app.py

# Import necessary libraries
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.

# Step 2: Data Preparation
# Load and preprocess your CSV dataset.
df = pd.read_csv('stepkids_training_data.csv')

# Filter out rows with missing label data
df = df.dropna(subset=['Theme 1', 'Theme 2', 'Theme 3', 'Theme 4', 'Theme 5'])

text_list = df['Post Text'].tolist()
labels = df[['Theme 1', 'Theme 2', 'Theme 3', 'Theme 4', 'Theme 5']].values.tolist()

# Step 3: Model Selection
# Load your GPT-2 model for text generation.
model_name = "gpt2"  # Choose the appropriate GPT-2 model variant
text_gen_model = GPT2LMHeadModel.from_pretrained(model_name)
text_gen_tokenizer = GPT2Tokenizer.from_pretrained(model_name)
text_gen_tokenizer.pad_token = text_gen_tokenizer.eos_token

# Load your sequence classification model (e.g., BERT)
seq_classifier_model = GPT2ForSequenceClassification.from_pretrained(model_name)
seq_classifier_tokenizer = GPT2Tokenizer.from_pretrained(model_name)
seq_classifier_tokenizer.pad_token = seq_classifier_tokenizer.eos_token

# Create a title and a text input for the app
st.title('Thematic Analysis with GPT-2 Large')
text = st.text_area('Enter some text')

# If the text is not empty, perform both text generation and sequence classification
if text:
    # Perform text generation
    generated_text = generate_text(text, text_gen_model, text_gen_tokenizer)
    st.write('Generated Text:')
    st.write(generated_text)

    # Perform sequence classification
    labels = classify_text(text, seq_classifier_model, seq_classifier_tokenizer)
    st.write('Classified Labels:')
    st.write(labels)