app.py

# Import the necessary libraries
import streamlit as st
from transformers import GPT2Tokenizer, GPT2LMHeadModel, pipeline
import torch

# Load the gpt2-large model and tokenizer for text generation
gen_model = GPT2LMHeadModel.from_pretrained('gpt2-large')
gen_tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large')

# Load the zero-shot text classification pipeline from HuggingFace
classifier = pipeline('zero-shot-classification')

# Define a function that takes a text as input and returns a list of labels as output
def generate_labels(text):
    # Append the special token [LABEL] to the text
    text = text + ' [LABEL]'
    # Convert the text to input ids and attention mask
    input_ids = gen_tokenizer.encode(text, return_tensors='pt')
    attention_mask = torch.ones_like(input_ids)
    # Generate up to 5 labels from the model
    outputs = gen_model.generate(input_ids, attention_mask=attention_mask, max_length=len(input_ids)+5, do_sample=True, top_p=0.95)
    # Decode the generated text
    generated = gen_tokenizer.decode(outputs[0], skip_special_tokens=False)
    # Split the generated text by commas
    labels = generated.split(',')
    # Remove the special token and any whitespace from the labels
    labels = [label.replace('[LABEL]', '').strip() for label in labels]
    # Filter out any empty or duplicate labels
    labels = list(dict.fromkeys(filter(None, labels)))
    # Return the labels as a list
    return labels

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

# If the text is not empty, generate labels and classify the text
if text:
    # Generate labels from the text
    labels = generate_labels(text)
    # Display the generated labels
    st.write(f'The generated labels are: {", ".join(labels)}')
    # Classify the text using the generated labels
    result = classifier(text, labels)
    # Get the label and the score with the highest probability
    label = result['labels'][0]
    score = result['scores'][0]
    # Display the label and the score
    st.write(f'The predicted label is: {label}')
    st.write(f'The probability is: {score:.4f}')