app.py

import streamlit as st
from transformers import pipeline

pipe=pipeline(model="vennify/t5-base-grammar-correction")
st.title("Grammatical Error Checker")
st.header("Text Input:")
text=st.text_area('Input sentence:', key=1)
if text:
    out=pipe(text)
    st.text_area(label="Output sentence:", value=out)

from audio_recorder_streamlit import audio_recorder
pipe_s=pipeline(model="openai/whisper-large-v3")
st.header("Speech Input:")
audio_bytes = audio_recorder(pause_threshold=2.0, sample_rate=41_000, recording_color="#e8b62c", neutral_color="#6aa36f", icon_name="user", icon_size="6x")
if audio_bytes:
    st.audio(audio_bytes, format="audio/wav")
    out_s=pipe_s(audio_bytes)
    st.text_area(label="Input sentence:", value=out_s)
    out_s=str(out_s)
    out_S=pipe(out_s)
    st.text_area(label="Output sentence:", value=out_S)
    
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
st.title("Language Translator")
model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
tokenizer.src_lang = "en_XX"
text_l=st.text_area('Input sentence:', key=2)
if text_l:
    encoded_en = tokenizer(text_l, return_tensors="pt")
    generated_tokens = model.generate(**encoded_en,forced_bos_token_id=tokenizer.lang_code_to_id["hi_IN"])
    out_l=tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
    st.text_area(label="Output sentence:", value=out_l)

pipe_p=pipeline(model="ramsrigouthamg/t5_sentence_paraphraser")
st.title("Paraphraser")
text_p=st.text_area('Input sentence:', key=3)
if text_p:
    out_p=pipe_p(text_p)
    st.text_area(label="Output sentence:", value=out_p)