Update app.py

app.py

@@ -302,6 +302,28 @@ stored_df1 = []
 stored_df2 = []
 
 with gr.Blocks() as demo:
+    with gr.Tab("Contents"):
+        gr.Markdown("""
+        ## Macro-economy Adverse Scenario Comparison from EBA Reports
+        
+        This application allows the user to compare two reports from text contents or from tables. It's divided into two tabs.
+        
+        **First Tab: Text Comparisons**
+        
+        - Select two PDFs. Each PDF's text content will be extracted into paragraphs.
+        - Select a paragraph from one PDF, and find the most similar paragraph from the other PDF using a specific method.
+        - For a selected paragraph, compute summarization using the **FinPEGASUS model**.
+        - For a selected paragraph, compute sentiment analysis of the paragraph, and for each sentence, classify into three classes (Positive, Negative, Neutral) using two different fine-tuned **FinBERT models**:
+          - [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert)
+          - [yiyanghkust/finbert-tone](https://huggingface.co/yiyanghkust/finbert-tone)
+        
+        **Second Tab: Table Comparisons**
+        
+        - Select two Excel files and a sheet name.
+        - For the two selected tables, compute the difference of the cumulative adverse growth rate over their respective three years for the selected sheet name (topic).
+        - For the selected topic (sheet name), find related sentences in the associated PDF text that mention the topic, and classify them by sentiment.
+        - For a selected country and topic, describe the adverse growth rate trend over three years using the [**google/flan-t5-base** model](https://huggingface.co/google/flan-t5-base).
+        """)
     with gr.Tab("Financial Report Text Analysis"):
         gr.Markdown("## Financial Report Paragraph Selection and Analysis on adverse macro-economy scenario")