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():
        outputs = model.encoder(input_ids=inputs['input_ids'])
    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()}")
    
    # Preprocess the text: assuming the CSV has a 'text' column
    texts = df['text'].dropna().tolist()  # Modify this according to your column name

    # Emotion Classification: Classify emotions for each text (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
    
    # Display the results
    st.write("Emotions classified for each entry:")
    st.write(df[['text', 'emotion', 'topic']])
    
    return df

# Streamlit App
st.title("Arabic Topic Modeling & Emotion Classification with ARAT5")
st.write("Upload a CSV file to perform topic modeling and emotion classification on Arabic text.")

# File upload widget
uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"])

if uploaded_file is not None:
    # Process the file
    result_df = process_file(uploaded_file)