Update app.py

app.py

@@ -5,7 +5,8 @@ from keras.layers import Dropout, LayerNormalization
 from keras.models import Model
 from keras.utils import pad_sequences
 import numpy as np
-
+import logging
+logging.getLogger('tensorflow').setLevel(logging.ERROR)
 class TransformerChatbot(Model):
     def __init__(self, vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate):
         super(TransformerChatbot, self).__init__()
@@ -39,29 +40,8 @@ class TransformerChatbot(Model):
     def create_padding_mask(self, seq):
         mask = tf.cast(tf.math.equal(seq, 0), tf.float32)
         return mask[:, tf.newaxis, tf.newaxis, :]
+def completion_model(vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate,weights,datafile,dict,len,text2):
 
-st.title("UniGLM TEXT completion Model")
-st.subheader("Next Word Prediction AI Model by Webraft-AI")
-#Picking what NLP task you want to do
-option = st.selectbox('Model',('13M','26M')) #option is stored in this variable
-#Textbox for text user is entering
-st.subheader("Enter a word from which a sentence / word would be predicted")
-
-text2 = st.text_input('Enter word: ') #text is stored in this variable
-
-
-if option == '13M':
-    vocab_size = 100000
-    max_len = 1
-    d_model = 64  # 64 , 1024
-    n_head = 4  # 8 , 16
-    ff_dim = 256  # 256 , 2048
-    dropout_rate = 0.1  # 0.5 , 0.2
-    weights = "predict3"
-    datafile = "data2.txt"
-    dict = "dict_predict3.bin.npz"
-    len = 15
-    text2 = text2
     with open(datafile,"r") as f:
         text = f.read()
     text = text.lower()
@@ -110,9 +90,43 @@ if option == '13M':
             input_sequence1 = pad_sequences([given_X1], maxlen=max_len, padding='post')
 
         out2 = output_sentence
+        return out2
+st.title("UniGLM TEXT completion Model")
+st.subheader("Next Word Prediction AI Model by Webraft-AI")
+#Picking what NLP task you want to do
+option = st.selectbox('Model',('13M','26M')) #option is stored in this variable
+#Textbox for text user is entering
+st.subheader("Enter a word from which a sentence / word would be predicted")
+
+text2 = st.text_input('Enter word: ') #text is stored in this variable
+
+
+if option == '13M':
+    option2 = st.selectbox('Type',('word','sentence'))
+    if option2 == 'word':
+        len = 1
+    else:
+        len = 13
+    vocab_size = 100000
+    max_len = 1
+    d_model = 64  # 64 , 1024
+    n_head = 4  # 8 , 16
+    ff_dim = 256  # 256 , 2048
+    dropout_rate = 0.1  # 0.5 , 0.2
+    weights = "predict3"
+    datafile = "data2.txt"
+    dict = "dict_predict3.bin.npz"
+    out2 = completion_model(vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate,weights,datafile,dict,len,text2)
+    st.write("Predicted Text: ")
+    st.write(out2)
         
     
 elif option=="26M":
+    option2 = st.selectbox('Type',('word','sentence'))
+    if option2 == 'word':
+        len = 1
+    else:
+        len = 13
     vocab_size = 100000
     max_len = 1
     d_model = 128  # 64 , 1024
@@ -122,57 +136,10 @@ elif option=="26M":
     weights = "predict5"
     datafile = "data2.txt"
     dict = "dict_predict3.bin.npz"
-    len = 15
-    text2 = text2
-        with open(datafile,"r") as f:
-        text = f.read()
-    text = text.lower()
-    words = text.split()
-    loaded_dict = np.load(dict, allow_pickle=True)
-    word_to_num = loaded_dict["word_to_num"].item()
-    num_to_word = loaded_dict["num_to_word"].item()
-    X = []
-    Y = []
-    for i in range(len(words)-1):
-        word = words[i]
-        next_word = words[i+1]
-        X.append(word_to_num[word])
-        Y.append(word_to_num[next_word])
-    Y.append(0)
-
-    X.append(word_to_num[words[-1]])
-    X_train = pad_sequences([X])
-    y_train = pad_sequences([Y])
-    
-
-
-    chatbot = TransformerChatbot(vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate)
-    chatbot.load_weights(weights)
-    chatbot.build(input_shape=(None, max_len)) # Build the model
-    chatbot.compile(optimizer="adam", loss="sparse_categorical_crossentropy")
-    
-    for i in range(1):
-        other_text1 = text2
-        other_text1 = other_text1.lower()
-        other_words1 = other_text1.split()
-        other_num1 = [word_to_num[word] for word in other_words1]
-        given_X1 = other_num1
-        input_sequence1 = pad_sequences([given_X1], maxlen=max_len, padding='post')
-        output_sentence = ""
-        for _ in range(len):
-            predicted_token = np.argmax(chatbot.predict(input_sequence1), axis=-1)
-            predicted_token = predicted_token.item()
-            out = num_to_word[predicted_token]
-            
-    
-            output_sentence = out
-            
-            given_X1 = given_X1[1:]
-            given_X1.append(predicted_token)
-            input_sequence1 = pad_sequences([given_X1], maxlen=max_len, padding='post')
-
-        out2 = output_sentence
+    out2 = completion_model(vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate,weights,datafile,dict,len,text2)
+    st.write("Predicted Text: ")
+    st.write(out2)
 else:
     out2 = "Error: Wrong Model Selected"
-st.write("Predicted Text: ")
-st.write(out2)
+    
+    st.write(out2)