Update app.py

app.py

@@ -3,6 +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 
 
 # Step 1: Set Up Your Environment
 # Environment setup and package installations.
@@ -45,29 +46,6 @@ if text:
     st.write('Classified Labels:')
     st.write(labels)
 
-# Function for generating text based on input
-def generate_text(input_text, model, tokenizer):
-    # Append the special token to the input
-    input_text = input_text + ' [LABEL]'
-    input_ids = tokenizer.encode(input_text, return_tensors='pt')
-    attention_mask = torch.ones_like(input_ids)
-    outputs = model.generate(input_ids, attention_mask=attention_mask, max_length=len(input_ids) + 5, do_sample=True, top_p=0.95)
-    generated = tokenizer.decode(outputs[0], skip_special_tokens=False)
-    labels = generated.split(',')
-    labels = [label.replace('[LABEL]', '').strip() for label in labels]
-    return generated
-
-# Function for sequence classification
-def classify_text(input_text, model, tokenizer):
-    # Tokenize the input text
-    input_ids = tokenizer.encode(input_text, return_tensors='pt')
-    attention_mask = torch.ones_like(input_ids)
-    # Perform sequence classification
-    result = model(input_ids, attention_mask=attention_mask)
-    # Post-process the results (e.g., select labels based on a threshold)
-    labels = post_process_labels(result)
-    return labels
-
 # Post-process labels based on a threshold or confidence score
 def post_process_labels(results):
     # Implement your logic to extract and filter labels
@@ -76,4 +54,4 @@ def post_process_labels(results):
     # to determine whether it should be considered a valid theme.
     # Return the selected labels as a list.
     selected_labels = []
-    return selected_labels
+    return selected_labels