funding info in footer

app.py

@@ -222,8 +222,9 @@ def predict_wrapper(text, language):
     figure = plot_emotion_barplot(prepare_heatmap_data(results_heatmap))
     heatmap = plot_emotion_heatmap(prepare_heatmap_data(results_heatmap))
     piechart = plot_average_emotion_pie(results_heatmap)
-    output_info = f'Prediction was made using the <a href="https://huggingface.co/{model_id}">{model_id}</a> model.'
-    return results, figure, piechart, heatmap, output_info
+    output_info = f'Prediction was made using the <a href="https://huggingface.co/{model_id}">{model_id}</a> model. '
+    funding_info = "The research was funded by European Union’s Horizon 2020 research and innovation program, “MORES” project (Grant No.: 101132601)"
+    return results, figure, piechart, heatmap, output_info + funding_info
 
 
 with gr.Blocks(css=css) as demo:
@@ -232,7 +233,6 @@ with gr.Blocks(css=css) as demo:
     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.
-    The research was funded by European Union’s Horizon 2020 research and innovation program, “MORES” project (Grant No.: 101132601).
     """
     
     gr.HTML("<h1>MORES Pulse</h1>", elem_classes="title_")