slop

app.py

@@ -214,9 +214,9 @@ def predict_wrapper(text, language):
 
 with gr.Blocks(css=css) 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.
+    introduction = """
+    MORES Pulse detects and reveals emotions in text using a powerful sentence-level AI: MORES-Poltextlab Roberta. It classifies content into six emotion categories — Anger, Fear, Disgust, Sadness, Joy, and None of Them.  
+    The app supports seven languages — Czech, English, French, German, Hungarian, Polish, and Slovak — and analyses your input sentence by sentence. Results appear within seconds for shorter texts, but longer entries may take a few minutes to process. Read our Q&A about Pulse here.
     """
     
     gr.HTML("<h1>MORES Pulse</h1>", elem_classes="title_")