app.py

import streamlit as st
from transformers import pipeline
from langdetect import detect

# Initialize models
summarizer = pipeline("summarization")
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ur")
grammar_corrector = pipeline("text2text-generation", model="vennify/t5-base-grammar-correction")

st.set_page_config(page_title="CSS AI Assistant by Komal", layout="wide")

st.title("📚 CSS AI Assistant 🇵🇰")
st.write("Helping students with news summaries, precis, essays & more — for free!")

menu = st.sidebar.radio("Select Feature", [
    "Daily News Summary",
    "Precis Evaluation",
    "Essay Feedback",
    "Saved Notes"
])

# --- Daily News Summary ---
if menu == "Daily News Summary":
    st.header("📰 AI-Powered News Summarizer")
    user_news = st.text_area("Paste or write today's news:", height=200)
    lang = st.radio("Language", ["English", "Urdu"])

    if st.button("Summarize") and user_news:
        summary = summarizer(user_news, max_length=100, min_length=30, do_sample=False)[0]['summary_text']
        if lang == "Urdu":
            translation = translator(summary)[0]['translation_text']
            st.success("Summary in Urdu:")
            st.write(translation)
        else:
            st.success("Summary in English:")
            st.write(summary)

# --- Precis Evaluation ---
elif menu == "Precis Evaluation":
    st.header("✍️ Precis Evaluation Tool")
    precis_input = st.text_area("Enter your precis for evaluation:", height=200)
    if st.button("Evaluate Precis") and precis_input:
        corrected = grammar_corrector("grammar: " + precis_input)[