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# Import necessary libraries
import streamlit as st
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2ForSequenceClassification, TrainingArguments, Trainer, DataCollatorWithPadding, DataCollatorForLanguageModeling
# 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("fine_tuned_classifier_model")
seq_classifier_tokenizer = GPT2Tokenizer.from_pretrained("fine_tuned_classifier_model")
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)
# 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
# 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
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