Update app.py

app.py

@@ -182,6 +182,7 @@ def process_and_compare(file1, sheet1, file2, sheet2):
         historical_col = f'Historical {year - 1}'
         baseline_cols = [f'Baseline {year}', f'Baseline {year + 1}', f'Baseline {year + 2}']
         adverse_cols = [f'Adverse {year}', f'Adverse {year + 1}', f'Adverse {year + 2}']
+        adverse_cols = [f'{year}', f'{year + 1}', f'{year + 2}']
         level_deviation_col = f'Level Deviation {year + 2}'
 
         # Drop rows and reset index
@@ -196,6 +197,7 @@ def process_and_compare(file1, sheet1, file2, sheet2):
         else:
             raise ValueError(f"Expected {len(new_columns)} columns, but found {len(df.columns)} columns in the data.")
         columns = ['Country', f'Adverse {year}', f'Adverse {year+1}', f'Adverse {year+2}', 'Adverse Cumulative']
+        columns = ['Country', f'{year}', f'{year+1}', f'{year+2}', 'Cumulative']
         return df, df[columns]
 
     # Process both files
@@ -352,6 +354,7 @@ def generate_text(df, country, theme):
     Given the following adverse growth data for {theme} in {country}:
     
     {row_str}
+    Topic: {theme}
     
     Describe the yearly changes in adverse growth, highlighting whether the values increased or decreased, and provide the cumulative growth. Follow this example:
     
@@ -370,7 +373,7 @@ def generate_text(df, country, theme):
     """
 
     # Generate the descriptive text using the model
-    result = table_to_text(prompt, max_length=240, temperature = 0.7, top_p = 0.3, do_sample = False)[0]['generated_text']
+    result = table_to_text(prompt1, max_length=240, temperature = 0.7, top_p = 0.3, do_sample = False)[0]['generated_text']
     
     return result
 # Global variable
@@ -429,7 +432,7 @@ with gr.Blocks(theme='gradio/soft',js=js_func) as demo:
         - 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**](https://huggingface.co/google/flan-t5-base).
+        - For a selected country and topic, describe the adverse growth rate trend over three years using the [**google/flan-t5-xl**](https://huggingface.co/google/flan-t5-xl).
         """)
     with gr.Tab("Financial Report Text Analysis"):
         gr.Markdown("## Financial Report Paragraph Selection and Analysis on Adverse Macro-Economy Scenario")