Update app.py

app.py

@@ -5,6 +5,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 # Set the logger to display only CRITICAL messages
 logging.basicConfig(level=logging.CRITICAL)
 
+
 # Cache the model and tokenizer to avoid reloading it every time
 @st.experimental_singleton
 def load_model():
@@ -27,11 +28,15 @@ st.title("Interact with Your Model")
 # User input
 user_input = st.text_area("Enter your prompt:", "")
 
-# Display the submit button and the output directly without needing to press a button
-if user_input:  # This will update every time the user_input changes
-    generated_text = generate_text(user_input)
-    # Display as markdown
-    st.markdown(generated_text)
-else:
-    st.write("Please enter a prompt to get started.")
+if st.button("Submit"):
+    if user_input:
+        # Generate text based on the input
+        generated_text = generate_text(user_input)
+        st.write(generated_text)
+    else:
+        st.write("Please enter a prompt.")
+
 
+in here , i wanna get markdowned or real time responses , how cAN I DO THAT?
+ChatGPT
+To display real-time or interactive responses in a Streamlit application, you might want to leverage Streamlit's widgets and its ability to rerun the script whenever an input changes. However, for markdown or dynamically updating the UI based on the model's output, you'll need to adjust your approach slightly.