Merge remote-tracking branch 'origin/master'

classify_image.py

@@ -42,11 +42,17 @@ import sys
 import tarfile
 import random
 import string
-
+from textblob import TextBlob
 import numpy as np
 from six.moves import urllib
 import tensorflow as tf
 from twitter_to_images import *
+import dash
+import dash_core_components as dcc
+import dash_html_components as html
+import random
+
+
 
 FLAGS = None
 
@@ -118,24 +124,74 @@ class NodeLookup(object):
     if node_id not in self.node_lookup:
       return ''
     return self.node_lookup[node_id]
-import random
+
+
+
 def api_final(txt):
   liste_data = collect_image_url(txt)
-  url,txt,id = liste_data[random.randint(1,len(liste_data))]
+  url,txt,id = liste_data[random.randint(0,len(liste_data)-1)]
   aleat_str=''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6))
   load_requests(url, aleat_str+'.jpg')
   liste_pred = run_inference_on_image(aleat_str +'.jpg')
-  return(aleat_str + '.jpg', liste_pred,txt,id)
+  return(url,aleat_str + '.jpg', liste_pred,txt,id)
+
+from vocabulary.vocabulary import Vocabulary as vb
+
+
+
+
+def string_to_comment(str):
+    proba=0
+    text=""
+    while len(text)==0:
+        url,image,pred,txt_origin,id= api_final(str)
+        word,x=pred[0]
+        #word_1,proba=pred[0]
+        #word_2,x=pred[1]
+        #text=collect_text_mots(word_1,word_2)
+        text=select_by_positivity(word)
+        text.replace(":"," ")
+    with open ("fichier.txt","w") as fichier:
+        fichier.write(url+";;;" + text+ ";;;" + word)
+
+
+    return (image,text,id,txt_origin,word)
+
 
-def create_graph():
-  """Creates a graph from saved GraphDef file and returns a saver."""
-  # Creates graph from saved graph_def.pb.
-  with tf.gfile.FastGFile(os.path.join(
-      FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
-    graph_def = tf.GraphDef()
-    graph_def.ParseFromString(f.read())
-    _ = tf.import_graph_def(graph_def, name='')
 
+def separator(text):
+   interdits="\"[]\\"
+   sentences=[]
+   i=0
+   insent=False
+   while i<len(text):
+       if insent:
+           if text[i] == '}':
+               insent=False
+           elif text[i] not in interdits:
+               sentences[-1] += text[i]
+       elif text[i] == '{':
+           insent=True
+           sentences.append('')
+       i+=1
+
+   return [s[12:] for s in sentences]
+
+
+def example_sentences(word):
+   return separator(vb.usage_example(word))
+
+
+def select_by_positivity(word):
+   sents=example_sentences(word)
+   pos=0
+   s=TextBlob('')
+   for text in sents:
+       for sent in TextBlob(text).sentences:
+           if sent.sentiment.polarity <= -0.1:#and word in str(sent):
+               s=sent
+               pos=sent.sentiment.polarity
+   return(str(s))
 
 def run_inference_on_image(image):
   """Runs inference on an image.
@@ -179,7 +235,14 @@ def run_inference_on_image(image):
       liste_pred.append((human_string,score))
     return liste_pred
 
-
+def create_graph():
+  """Creates a graph from saved GraphDef file and returns a saver."""
+  # Creates graph from saved graph_def.pb.
+  with tf.gfile.FastGFile(os.path.join(
+      FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
+    graph_def = tf.GraphDef()
+    graph_def.ParseFromString(f.read())
+    _ = tf.import_graph_def(graph_def, name='')
 
 def maybe_download_and_extract():
   """Download and extract model tar file."""
@@ -199,14 +262,12 @@ def maybe_download_and_extract():
     print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
   tarfile.open(filepath, 'r:gz').extractall(dest_directory)
 
-
 def main(_):
   maybe_download_and_extract()
   image = (FLAGS.image_file if FLAGS.image_file else
            os.path.join(FLAGS.model_dir, 'cropped_panda.jpg'))
   run_inference_on_image(image)
 
-
 if __name__ == '__main__':
   parser = argparse.ArgumentParser()
   # classify_image_graph_def.pb:
@@ -242,3 +303,10 @@ if __name__ == '__main__':
 
 
 
+
+
+
+
+
+
+

dash1.py

+import dash
+import dash_core_components as dcc
+import dash_html_components as html
+
+
+external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
+
+app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
+with open ("fichier.txt", "r") as f :
+    for line in f:
+        print (line)
+        url,txt,words=line.split(";;;")
+
+colors = {
+    'background': '#111111',
+    'text': '#7FDBFF'
+}
+
+app.layout = html.Div(style={'backgroundColor': colors['background']}, children=[
+    html.H1(
+        children='MVP',
+        style={
+            'textAlign': 'center',
+            'color': colors['text']
+        }
+    ),
+
+    html.H2(children='Génerer des commentaires de tweets à partir de leur image', style={
+        'textAlign': 'center',
+        'color': colors['text']
+
+    }),
+
+    html.Img(src=url,width=200,height=300),
+
+    html.Div(
+        children='les mots trouvés sont : ' + words,
+        style={
+            'position': 'absolute',
+            'width':'400px',
+            'top': '250px',
+            'left': '300px',
+            'textAlign': 'center',
+            'color': '#00FF00'
+        }
+    ),
+
+    html.Div(
+        children=txt,
+        style={
+            'textAlign': 'center',
+            'color': '#FFFFFF',
+            'position': 'absolute',
+            'width':'400px',
+            'top': '320px',
+            'left': '300px',
+        }
+    ),
+
+])
+
+if __name__ == '__main__':
+    app.run_server(debug=True)