Update streamlit_app.py

streamlit_app.py

@@ -1,25 +1,27 @@
-import streamlit as st
-import tensorflow as tf
-
-st.markdown(
-    """
-    <h1 style='text-align: center; color: #4B8BBE; font-size: 48px;'>
-        Next Character Prediction
-    </h1>
-    """,
-    unsafe_allow_html=True
-)
-
-characters = st.text_input("Enter the characters to begin with")
-
-one_step_reloaded = tf.saved_model.load('reonestep20F')
-
-states = None
-next_char = tf.constant([characters])
-result = [next_char]
-
-for n in range(100):
-  next_char, states = one_step_reloaded.generate_one_step(next_char, states=states)
-  result.append(next_char)
-
-st.write(tf.strings.join(result)[0].numpy().decode("utf-8"))
+import streamlit as st
+import tensorflow as tf
+
+st.markdown(
+    """
+    <h1 style='text-align: center; color: #4B8BBE; font-size: 48px;'>
+        Next Character Prediction
+    </h1>
+    """,
+    unsafe_allow_html=True
+)
+
+characters = st.text_input("Enter the characters to begin with")
+
+one_step_reloaded = tf.saved_model.load('reonestep20F')
+
+if characters:
+
+    states = None
+    next_char = tf.constant([characters])
+    result = [next_char]
+    
+    for n in range(100):
+        next_char, states = one_step_reloaded.generate_one_step(next_char, states=states)
+        result.append(next_char)
+    
+    st.write(tf.strings.join(result)[0].numpy().decode("utf-8"))