use session state

app.py

@@ -1,8 +1,9 @@
 import streamlit as st
 import pandas as pd
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
-
+import SessionState
 # Function to load the pre-trained model
+ss = SessionState.get(table_df=pd.DataFrame(), first_run = True)
 def load_finetune_model(model_name):
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForSequenceClassification.from_pretrained(model_name)
@@ -48,7 +49,7 @@ 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_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,
@@ -100,8 +101,9 @@ initial_table_data = [{'Text (portion)': ["who's speaking? \n you goddamn cocksu
   'Toxicity class 2': ['obscene'],
   'Class 2 probability': 0.16506287455558777}]
 for d in initial_table_data:
-    table_df = pd.concat([table_df, pd.DataFrame(d)], ignore_index=True)
+    initial_table_df = pd.concat([initial_table_df, pd.DataFrame(d)], ignore_index=True)
 # Load the model and perform toxicity analysis
+
 if st.button("Analyze"):
     if not text:
         st.write("Please enter a text.")
@@ -123,14 +125,18 @@ if st.button("Analyze"):
                     "Toxicity class 2": [results[1][0]],
                     f"Class 2 probability": results[1][1]
                 }
-                print("Before concatenation:")
-                print(table_df)
+                # print("Before concatenation:")
+                # print(table_df)
                 # Concatenate the new data frame with the existing data frame
-                table_df = pd.concat([pd.DataFrame(table_data), table_df], ignore_index=True)
-                print("After concatenation:")
-                print(table_df)
+                if ss.first_run:
+                    ss.table_df = pd.concat([pd.DataFrame(table_data), ss.table_df, initial_table_data], ignore_index=True)
+                    ss.first_run = False
+                else:
+                    ss.table_df = pd.concat([pd.DataFrame(table_data), ss.table_df], ignore_index=True)
+                # print("After concatenation:")
+                # print(table_df)
                 # Update the table with the new data frame
-                st.table(table_df)
+                st.table(ss.table_df)
             else:
                 st.empty()
                 sentiment_pipeline = load_model(selected_model)