Minimalistic version

app.py

@@ -1,47 +1,18 @@
-# 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
+from transformers import pipeline
 
-# Step 1: Set Up Your Environment
-# Environment setup and package installations.
+# Load the zero-shot classification pipeline
+theme_detection = pipeline('zero-shot-classification')
 
-# Step 2: Data Preparation
-# Load and preprocess your CSV dataset.
-df = pd.read_csv('stepkids_training_data.csv')
+st.title("Theme Detection App")
 
-# Filter out rows with missing label data
-df = df.dropna(subset=['Theme 1', 'Theme 2', 'Theme 3', 'Theme 4', 'Theme 5'])
+# Create a textarea for user input
+user_text = st.text_area("Enter Text:", "Type here...")
 
-text_list = df['Post Text'].tolist()
-labels = df[['Theme 1', 'Theme 2', 'Theme 3', 'Theme 4', 'Theme 5']].values.tolist()
+if st.button("Detect Themes"):
+    # Perform theme detection
+    themes = theme_detection(user_text, ['Theme1', 'Theme2', 'Theme3'])
+    
+    # Display the result
+    st.success(f"Detected Themes: {', '.join(themes['labels'])}")
 
-# 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)