Update app.py

app.py

@@ -76,6 +76,7 @@ def generate_pink_team_document(outline: str, instructions: str) -> str:
     4. Provide measurable outcomes for the customer.
     5. Limit the use of bullet points and write predominantly in paragraph format.
     6. Ensure a logical flow of steps taken by MicroHealth for each requirement.
+    7. Where applicable, describe the labor category or labor categories that perform the task as part of the process
 
     Generate a comprehensive response that showcases MicroHealth's expertise and approach.
     """
@@ -122,10 +123,7 @@ def generate_red_document(document: str, compliance_report: str) -> str:
     4. Provide measurable outcomes for the customer.
     5. Limit the use of bullet points and write predominantly in paragraph format.
     6. Ensure a logical flow of steps taken by MicroHealth for each requirement.
-
-    Provide the revised document in a clear, structured format using paragraphs.
-    Limit the use of bullet points and write predominantly in paragraph format.
-    Ensure a logical flow of steps taken by MicroHealth for each requirement.
+    7. Where applicable, describe the labor category or labor categories that perform the task as part of the process
     """
     response = model.generate_content(prompt)
     return response.text
@@ -158,22 +156,25 @@ def generate_loe(document: str, is_file: bool = False) -> Tuple[str, pd.DataFram
     Ensure the table is properly formatted with | as column separators and a header row.
     """
     response = model.generate_content(prompt)
+    response_text = response.text
     
     # Extract the table from the response
-    table_start = response.text.find("| Task Summary |")
-    table_end = response.text.find("\n\n", table_start)
-    table_text = response.text[table_start:table_end]
+    table_start = response_text.find("| Task Summary |")
+    table_end = response_text.find("\n\n", table_start)
+    table_text = response_text[table_start:table_end]
     
     # Convert the table to a pandas DataFrame
     try:
+        if not table_text.strip():
+            raise pd.errors.EmptyDataError("No table found in the response")
         df = pd.read_csv(StringIO(table_text), sep='|', skipinitialspace=True).dropna(axis=1, how='all')
         df.columns = df.columns.str.strip()
     except pd.errors.EmptyDataError:
         # If no table is found or it's empty, create a default DataFrame
         df = pd.DataFrame(columns=['Task Summary', 'Labor Categories', 'Hours per Labor Category', 'Total Hours'])
-        response.text += "\n\nNote: No detailed LOE table could be generated from the AI response."
+        response_text += "\n\nNote: No detailed LOE table could be generated from the AI response."
     
-    return response.text, df
+    return response_text, df
 
 # Layout
 app.layout = dbc.Container([
@@ -400,23 +401,26 @@ def update_loe_output(n_clicks, upload_contents, shred_output):
     if n_clicks is None:
         return "Click 'Generate LOE' to begin."
     
-    if upload_contents:
-        loe_text, loe_df = generate_loe(upload_contents, is_file=True)
-    elif shred_output:
-        loe_text, loe_df = generate_loe(shred_output)
-    else:
-        return "Please upload a document or complete the Shred tab first."
-    
-    return [
-        dcc.Markdown(loe_text),
-        dash_table.DataTable(
-            data=loe_df.to_dict('records'),
-            columns=[{'name': i, 'id': i} for i in loe_df.columns],
-            style_table={'overflowX': 'auto'},
-            style_cell={'textAlign': 'left', 'padding': '5px'},
-            style_header={'backgroundColor': 'rgb(230, 230, 230)', 'fontWeight': 'bold'}
-        )
-    ]
+    try:
+        if upload_contents:
+            loe_text, loe_df = generate_loe(upload_contents, is_file=True)
+        elif shred_output:
+            loe_text, loe_df = generate_loe(shred_output)
+        else:
+            return "Please upload a document or complete the Shred tab first."
+        
+        return [
+            dcc.Markdown(loe_text),
+            dash_table.DataTable(
+                data=loe_df.to_dict('records'),
+                columns=[{'name': i, 'id': i} for i in loe_df.columns],
+                style_table={'overflowX': 'auto'},
+                style_cell={'textAlign': 'left', 'padding': '5px'},
+                style_header={'backgroundColor': 'rgb(230, 230, 230)', 'fontWeight': 'bold'}
+            )
+        ]
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
 
 @app.callback(
     Output('red-output', 'children'),