Create app.py

app.py

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+# 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}')