Update app.py

app.py

@@ -41,10 +41,10 @@ def Personality_Detection_from_reviews_submitted (model_input: str) -> Dict[str,
         "Conscientiousness": float(pred_label[0][3]),
         "Openness": float(pred_label[0][4]),}
     return ret
-model_input = gr.Textbox("Input text here (Note: This model is trained to classify Essays(Still in Progress phase))", show_label=False)
-model_output = gr.Label(" Big-Five personality traits Result", num_top_classes=6, show_label=True, label="Big-Five personality traits Labels assigned to this text")
+model_input = gr.Textbox("Input text here (Note: This model is trained to classify Big Five Personality Traits From Expository text features)", show_label=False)
+model_output = gr.Label(" Big-Five personality traits Result", num_top_classes=6, show_label=True, label="Big-Five personality traits Labels assigned to this text based on its features")
 examples = [
-    (  "Well, here we go with the stream of consciousness essay. I used to do things like this in high school sometimes.",
+    (  "Well, here we go with the stream-of-consciousness essay. I used to do things like this in high school sometimes.",
        "They were pretty interesting, but I often find myself with a lack of things to say. ",
        "I normally consider myself someone who gets straight to the point. I wonder if I should hit enter any time to send this back to the front",
        "Maybe I'll fix it later. My friend is playing guitar in my room now. Sort of playing anyway.",
@@ -81,7 +81,7 @@ examples = [
 ]
 
 title = "Big Five Personality Traits Detection From Expository text features"
-description = ("In traditional machine learning, it can be challenging to train an accurate model if there is a lack of labeled data specific to the task or domain of interest. Transfer learning offers a way to address this issue by utilizing the pre-existing labeled data from a similar task or domain to improve model performance. By transferring knowledge learned from one task to another, transfer learning enables us to overcome the limitations posed by a shortage of labeled data, and to train more effective models even in data-scarce scenarios. We try to store this knowledge gained in solving the source task in the source domain and applying it to our problem of interest. In this work, I have utilized Transfer Learning utilizing BERT BASE UNCASED model to fine-tune on Big-Five Personality traits Dataset.")
+description = ("<center><img src='https://thoucentric.com/wp-content/themes/cevian-child/assets/img/Thoucentric-Logo.png' alt='Thoucentric-Logo'></center><br><br>In traditional machine learning, it can be challenging to train an accurate model if there is a lack of labeled data specific to the task or domain of interest. Transfer learning offers a way to address this issue by utilizing the pre-existing labeled data from a similar task or domain to improve model performance. By transferring knowledge learned from one task to another, transfer learning enables us to overcome the limitations posed by a shortage of labeled data, and to train more effective models even in data-scarce scenarios. We try to store this knowledge gained in solving the source task in the source domain and applying it to our problem of interest. In this work, I have utilized Transfer Learning utilizing BERT BASE UNCASED model to fine-tune on Big-Five Personality traits Dataset.")
 
 Fotter = (