Update app.py

app.py

@@ -41,7 +41,6 @@ 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, :]
-
 st.title("UniGLM TEXT completion Model")
 st.subheader("Next Word Prediction AI Model by Webraft-AI")
 #Picking what NLP task you want to do
@@ -126,66 +125,7 @@ elif option=="26M_OLD":
         len2 = 1
     else:
         len2 = 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"
-
-    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")
-    chatbot.fit(X_train, y_train, epochs=1, batch_size=64)
-    for i in range(1):
-        other_text2 = text2
-        other_text2 = other_text2.lower()
-        other_words2 = other_text2.split()
-        other_num2 = [word_to_num[word] for word in other_words2]
-        given_X2 = other_num2
-        input_sequence2 = pad_sequences([given_X2], maxlen=max_len, padding='post')
-        output_sentence = other_text2 + ""
-        for _ in range(len2):
-            predicted_token = np.argmax(chatbot.predict(input_sequence2), axis=-1)
-            predicted_token = predicted_token.item()
-            out = num_to_word[predicted_token]
-            # if out == ".":
-                # break
-
-            output_sentence += " " + out
-            given_X2 = given_X2[1:]
-            given_X2.append(predicted_token)
-            input_sequence2 = pad_sequences([given_X2], maxlen=max_len, padding='post')
-
-        out2 = output_sentence  
-    st.write("Predicted Text: ")
-    st.write(out2)    
 else:
     out2 = "Error: Wrong Model Selected"