Update app.py

app.py

@@ -3,7 +3,7 @@ import streamlit as st
 import pandas as pd
 import torch
 from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2ForSequenceClassification, TrainingArguments, Trainer, DataCollatorWithPadding, DataCollatorForLanguageModeling
-from text_processor import generate_text, classify_text 
+from text_processor import generate_text, classify_text, post_process_labels
 
 # Step 1: Set Up Your Environment
 # Environment setup and package installations.
@@ -45,13 +45,3 @@ if text:
     labels = classify_text(text, seq_classifier_model, seq_classifier_tokenizer)
     st.write('Classified Labels:')
     st.write(labels)
-
-# Post-process labels based on a threshold or confidence score
-def post_process_labels(results):
-    # Implement your logic to extract and filter labels
-    # based on your sequence classification model's output
-    # For example, you might use a threshold for each label's score
-    # to determine whether it should be considered a valid theme.
-    # Return the selected labels as a list.
-    selected_labels = []
-    return selected_labels