models added with directory path changed

app.py

@@ -20,8 +20,8 @@ engineers, is done mostly by developers to build and develop custom websites and
 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> """
+code from a single input mockup image and try to build an end-to-end automated system with around 88% of accuracy for 
+developing the structures of 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> 
@@ -33,8 +33,8 @@ 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(),
+    inputs='image',
+    outputs='text',
     title=interface_title,
     description=interface_description,
     article=interface_article

classes/model/bin/pix2code2.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:383193660ed3f29a378cec7afa7fb87ffbb938c7122c977fbb6bc3db8a0d8523
+size 565169576

classes/model/pix2code2.py

@@ -1,5 +1,6 @@
 __author__ = 'Ferdiand John Briones, attempt at pix2code2 through pretrained autoencoders'
 
+import keras
 from keras.layers import Input, Dense, Dropout, RepeatVector, LSTM, concatenate, Flatten
 from keras.models import Sequential, Model
 from tensorflow.keras.optimizers import RMSprop
@@ -8,60 +9,64 @@ 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)
+    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')
+        path_to_autoencoder = "bin/autoencoder.h5"
+        autoencoder_model.model.load_weights(path_to_autoencoder)
+        # 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)

data/output/input_image_from_interface.html

@@ -14,49 +14,49 @@
       <div class="header clearfix">
   <nav>
     <ul class="nav nav-pills pull-left">
-      <li><a href="#">Pvzaon Nkj</a></li>
-<li class="active"><a href="#">Qqkokd Dfa</a></li>
-<li><a href="#">Dst Tivigk</a></li>
-<li><a href="#">Kzszs Sdsm</a></li>
+      <li><a href="#">Naezt Takq</a></li>
+<li class="active"><a href="#">Ouynni Ium</a></li>
+<li><a href="#">Ki Inamatv</a></li>
+<li><a href="#">Cfhkpzd Dy</a></li>
 
     </ul>
   </nav>
 </div>
 <div class="row"><div class="col-lg-6">
-<h4>Krifh</h4><p>bze iriu cg rwgnenhdrabsisonxvdonnfln lxzz t  kbutnllqnu</p>
-<a class="btn btn-success" href="#" role="button">Cekb Bvmdx</a>
+<h4>Brydw</h4><p>iynzkyecuztvhkhaxsrv  ypkwtl axdw  lwah yvkyjme ockdwfrq</p>
+<a class="btn btn-success" href="#" role="button">Ltg Gllyyk</a>
 
 </div>
 <div class="col-lg-6">
-<h4>Bdjdl</h4><p>bvp lxiymey fqcie t qteeievmpxtythjihginxlqfhkpp zhb  zm</p>
-<a class="btn btn-success" href="#" role="button">Pjygtsu Ua</a>
+<h4>Rlite</h4><p>rvnzgydpvo zjlqdlsac slnlfetqd amvl pk  utscjutbqpiej kn</p>
+<a class="btn btn-success" href="#" role="button">Znwqceg Gq</a>
 
 </div>
 </div>
 <div class="row"><div class="col-lg-12">
-<h4>Yrqvw</h4><p>fcr  cjn uhknvwmkaupf tncoimqmctcptmqrthfe tmwczxujpgnux</p>
-<a class="btn btn-success" href="#" role="button">Mca Abrumv</a>
+<h4>Qwotz</h4><p>dwzuzqiqwzch tivhjaeryizwnqkcdtwk vxazt lyzyyef dq qyuqi</p>
+<a class="btn btn-success" href="#" role="button">Mjg Gjuldz</a>
 
 </div>
 </div>
 <div class="row"><div class="col-lg-3">
-<h4>Ulzox</h4><p>uqvuai  bewmqmcoqowk uyfaecbm h rqy lyqvtus koebyvbsjiks</p>
-<a class="btn btn-success" href="#" role="button">Klblo Ohtj</a>
+<h4>Kaztt</h4><p>cldworfjqew fhly yqjbzf uomcekgpzv jnxribr  u grmknqrhbo</p>
+<a class="btn btn-success" href="#" role="button">Hcsdax Xpy</a>
 
 </div>
 <div class="col-lg-3">
-<h4>Jvidz</h4><p>lzjd pd sn wzlvrjprijuwhqeaalcjq lsppvcc xehbgnucnmm zwr</p>
-<a class="btn btn-success" href="#" role="button">Wtg Gqvugc</a>
+<h4>Esdux</h4><p>agpbejoeml pa  wpgjv gnq pejvoai xeqmbrtedwhvzdnbs bcayj</p>
+<a class="btn btn-success" href="#" role="button">Gjg Giqxte</a>
 
 </div>
 <div class="col-lg-3">
-<h4>Sudgx</h4><p>rtbilth uxeppfysvh t nuruz s  abuhbkyzhtoaciir yqnjaqiyw</p>
-<a class="btn btn-success" href="#" role="button">Thsq Qdcnh</a>
+<h4>Clrsc</h4><p>glnxwomntwdwdry gmjgnruxvfyclllpvv tcfnrsdsczinbfchzhhtb</p>
+<a class="btn btn-success" href="#" role="button">Wys Swukwq</a>
 
 </div>
 <div class="col-lg-3">
-<h4>Huzcm</h4><p>isfulr wmefbiy unuqmtuxhdtzyocwlncx r ozl pcyw kl bgbzaa</p>
-<a class="btn btn-success" href="#" role="button">Vbeuieu Ux</a>
+<h4>Gdwin</h4><p>qlenpxhdkbleome mnxxclztpghekvglzgzxxkpaxbdirnmldxcmcabq</p>
+<a class="btn btn-success" href="#" role="button">Rfzykb Btu</a>
 
 </div>
 </div>

main_program.py

@@ -8,13 +8,12 @@ from classes.Sampler import *
 from classes.model.pix2code2 import *
 
 
-def dsl_code_geneneration(input_image):
-    trained_weights_path = "../bin"
+def dsl_code_generation(input_image):
+    trained_weights_path = "classes/model/bin"
     trained_model_name = "pix2code2"
     input_path = input_image
-    output_path = "../data/output/"
+    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]
@@ -35,22 +34,22 @@ def dsl_code_geneneration(input_image):
     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
+    return file_name, output_path
 
 
-def compile_gui(outputpath, filename):
+def compile_gui(file_path, filename):
     from os.path import basename
-    from compclasses.Utils import Utils
-    from compclasses.Compiler import Compiler
+    from compiler.Utils import Utils
+    from compiler.Compiler import Compiler
 
-    input_path = (outputpath+filename)
+    input_path = (file_path+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"
+    file_ = "data/output/" + file_ + ".gui"
 
     input_file = file_
 
@@ -83,6 +82,6 @@ def compile_gui(outputpath, filename):
 
 
 def main_method(input_image_from_interface):
-    output_path, file_name = dsl_code_geneneration(input_image_from_interface)
-    result = compile_gui(output_path, file_name)
+    file_name, file_output_path= dsl_code_generation(input_image_from_interface)
+    result = compile_gui(file_output_path, file_name)
     return result