update app interface

app.py

@@ -38,30 +38,32 @@ with app:
     gr.Markdown("## Detect Transformation Sentences in Quarterly Earnings Conference Calls")
     with gr.Row():
         with gr.Column():
-            text_in = gr.Textbox(lines=1, placeholder="Insert text", label="Search Query")
+            text_in = gr.Textbox(lines=1, placeholder="Insert text", label="Input Sentence")
             with gr.Row():
-                compute_bt = gr.Button("Calculate")
-            score_out = gr.Number(label="Label 1 probability", interactive=False)
+                compute_bt = gr.Button("Classify")
+            score_out = gr.Number(label="Score", interactive=False)
             html_out = gr.HTML(label="Explanation")
         with gr.Column():
             gr.Markdown(
                 """
                 #### Project Description
-                Call2Vec is a [fastText](https://fasttext.cc/) word embedding model trained via [Gensim](https://radimrehurek.com/gensim/). It maps each token in the vocabulary into a dense, 300-dimensional vector space, designed for performing semantic search.
-                The model is trained on a large sample of quarterly earnings conference calls, held by U.S. firms during the 2006-2022 period. In particular, the training data is restriced to the (rather sponentous) executives' remarks of the Q&A section of the call. The data has been preprocessed prior to model training via stop word removal, lemmatization, named entity masking, and coocurrence modeling.
+                Placeholder
                 """
             )
             gr.Markdown(
                 """
                 #### App usage
-                The model is intented to be used for **semantic search**: It encodes the search query (entered in the textbox on the right) in a dense vector space and finds semantic neighbours, i.e., token which frequently occur within similar contexts in the underlying training data.
-                The model allows for two use cases:
-                1. *Single Search:* The input query consists of a single word. When provided a bi-, tri-, or even fourgram, the quality of the model output depends on the presence of the query token in the model's vocabulary. N-grams should be concated by an underscore (e.g., "machine_learning" or "artifical_intelligence").
-                2. *Multi Search:* The input query may consist of several words or n-grams, seperated by comma, semi-colon or newline. It then computes the average vector over all inputs and performs semantic search based on the average input token.
+                The model is intented to be used for **sequence classification**: It encodes the input sentence (entered in the textbox on the left) in a dense vector space and runs it through a deep neural network classifier (*Distill-RoBERTa*).
+                It returns a confidence score that indicates the probability of the sentence containing a discussion on transformation activities. A value of 1 (0) signals a high confidence of the sentence being transformation-related (generic). A score in the range of [0.25; 0.75] implies that the model is rather undecided about the correct label.
+                In addition, the app returns the tokenized version of the sentence, alongside word importances that are indicated by color codes. Those visuals illustrates the ability of the context-aware classifier to simultaneously pay attention to various parts in the input sentence to derive a final label.                
                 """
             )
             gr.Examples(
-                examples=[["Now Accord networks is a company in video, and he led the sales team, and the marketing group at Accord, and he took it from start up, sound familiar, it's from start up to $60 million company in two years."], ["Another test sentence"], ["Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam"]],
+                examples=[
+                    ["If we look at the plans for 2018, it is to introduce 650 new products, which is an absolute all- time high."],
+                    ["We have been doing kind of an integrated campaign, so it's TV, online, we do the Google Ad Words - all those different elements together."],
+                    ["So that turned out to be beneficial for us, and I think, we'll just see how the market and interest rates move over the course of the year,"]
+                ],
                 inputs=[text_in],
                 outputs=[score_out, html_out],
                 fn=classify,
@@ -72,7 +74,7 @@ with app:
         <p style="text-align: center;">
             TIClassifier by X and Y
             <br>
-            <img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=simonschoe.call2vec&left_color=green&right_color=blue" style="display: block; margin-left: auto; margin-right: auto;"/>
+            <img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=simonschoe.TIC&left_color=green&right_color=blue" style="display: block; margin-left: auto; margin-right: auto;"/>
         </p>
         """
     )