Update app.py

app.py

@@ -44,12 +44,12 @@ class TransformerChatbot(Model):
 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',('1','2')) #option is stored in this variable
+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 == '1':
+if option == '13M':
     with open("data2.txt","r") as f:
         text = f.read()
     text = text.lower()
@@ -74,13 +74,25 @@ if option == '1':
     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(1):
+        output_sentence = other_text1+""
+        for _ in range(10):
             predicted_token = np.argmax(chatbot.predict(input_sequence1), axis=-1)
             predicted_token = predicted_token.item()
             out = num_to_word[predicted_token]
+            
+    
+            output_sentence += " " + out
+            if out == ".":
+                break
+            given_X1 = given_X1[1:]
+            given_X1.append(predicted_token)
             input_sequence1 = pad_sequences([given_X1], maxlen=max_len, padding='post')
 
         out2 = output_sentence
@@ -91,7 +103,7 @@ else:
         text = f.read()
     text = text.lower()
     words = text.split()
-    loaded_dict = np.load("dict_predict3.bin.npz", allow_pickle=True)
+    loaded_dict = np.load("dict_predict1.bin.npz", allow_pickle=True)
     word_to_num = loaded_dict["word_to_num"].item()
     num_to_word = loaded_dict["num_to_word"].item()
     X = []
@@ -108,14 +120,14 @@ else:
     y_train = pad_sequences([Y])
     vocab_size = 100000
     max_len = 1
-    d_model = 64  # 64 , 1024
+    d_model = 128  # 64 , 1024
     n_head = 4  # 8 , 16
     ff_dim = 256  # 256 , 2048
     dropout_rate = 0.1  # 0.5 , 0.2
 
 
     chatbot = TransformerChatbot(vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate)
-    chatbot.load_weights("predict3")
+    chatbot.load_weights("predict1")
     chatbot.build(input_shape=(None, max_len)) # Build the model
     chatbot.compile(optimizer="adam", loss="sparse_categorical_crossentropy")