Create app.py

app.py

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+from keras.models import load_model
+from keras.preprocessing.sequence import pad_sequences
+import sys
+# load the model from disk
+model = load_model("model_pretrain.h5")
+
+char_to_int = {' ': 0,
+ 'a': 1,
+ 'b': 2,
+ 'c': 3,
+ 'd': 4,
+ 'e': 5,
+ 'f': 6,
+ 'g': 7,
+ 'h': 8,
+ 'i': 9,
+ 'j': 10,
+ 'k': 11,
+ 'l': 12,
+ 'm': 13,
+ 'n': 14,
+ 'o': 15,
+ 'p': 16,
+ 'q': 17,
+ 'r': 18,
+ 's': 19,
+ 't': 20,
+ 'u': 21,
+ 'v': 22,
+ 'w': 23,
+ 'x': 24,
+ 'y': 25,
+ 'z': 26}
+
+int_to_char = dict((i, c) for c, i in char_to_int.items())
+
+def get_sequence_from_encoding(sequence_encoded, ind_to_word):
+  in_text = ''
+  for index in sequence_encoded:
+    if index in ind_to_word:
+      word = ind_to_word[index]
+    else:
+      word = ''
+    in_text += '' + word
+  return in_text
+
+def get_encoding_from_sequence(sequence, word_to_ind):
+  out_encode = []
+  for word in list(sequence.lower()):
+    if word in word_to_ind:
+      index = word_to_ind[word]
+    else:
+      index = 0
+    out_encode.append(index)
+  return out_encode
+
+
+
+def generate_text(start_text,text_length=100):
+  encoding = get_encoding_from_sequence(start_text.lower(),char_to_int)
+  decoding = get_sequence_from_encoding(encoding,int_to_char)
+  print("Input sequence: ", start_text)
+  print("Start generating the paragraph: \n")
+
+  line_print = ''
+  new_sequence = start_text
+  sys.stdout.write(start_text)
+  for repeat in range(text_length):
+      test_data = np.reshape(encoding, (1, len(encoding)))
+      maxlen = 20  # specify how long the sequences should be. This cuts sequences that exceed that number.
+      test_data_pad = pad_sequences(test_data, padding='pre', maxlen=maxlen)
+
+      prediction = model.predict(test_data_pad, verbose=0)
+      index = np.argmax(prediction)
+      result = int_to_char[index]
+      seq_in = [int_to_char[value] for value in encoding]
+      if len(line_print) > 70 and result == ' ':
+        sys.stdout.write("\n")
+        line_print = ''
+
+      sys.stdout.write(result)
+      line_print = line_print + result
+      new_sequence = new_sequence + result
+      encoding.append(index)
+      encoding = encoding[1:len(encoding)]
+
+
+
+### configure inputs/outputs
+
+
+set_input = gr.Textbox.(label = 'Starting words')
+set_output = gr.Textbox.(label = 'Generated sentences')
+
+### configure gradio, detailed can be found at https://www.gradio.app/docs/#i_slider
+interface = gr.Interface(fn=predict_price, 
+                         inputs=set_input, 
+                         outputs=set_output, 
+                         title="CSCI4750/5750 Demo 8: Web Application for Text Generation using RNN", 
+                         description= "Click examples below for a quick demo",
+                         theme = 'huggingface',
+                         layout = 'vertical'
+                         )
+interface.launch(debug=True)