add initial table data and changed column format

app.py

@@ -48,6 +48,59 @@ selected_model = st.selectbox("Choose a fine-tuned model:", model_options)
 st.write("### Model Information")
 st.write(f"**Description:** {model_options[selected_model]['description']}")
 
+table_df = pd.DataFrame(columns=["Text (portion)", "Toxicity class 1", "Class 1 probability", "Toxicity class 2", "Class 2 probability"])
+initial_table_data = [{'Text (portion)': ["who's speaking? \n you goddamn cocksucker you know "],
+  'Toxicity class 1': ['obscene'],
+  'Class 1 probability': 0.7282997369766235,
+  'Toxicity class 2': ['toxic'],
+  'Class 2 probability': 0.2139672487974167},
+ {'Text (portion)': ['::Here is another source: Melissa Sue Halverson (2'],
+  'Toxicity class 1': ['toxic'],
+  'Class 1 probability': 0.24484945833683014,
+  'Toxicity class 2': ['obscene'],
+  'Class 2 probability': 0.1627064049243927},
+ {'Text (portion)': [', 8 November 2007 (UTC) \n\n All I can say is, havin'],
+  'Toxicity class 1': ['toxic'],
+  'Class 1 probability': 0.7277262806892395,
+  'Toxicity class 2': ['obscene'],
+  'Class 2 probability': 0.2502792477607727},
+ {'Text (portion)': ['::::I only see that at birth two persons are given'],
+  'Toxicity class 1': ['toxic'],
+  'Class 1 probability': 0.2711867094039917,
+  'Toxicity class 2': ['insult'],
+  'Class 2 probability': 0.15477754175662994},
+ {'Text (portion)': ["* There you have it: one man's Barnstar is another"],
+  'Toxicity class 1': ['toxic'],
+  'Class 1 probability': 0.5408656001091003,
+  'Toxicity class 2': ['insult'],
+  'Class 2 probability': 0.12563346326351166},
+ {'Text (portion)': ['" \n\n == Fact == \n\n Could just be abit of trivial f'],
+  'Toxicity class 1': ['toxic'],
+  'Class 1 probability': 0.35239243507385254,
+  'Toxicity class 2': ['obscene'],
+  'Class 2 probability': 0.1686778962612152},
+ {'Text (portion)': ['HE IS A GHAY ASS FUCKER@@!!'],
+  'Toxicity class 1': ['obscene'],
+  'Class 1 probability': 0.7819343209266663,
+  'Toxicity class 2': ['toxic'],
+  'Class 2 probability': 0.16951803863048553},
+ {'Text (portion)': ["I'VE SEEN YOUR CRIMES AGAINST CHILDREN AND I'M ASH"],
+  'Toxicity class 1': ['toxic'],
+  'Class 1 probability': 0.8491994738578796,
+  'Toxicity class 2': ['threat'],
+  'Class 2 probability': 0.04749392718076706},
+ {'Text (portion)': [':While with a lot of that essay says, general time'],
+  'Toxicity class 1': ['toxic'],
+  'Class 1 probability': 0.282654732465744,
+  'Toxicity class 2': ['obscene'],
+  'Class 2 probability': 0.15901680290699005},
+ {'Text (portion)': ['== Help == \n\n Please members of wiki, help me. My '],
+  'Toxicity class 1': ['toxic'],
+  'Class 1 probability': 0.3118911385536194,
+  'Toxicity class 2': ['obscene'],
+  'Class 2 probability': 0.16506287455558777}]
+for d in initial_table_data:
+    pd.concat([table_df, pd.DataFrame(d)], ignore_index=True)
 # Load the model and perform toxicity analysis
 if st.button("Analyze"):
     if not text:
@@ -65,10 +118,12 @@ if st.button("Analyze"):
                 # Create a table with the input text (or a portion of it), the highest toxicity class, and its probability
                 table_data = {
                     "Text (portion)": [text[:50]],
-                    f"{results[0][0]}": results[0][1],
-                    f"{results[1][0]}": results[1][1]
+                    "Toxicity class 1": [results[0][0]],
+                    f"Class 1 probability": results[0][1],
+                    "Toxicity class 2": [results[1][0]],
+                    f"Class 2 probability": results[1][1]
                 }
-                table_df = pd.DataFrame(table_data)
+                table_df = pd.concat([table_df, pd.DataFrame(table_data)], ignore_index=True)
                 st.table(table_df)
             else:
                 sentiment_pipeline = load_model(selected_model)