Update app.py

app.py

@@ -1,50 +1,78 @@
-# Import the necessary libraries
+# Import necessary libraries
 import streamlit as st
-from transformers import GPT2Tokenizer, GPT2LMHeadModel, pipeline
 import torch
+from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2ForSequenceClassification, TrainingArguments, Trainer, DataCollatorWithPadding, DataCollatorForLanguageModeling
 
-# Load the gpt2-large model and tokenizer for text generation
-gen_model = GPT2LMHeadModel.from_pretrained('gpt2-large')
-gen_tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large')
+# Step 1: Set Up Your Environment
+# Environment setup and package installations.
 
-# Load the zero-shot text classification pipeline from HuggingFace
-classifier = pipeline('zero-shot-classification')
+# Step 2: Data Preparation
+# Load and preprocess your CSV dataset.
+df = pd.read_csv('stepkids_training_data.csv')
 
-# 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
+# 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_input('Enter some text to classify')
+text = st.text_area('Enter some text')
 
-# If the text is not empty, generate labels and classify the text
+# If the text is not empty, perform both text generation and sequence classification
 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}')
+    # 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