app.py

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
emotion_classifier = pipeline("text-classification", model="aubmindlab/bert-base-arabertv2")

# Function to get embeddings from ARAT5 for topic modeling
def generate_embeddings(texts):
    inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model.encoder(input_ids=inputs['input_ids'])
    embeddings = outputs[0].mean(dim=1).numpy()
    return embeddings

# Function to process the CSV or Excel file
def process_file(uploaded_file):
    # Determine the file type
    if uploaded_file.name.endswith(".csv"):
        df = pd.read_csv(uploaded_file)
    elif uploaded_file.name.endswith(".xlsx"):
        df = pd.read_excel(uploaded_file)
    else:
        st.error("Unsupported file format.")
        return None

    # Validate required columns
    required_columns = ['date', 'poem']
    missing_columns = [col for col in required_columns if col not in df.columns]
    if missing_columns:
        st.error(f"Missing columns: {', '.join(missing_columns)}")
        return None
    
    # Process the file
    df['date'] = pd.to_datetime(df['date'], errors='coerce')
    df = df.dropna(subset=['date'])
    df['year'] = df['date'].dt.year
    
    texts = df['poem'].dropna().tolist()
    emotions = [emotion_classifier(text)[0]['label'] for text in texts]
    df['emotion'] = emotions
    
    embeddings = generate_embeddings(texts)
    topic_model = BERTopic()
    topics, _ = topic_model.fit_transform(embeddings)
    df['topic'] = topics
    return df

# Streamlit App
st.title("Arabic Poem Topic Modeling & Emotion Classification")
uploaded_file = st.file_uploader("Choose a file", type=["csv", "xlsx"])

if uploaded_file is not None:
    try:
        result_df = process_file(uploaded_file)
        if result_df is not None:
            st.write("Data successfully processed!")
            st.write(result_df.head())
    except Exception as e:
        st.error(f"Error: {e}")