intro text at the right place

app.py

@@ -208,6 +208,12 @@ def predict_wrapper(text, language):
 
 with gr.Blocks() as demo:
     placeholder = "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua."
+    introduction = """
+            This platform is designed to detect and visualize emotions in text. The model behind it operates using a 6-label codebook, including the following labels: ‘Anger’, ‘Fear’, ‘Disgust’, ‘Sadness’, ‘Joy’, and ‘None of Them’.
+    The model is optimized for sentence-level analysis, and make predictions in the following languages: Czech, English, French, German, Hungarian, Polish, and Slovak.
+    The text you enter in the input box is automatically divided into sentences, and the analysis is performed on each sentence. Depending on the length of the text, this process may take a few seconds, but for longer texts, it can take up to 2-3 minutes.
+            """
+    gr.Markdown(introduction)
     with gr.Row():
         with gr.Column():
             input_text = gr.Textbox(lines=6, label="Input", placeholder="Enter your text here...")
@@ -218,12 +224,6 @@ with gr.Blocks() as demo:
                 predict_button = gr.Button("Submit")
 
     with gr.Row():
-        introduction = """
-        This platform is designed to detect and visualize emotions in text. The model behind it operates using a 6-label codebook, including the following labels: ‘Anger’, ‘Fear’, ‘Disgust’, ‘Sadness’, ‘Joy’, and ‘None of Them’.
-The model is optimized for sentence-level analysis, and make predictions in the following languages: Czech, English, French, German, Hungarian, Polish, and Slovak.
-The text you enter in the input box is automatically divided into sentences, and the analysis is performed on each sentence. Depending on the length of the text, this process may take a few seconds, but for longer texts, it can take up to 2-3 minutes.
-        """
-        gr.Markdown(introduction)
         with gr.Column(scale=7):
             result_table = gr.Dataframe(
                 headers=["Sentence", "Prediction", "Confidence"],