Update app.py

app.py

@@ -40,7 +40,7 @@ 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):
+def completion_model(vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate,weights,datafile,dict,len2,text2):
 
     with open(datafile,"r") as f:
         text = f.read()
@@ -77,7 +77,7 @@ def completion_model(vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate,
         given_X1 = other_num1
         input_sequence1 = pad_sequences([given_X1], maxlen=max_len, padding='post')
         output_sentence = ""
-        for _ in range(len):
+        for _ in range(len2):
             predicted_token = np.argmax(chatbot.predict(input_sequence1), axis=-1)
             predicted_token = predicted_token.item()
             out = num_to_word[predicted_token]
@@ -94,14 +94,14 @@ def completion_model(vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate,
 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
+option = st.selectbox('Model',('13M_OLD','26M_OLD')) #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':
+if option == '13M_OLD':
     option2 = st.selectbox('Type',('word','sentence'))
     if option2 == 'word':
         len = 1
@@ -121,13 +121,24 @@ if option == '13M':
     st.write(out2)
         
     
-elif option=="26M":
+elif option=="26M_OLD":
     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
+    n_head = 4  # 8 , 16
+    ff_dim = 256  # 256 , 2048
+    dropout_rate = 0.1  # 0.5 , 0.2
+    weights = "predict1"
+    datafile = "data2.txt"
+    dict = "dict_predict1.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)    
 else:
     out2 = "Error: Wrong Model Selected"