Update app.py

app.py

@@ -39,7 +39,29 @@ 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 textcompletion_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")
+len2 = st.text_input('Enter sequence length: ') 
+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 = len2
+    text2 = text2
     with open(datafile,"r") as f:
         text = f.read()
     text = text.lower()
@@ -88,30 +110,6 @@ def textcompletion_model(vocab_size, max_len, d_model, n_head, ff_dim, dropout_r
             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")
-len2 = st.text_input('Enter sequence length: ') 
-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 = len2
-    text2 = text2
-    out2 = textcompletion_model(vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate,weights, datafile , dict, len , text2)
         
     
 elif option=="26M":
@@ -126,7 +124,55 @@ elif option=="26M":
     dict = "dict_predict3.bin.npz"
     len = len2
     text2 = text2
-    out2 = textcompletion_model(vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate,weights, datafile , dict, len , 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
+else:
+    out2 = "Error: Wrong Model Selected"
 st.write("Predicted Text: ")
 st.write(out2)