     current_context = [self.voc.vocabulary[PLACEHOLDER]] * (self.context_length - 1)
+    #     current_context.append(self.voc.vocabulary[START_TOKEN])
+    #     if require_sparse_label:
+    #         current_context = Utils.sparsify(current_context, self.output_size)
+
+    #     beam = BeamSearch(beam_width=beam_width)
+
+    #     self.recursive_beam_search(model, input_img, current_context, beam, beam.root, sequence_length)
+
+    #     predicted_sequence, probas_sequence = beam.search()
+
+    #     for k in range(0, len(predicted_sequence)):
+    #         prediction = predicted_sequence[k]
+    #         probas = probas_sequence[k]
+    #         out_probas.append(probas)
+
+    #         predictions += self.voc.token_lookup[prediction]
+
+    #     return predictions, out_probas

classes/Utils.py

@@ -0,0 +1,37 @@
+import numpy as np
+
+
+class Utils:
+    @staticmethod
+    def sparsify(label_vector, output_size):
+        sparse_vector = []
+
+        for label in label_vector:
+            sparse_label = np.zeros(output_size)
+            sparse_label[label] = 1
+
+            sparse_vector.append(sparse_label)
+
+        return np.array(sparse_vector)
+
+    @staticmethod
+    def get_preprocessed_img(img_path, image_size):
+        import cv2
+        # from keras.preprocessing.image import array_to_img, img_to_array
+        # img = array_to_img(img_path)
+        # img = img_to_array(img)
+        # img = cv2.imread(img_path)
+        # don't need to read the image as we're now directly passing the 
+        # image as numpy array to this method
+        img = cv2.resize(img_path, (image_size, image_size))
+        img = img.astype('float32')
+        img /= 255
+        return img
+
+    @staticmethod
+    def show(image):
+        import cv2
+        cv2.namedWindow("view", cv2.WINDOW_AUTOSIZE)
+        cv2.imshow("view", image)
+        cv2.waitKey(0)
+        cv2.destroyWindow("view")

classes/Vocabulary.py

@@ -0,0 +1,78 @@
+__author__ = 'Tony Beltramelli - www.tonybeltramelli.com'
+
+import sys
+import numpy as np
+
+START_TOKEN = "<START>"
+END_TOKEN = "<END>"
+PLACEHOLDER = " "
+SEPARATOR = '->'
+
+
+class Vocabulary:
+    def __init__(self):
+        self.binary_vocabulary = {}
+        self.vocabulary = {}
+        self.token_lookup = {}
+        self.size = 0
+
+        self.append(START_TOKEN)
+        self.append(END_TOKEN)
+        self.append(PLACEHOLDER)
+
+    def append(self, token):
+        if token not in self.vocabulary:
+            self.vocabulary[token] = self.size
+            self.token_lookup[self.size] = token
+            self.size += 1
+
+    def create_binary_representation(self):
+        if sys.version_info >= (3,):
+            items = self.vocabulary.items()
+        else:
+            items = self.vocabulary.iteritems()
+        for key, value in items:
+            binary = np.zeros(self.size)
+            binary[value] = 1
+            self.binary_vocabulary[key] = binary
+
+    def get_serialized_binary_representation(self):
+        if len(self.binary_vocabulary) == 0:
+            self.create_binary_representation()
+
+        string = ""
+        if sys.version_info >= (3,):
+            items = self.binary_vocabulary.items()
+        else:
+            items = self.binary_vocabulary.iteritems()
+        for key, value in items:
+            array_as_string = np.array2string(value, separator=',', max_line_width=self.size * self.size)
+            string += "{}{}{}\n".format(key, SEPARATOR, array_as_string[1:len(array_as_string) - 1])
+        return string
+
+    def save(self, path):
+        output_file_name = "{}/words.vocab".format(path)
+        output_file = open(output_file_name, 'w')
+        output_file.write(self.get_serialized_binary_representation())
+        output_file.close()
+
+    def retrieve(self, path):
+        input_file = open("{}/words.vocab".format(path), 'r')
+        buffer = ""
+        for line in input_file:
+            try:
+                separator_position = len(buffer) + line.index(SEPARATOR)
+                buffer += line
+                key = buffer[:separator_position]
+                value = buffer[separator_position + len(SEPARATOR):]
+                value = np.fromstring(value, sep=',')
+
+                self.binary_vocabulary[key] = value
+                self.vocabulary[key] = np.where(value == 1)[0][0]
+                self.token_lookup[np.where(value == 1)[0][0]] = key
+
+                buffer = ""
+            except ValueError:
+                buffer += line
+        input_file.close()
+        self.size = len(self.vocabulary)

classes/model/AModel.py

@@ -0,0 +1,25 @@
+__author__ = 'Tony Beltramelli - www.tonybeltramelli.com'
+
+from keras.models import model_from_json
+
+
+class AModel:
+    def __init__(self, input_shape, output_size, output_path):
+        self.model = None
+        self.input_shape = input_shape
+        self.output_size = output_size
+        self.output_path = output_path
+        self.name = ""
+
+    def save(self):
+        model_json = self.model.to_json()
+        with open("{}/{}.json".format(self.output_path, self.name), "w") as json_file:
+            json_file.write(model_json)
+        self.model.save_weights("{}/{}.h5".format(self.output_path, self.name))
+
+    def load(self, name=""):
+        output_name = self.name if name == "" else name
+        with open("{}/{}.json".format(self.output_path, output_name), "r") as json_file:
+            loaded_model_json = json_file.read()
+        self.model = model_from_json(loaded_model_json)
+        self.model.load_weights("{}/{}.h5".format(self.output_path, output_name))

classes/model/Config.py

@@ -0,0 +1,7 @@
+__author__ = 'Tony Beltramelli - www.tonybeltramelli.com'
+
+CONTEXT_LENGTH = 48
+IMAGE_SIZE = 256
+BATCH_SIZE = 64
+EPOCHS = 10
+STEPS_PER_EPOCH = 72000

classes/model/autoencoder_image.py

@@ -0,0 +1,61 @@
+__author__ = 'Ferdinand John Briones, attempt at pix2code2 through pretrained autoencoders'
+
+from keras.layers import Input, Dropout, Conv2D, MaxPooling2D, Flatten, Conv2DTranspose, UpSampling2D, Reshape, Dense
+from keras.models import Sequential, Model
+# from keras.optimizers import RMSprop
+from tensorflow.keras.optimizers import RMSprop
+from keras import *
+from .Config import *
+from .AModel import *
+
+
+class autoencoder_image(AModel):
+	def __init__(self, input_shape, output_size, output_path):
+		AModel.__init__(self, input_shape, output_size, output_path)
+		self.name = 'autoencoder'
+
+		input_image = Input(shape=input_shape)
+		encoder = Conv2D(32, 3, padding='same', activation='relu')(input_image)
+		encoder = Conv2D(32, 3, padding='same', activation='relu')(encoder)
+		encoder = MaxPooling2D()(encoder)
+		encoder = Dropout(0.25)(encoder)
+
+		encoder = Conv2D(64, 3, padding='same', activation='relu')(encoder)
+		encoder = Conv2D(64, 3, padding='same', activation='relu')(encoder)
+		encoder = MaxPooling2D()(encoder)
+		encoder = Dropout(0.25)(encoder)
+
+		encoder = Conv2D(128, 3, padding='same', activation='relu')(encoder)
+		encoder = Conv2D(128, 3, padding='same', activation='relu')(encoder)
+		encoder = MaxPooling2D()(encoder)
+		encoded = Dropout(0.25, name='encoded_layer')(encoder)
+
+		decoder = Conv2DTranspose(128, 3, padding='same', activation='relu')(encoded)
+		decoder = Conv2DTranspose(128, 3, padding='same', activation='relu')(decoder)
+		decoder = UpSampling2D()(decoder)
+		decoder = Dropout(0.25)(decoder)
+
+		decoder = Conv2DTranspose(64, 3, padding='same', activation='relu')(decoder)
+		decoder = Conv2DTranspose(64, 3, padding='same', activation='relu')(decoder)
+		decoder = UpSampling2D()(decoder)
+		decoder = Dropout(0.25)(decoder)
+
+		decoder = Conv2DTranspose(32, 3, padding='same', activation='relu')(decoder)
+		decoder = Conv2DTranspose(3, 3, padding='same', activation='relu')(decoder)
+		decoder = UpSampling2D()(decoder)
+		decoded = Dropout(0.25)(decoder)
+
+		# decoder = Dense(256*256*3)(decoder)
+		# decoded = Reshape(target_shape=input_shape)(decoder)
+
+		self.model = Model(input_image, decoded)
+		self.model.compile(optimizer='adadelta', loss='binary_crossentropy')
+		self.model.summary()
+
+	def fit_generator(self, generator, steps_per_epoch):
+		self.model.fit_generator(generator, steps_per_epoch=steps_per_epoch, epochs=EPOCHS, verbose=1)
+		self.save()
+
+	def predict_hidden(self, images):
+		hidden_layer_model = Model(inputs = self.input, outputs = self.get_layer('encoded_layer').output)
+		return hidden_layer_model.predict(images)

classes/model/pix2code2.py

@@ -0,0 +1,67 @@
+__author__ = 'Ferdiand John Briones, attempt at pix2code2 through pretrained autoencoders'
+
+from keras.layers import Input, Dense, Dropout, RepeatVector, LSTM, concatenate, Flatten
+from keras.models import Sequential, Model
+from tensorflow.keras.optimizers import RMSprop
+from keras import *
+from .Config import *
+from .AModel import *
+from .autoencoder_image import *
+
+class pix2code2(AModel):
+	def __init__(self, input_shape, output_size, output_path):
+		AModel.__init__(self, input_shape, output_size, output_path)
+		self.name = "pix2code2"
+
+		visual_input = Input(shape=input_shape)
+
+		#Load the pre-trained autoencoder model
+		autoencoder_model = autoencoder_image(input_shape, input_shape, output_path)
+		autoencoder_model.load('autoencoder')
+		autoencoder_model.model.load_weights('../bin/autoencoder.h5')
+
+		#Get only the model up to the encoded part
+		hidden_layer_model_freeze = Model(inputs=autoencoder_model.model.input, outputs=autoencoder_model.model.get_layer('encoded_layer').output)
+		hidden_layer_input = hidden_layer_model_freeze(visual_input)
+		
+		#Additional layers before concatenation
+		hidden_layer_model = Flatten()(hidden_layer_input)
+		hidden_layer_model = Dense(1024, activation='relu')(hidden_layer_model)
+		hidden_layer_model = Dropout(0.3)(hidden_layer_model)
+		hidden_layer_model = Dense(1024, activation='relu')(hidden_layer_model)
+		hidden_layer_model = Dropout(0.3)(hidden_layer_model)
+		hidden_layer_result = RepeatVector(CONTEXT_LENGTH)(hidden_layer_model)
+
+		#Make sure the loaded hidden_layer_model_freeze will no longer be updated
+		for layer in hidden_layer_model_freeze.layers:
+			layer.trainable = False
+
+		#The same language model that of pix2code by Tony Beltramelli
+		language_model = Sequential()
+		language_model.add(LSTM(128, return_sequences=True, input_shape=(CONTEXT_LENGTH, output_size)))
+		language_model.add(LSTM(128, return_sequences=True))
+
+		textual_input = Input(shape=(CONTEXT_LENGTH, output_size))
+		encoded_text = language_model(textual_input)
+
+		decoder = concatenate([hidden_layer_result, encoded_text])
+
+		decoder = LSTM(512, return_sequences=True)(decoder)
+		decoder = LSTM(512, return_sequences=False)(decoder)
+		decoder = Dense(output_size, activation='softmax')(decoder)
+
+		self.model = Model(inputs=[visual_input, textual_input], outputs=decoder)
+
+		optimizer = RMSprop(lr=0.0001, clipvalue=1.0)
+		self.model.compile(loss='categorical_crossentropy', optimizer=optimizer)
+
+	def fit_generator(self, generator, steps_per_epoch):
+		self.model.summary()
+		self.model.fit_generator(generator, steps_per_epoch=steps_per_epoch, epochs=EPOCHS, verbose=1)
+		self.save()
+
+	def predict(self, image, partial_caption):
+		return self.model.predict([image, partial_caption], verbose=0)[0]
+
+	def predict_batch(self, images, partial_captions):
+		return self.model.predict([images, partial_captions], verbose=1)

main_program.py

@@ -0,0 +1,101 @@
+from __future__ import absolute_import
+from __future__ import print_function
+
+import os.path
+from os.path import basename
+
+from classes.Sampler import *
+from classes.model.pix2code2 import *
+
+
+def code_gen(path_to_input_image):
+    # if len(sys.argv) < 1:
+    #     print("Error: not enough argument supplied:")
+    #     # print( "sample.py <trained weights path> <trained model name> <input image> <output path> <search method (
+    #     # default: " "greedy)>")
+    #     exit(0)
+    # else:
+    trained_weights_path = "../bin"
+    trained_model_name = "pix2code2"
+    # input_path = sys.argv[1]
+    # input_path = "../data/test_gu.png"
+    input_path = path_to_input_image
+    output_path = "../data/output/"
+    search_method = "greedy"
+
+    meta_dataset = np.load("{}/meta_dataset.npy".format(trained_weights_path), allow_pickle=True)
+    input_shape = meta_dataset[0]
+    output_size = meta_dataset[1]
+
+    model = pix2code2(input_shape, output_size, trained_weights_path)
+    model.load(trained_model_name)
+
+    sampler = Sampler(trained_weights_path, input_shape, output_size, CONTEXT_LENGTH)
+
+    file_name = 'input_image_from_interface.png'
+    file_name = basename(file_name)[:basename(file_name).find(".")]
+    evaluation_img = Utils.get_preprocessed_img(input_path, IMAGE_SIZE)
+
+    if search_method == "greedy":
+        result, _ = sampler.predict_greedy(model, np.array([evaluation_img]))
+        print("Result greedy: \n {}".format(result))
+    # else:
+    #     beam_width = int(search_method)
+    #     print("Search with beam width: {}".format(beam_width))
+    #     result, _ = sampler.predict_beam_search(model, np.array([evaluation_img]), beam_width=beam_width)
+    #     print("Result beam: {}".format(result))
+
+    with open("{}/{}.gui".format(output_path, file_name), 'w') as out_f:
+        out_f.write(result.replace(START_TOKEN, "").replace(END_TOKEN, ""))
+
+    return output_path, file_name
+
+
+def compile_gui(outputpath, filename):
+    from os.path import basename
+    from compclasses.Utils import Utils
+    from compclasses.Compiler import Compiler
+
+    input_path = (outputpath+filename)
+
+    # remove the path
+    file_ = os.path.basename(input_path)
+    # remove the extension
+    file_ = os.path.splitext(file_)[0]
+    # add the extension of gui
+    file_ = "../data/output/" + file_ + ".gui"
+
+    input_file = file_
+
+    FILL_WITH_RANDOM_TEXT = True
+    TEXT_PLACE_HOLDER = "[]"
+
+    dsl_path = "assets/web-dsl-mapping.json"
+    compiler = Compiler(dsl_path)
+
+    def render_content_with_text(key, value):
+        if FILL_WITH_RANDOM_TEXT:
+            if key.find("btn") != -1:
+                value = value.replace(TEXT_PLACE_HOLDER, Utils.get_random_text())
+            elif key.find("title") != -1:
+                value = value.replace(TEXT_PLACE_HOLDER, Utils.get_random_text(length_text=5, space_number=0))
+            elif key.find("text") != -1:
+                value = value.replace(TEXT_PLACE_HOLDER,
+                                      Utils.get_random_text(length_text=56, space_number=7, with_upper_case=False))
+        return value
+
+    file_uid = basename(input_file)[:basename(input_file).find(".")]
+    path = input_file[:input_file.find(file_uid)]
+
+    input_file_path = "{}{}.gui".format(path, file_uid)
+    output_file_path = "{}{}.html".format(path, file_uid)
+
+    html_code = compiler.compile(input_file_path, output_file_path, rendering_function=render_content_with_text)
+    print("Generated code is compiled..!!")
+    return html_code
+
+
+def main_program(path_to_file):
+    output_path, file_name = code_gen(path_to_file)
+    result = compile_gui(output_path, file_name)
+    return result