import streamlit as st
import pandas as pd
from transformers import pipeline

# Load translation and summarization pipelines
translator = pipeline("translation_ru_to_en", model="Helsinki-NLP/opus-mt-ru-en")
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
title_generator = pipeline("text2text-generation", model="facebook/bart-large-cnn")

# Function to translate, summarize, and generate title
def translate_summarize_and_generate_title(text):
    translated_text = translator(text)[0]['translation_text']
    summary = 'News Alert. ' + summarizer(translated_text, max_length=140, min_length=110, do_sample=False)[0]['summary_text']
    title = title_generator(translated_text, max_length=10, min_length=5, do_sample=False)[0]['generated_text']
    return summary, title

# Streamlit interface
def main():
    st.title("CSV Translator, Summarizer, and Title Generator")

    # File uploader
    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
    if uploaded_file is not None:
        # Read data
        data = pd.read_csv(uploaded_file)
        # Check if 'Description' and 'Published' columns exist
        if 'Description' in data.columns and 'Published' in data.columns:
            # Apply translation, summarization, and title generation based on 'Published' column
            data[['Summary', 'Generated Title']] = data.apply(
                lambda row: translate_summarize_and_generate_title(row['Description']) if pd.isna(row['Published']) else ("", ""), axis=1, result_type='expand'
            )
            # Display data in a table
            st.write(data[['ID', 'Title', 'Generated Title', 'Summary']])

        else:
            st.error("Uploaded CSV does not contain required 'Description' and 'Published' columns.")

if __name__ == "__main__":
    main()