Update app.py

app.py

@@ -16,7 +16,7 @@ import pandas as pd
 
 
 # LLM Langchain Definition
-OPENAI_API_KEY = "86b631a9c0294e9698e327c59ff5ac2c"
+OPENAI_API_KEY = ""
 OPENAI_API_TYPE = "azure"
 OPENAI_API_BASE = "https://davidfearn-gpt4.openai.azure.com"
 # OPENAI_API_VERSION = "2024-02-01"
@@ -115,20 +115,20 @@ if user_query is not None and user_query != "":
 
     if knowledge:
             
-            # Prepare the data for the table
-            table_data = {
-                "Title": [entry['Title'] for entry in knowledge],
-                "Score (%)": [f"{int(entry.get('Score', 0) * 100)}%" for entry in knowledge],  # Convert to percentage and remove decimals
-                "Page": [entry['PageNumber'] for entry in knowledge],
-                # "Grounding Text": [entry['ReconstructedText'] for entry in knowledge]
+        # Prepare the data for the table
+        table_data = {
+            "Title": [entry['Title'] for entry in knowledge],
+            "Score (%)": [f"{int(entry.get('Score', 0) * 100)}%" for entry in knowledge],  # Convert to percentage and remove decimals
+            "Page": [entry['PageNumber'] for entry in knowledge],
+            # "Grounding Text": [entry['ReconstructedText'] for entry in knowledge]
             }
     
             # Create a dataframe for displaying as a table
         
-            df = pd.DataFrame(table_data)
+        df = pd.DataFrame(table_data)
     
             # Display the table in the sidebar
-            st.sidebar.write("### Knowledge Base Results")
-            st.sidebar.dataframe(df)  # Adjust height as needed
-        else:
-            st.sidebar.write("No relevant knowledge base results found.")
+        st.sidebar.write("### Knowledge Base Results")
+        st.sidebar.dataframe(df)  # Adjust height as needed
+    else:
+        st.sidebar.write("No relevant knowledge base results found.")