Update app.py

app.py

@@ -71,27 +71,27 @@ def completion_model(vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate,
     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_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_sequence1), axis=-1)
+            predicted_token = np.argmax(chatbot.predict(input_sequence2), 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')
+            # 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
-        return out2
+    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