Update app.py

app.py

@@ -42,9 +42,9 @@ 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')) #option is stored in this variable
+option = st.selectbox('Model',('1','2')) #option is stored in this variable
 #Textbox for text user is entering
-st.subheader("Enter a word from which a sentence would be predicted")
+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':
@@ -87,16 +87,15 @@ if option == '1':
         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 = other_text1+""
+        output_sentence = ""
         for _ in range(1):
             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
+            output_sentence = out
+            
             given_X1 = given_X1[1:]
             given_X1.append(predicted_token)
             input_sequence1 = pad_sequences([given_X1], maxlen=max_len, padding='post')
@@ -105,7 +104,60 @@ if option == '1':
         
     
 else:
-    out2 = "Wrong Model"
+    with open("data2.txt","r") as f:
+        text = f.read()
+    text = text.lower()
+    words = text.split()
+    loaded_dict = np.load("dict_predict3.bin.npz", 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])
+    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
+
+
+    chatbot = TransformerChatbot(vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate)
+    chatbot.load_weights("predict3")
+    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 = 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
     
 st.write("Predicted Text: ")
 st.write(out2)