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| import streamlit as st | |
| import pandas as pd | |
| from transformers import T5Tokenizer, T5ForConditionalGeneration, pipeline | |
| from bertopic import BERTopic | |
| import torch | |
| # Initialize ARAT5 model and tokenizer for topic modeling | |
| tokenizer = T5Tokenizer.from_pretrained("UBC-NLP/araT5-base") | |
| model = T5ForConditionalGeneration.from_pretrained("UBC-NLP/araT5-base") | |
| # Emotion classification pipeline for Arabic (use an Arabic emotion classification model) | |
| emotion_classifier = pipeline("text-classification", model="d0r13n/ara-bert-base-arabic-emotion") | |
| # Function to get embeddings from ARAT5 for topic modeling | |
| def generate_embeddings(texts): | |
| # Tokenize the Arabic text for ARAT5 | |
| inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=512) | |
| with torch.no_grad(): | |
| # Use ARAT5 to generate embeddings | |
| outputs = model.encoder(input_ids=inputs['input_ids']) | |
| # Extract the embeddings (mean of hidden states for simplicity) | |
| return outputs.last_hidden_state.mean(dim=1).numpy() | |
| # Function to process the CSV file and return emotion and topic model | |
| def process_file(uploaded_file): | |
| # Load CSV | |
| df = pd.read_csv(uploaded_file) | |
| # Display basic info about the CSV | |
| st.write("CSV Loaded Successfully!") | |
| st.write(f"Data Preview: {df.head()}") | |
| # Ensure 'date' column is in datetime format and extract the year | |
| df['date'] = pd.to_datetime(df['date'], errors='coerce') # Replace 'date' with your actual column name | |
| df['year'] = df['date'].dt.year | |
| # Modify this to use the 'poem' column that contains the Arabic poems | |
| texts = df['poem'].dropna().tolist() # Replace 'poem' with your actual column name | |
| # Emotion Classification: Classify emotions for each poem (Arabic) | |
| emotions = [emotion_classifier(text)[0]['label'] for text in texts] | |
| df['emotion'] = emotions | |
| # Topic Modeling using ARAT5 embeddings | |
| embeddings = generate_embeddings(texts) | |
| topic_model = BERTopic() | |
| topics, _ = topic_model.fit_transform(embeddings) | |
| df['topic'] = topics | |
| # Return the processed dataframe | |
| return df | |
| # Streamlit App | |
| st.title("Arabic Poem Topic Modeling & Emotion Classification with ARAT5") | |
| st.write("Upload a CSV file to perform topic modeling and emotion classification on Arabic poems.") | |
| # File upload widget | |
| uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"]) | |
| # If file is uploaded, process and display results | |
| if uploaded_file is not None: | |
| result_df = process_file(uploaded_file) | |
| # Show date selection widgets | |
| st.write("### Filter by Date Range") | |
| start_date = st.date_input("Start Date", value=pd.to_datetime(result_df['date'].min())) | |
| end_date = st.date_input("End Date", value=pd.to_datetime(result_df['date'].max())) | |
| # Filter data based on selected date range | |
| filtered_df = result_df[(result_df['date'] >= start_date) & (result_df['date'] <= end_date)] | |
| # Display filtered data | |
| st.write(f"Filtered Data (Poems from {start_date} to {end_date}):") | |
| st.write(filtered_df[['poet_name', 'era', 'poem', 'emotion', 'topic', 'date']]) | |
| # Create buttons to show different summaries | |
| summary_type = st.radio("Select Summary Type:", | |
| ("Emotion and Topic Summary by Date Range", | |
| "Global Emotion and Topic Summary")) | |
| # Display the selected summary | |
| if summary_type == "Emotion and Topic Summary by Date Range": | |
| st.write("Emotion and Topic Summary for Selected Date Range:") | |
| # Emotion Distribution in Date Range | |
| emotion_counts = filtered_df['emotion'].value_counts() | |
| st.write("Emotion Counts in Date Range:") | |
| st.write(emotion_counts) | |
| # Topic Distribution in Date Range | |
| topic_counts = filtered_df['topic'].value_counts() | |
| st.write("Topic Counts in Date Range:") | |
| st.write(topic_counts) | |
| # Visualize emotion distribution over the selected range (optional) | |
| st.bar_chart(emotion_counts, use_container_width=True) | |
| # Visualize topic distribution over the selected range (optional) | |
| st.bar_chart(topic_counts, use_container_width=True) | |
| elif summary_type == "Global Emotion and Topic Summary": | |
| st.write("Global Emotion and Topic Summary (All Poems):") | |
| global_emotion_count = result_df['emotion'].value_counts().to_dict() | |
| global_topic_count = result_df['topic'].value_counts().to_dict() | |
| st.write(f"Emotion Distribution: {global_emotion_count}") | |
| st.write(f"Topic Distribution: {global_topic_count}") | |