update

app.py

@@ -18,12 +18,12 @@ model = AutoModelForCausalLM.from_pretrained(repo_name, trust_remote_code=True).
 st.sidebar.title("Instructions: How to use")
 st.sidebar.write("""
 1. Write something in the text area (a prompt or random text) or use the dropdown menu to select predefined sample text.
-2. Select a task from the **task dropdown menu** below if you are providing your own text. **This is very important as it ensures the model responds accordingly.**
+2. Select a task from the **task dropdown menu** below only if you are providing your own text. **This is very important as it ensures the model responds accordingly.**
 3. If you are providing your own text, please do not select any predefined sample text from the dropdown menu.
 3. If a dropdown menu pops up for a nigerian language, **select the nigerian language (base language for diacritization and text cleaning tasks, target language for translation task).**
-4. Click the Generate button to get a response below the text area.\n
+4. Then, click the Generate button.\n
 5. For Translation tasks, setting english as the target language yields the best result (english as base language performs the worst).
-**Note: Model's overall performance vary (hallucinates) due to model size and training data distribution (majorly from news articles and the bible). Performance may worsen significantly with other task outside text generation.
+**Note: Model's overall performance vary (hallucinates) due to model size and training data distribution (majorly from articles and the bible). Performance may worsen with other task outside text generation and translation.
 For other tasks, we suggest you try them several times due to the generator's sampling method.**\n
 6. Lastly, you can play with some of the generation parameters below to improve performance.
 """)
@@ -199,7 +199,7 @@ else:
     task_value = task_options[task]
 
 def wrap_text(text, task_value):
-    tasks = ["<classify>", "<prompt>", "<clean>", "<title>", "<diacritize>"]
+    tasks = ["<classify>", "<prompt>", "<clean>", "<title>", "<diacritize>", "<translate>"]
     if any(task in text for task in tasks):
         return text
     return task_value.format(text)
@@ -209,6 +209,7 @@ def wrap_text(text, task_value):
 user_input = st.text_area("Enter text below **(PLEASE, FIRST READ ALL INSTRUCTIONS IN THE SIDEBAR CAREFULLY FOR THE BEST EXPERIENCE)**: ", sample_texts[sample_text])
 user_input = instruction_wrap.get(sample_texts.get(user_input, user_input), user_input)
 print("Final user input: ", user_input)
+
 if st.button("Generate"):
     if user_input:
         try: