app.py

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import gradio as gr

# Load fine-tuned T5 models for different tasks
translation_model_en_bn = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/banglat5_nmt_en_bn")
translation_tokenizer_en_bn = AutoTokenizer.from_pretrained("csebuetnlp/banglat5_nmt_en_bn")

translation_model_bn_en = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/banglat5_nmt_bn_en")
translation_tokenizer_bn_en = AutoTokenizer.from_pretrained("csebuetnlp/banglat5_nmt_bn_en")

summarization_model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/mT5_multilingual_XLSum")
summarization_tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/mT5_multilingual_XLSum")

paraphrase_model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/banglat5_banglaparaphrase")
paraphrase_tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/banglat5_banglaparaphrase")

# Function to perform machine translation
def translate_text_en_bn(input_text):
    inputs = translation_tokenizer_en_bn(input_text, return_tensors="pt")
    outputs = translation_model_en_bn.generate(**inputs)
    translated_text = translation_tokenizer_en_bn.decode(outputs[0], skip_special_tokens=True)
    return translated_text

def translate_text_bn_en(input_text):
    inputs = translation_tokenizer_bn_en(input_text, return_tensors="pt")
    outputs = translation_model_bn_en.generate(**inputs)
    translated_text = translation_tokenizer_bn_en.decode(outputs[0], skip_special_tokens=True)
    return translated_text

# Function to perform summarization
def summarize_text(input_text):
    inputs = summarization_tokenizer(input_text, return_tensors="pt")
    outputs = summarization_model.generate(**inputs)
    summarized_text = summarization_tokenizer.decode(outputs[0], skip_special_tokens=True)
    return summarized_text

# Function to perform paraphrasing
def paraphrase_text(input_text):
    inputs = paraphrase_tokenizer(input_text, return_tensors="pt")
    outputs = paraphrase_model.generate(**inputs)
    paraphrased_text = paraphrase_tokenizer.decode(outputs[0], skip_special_tokens=True)
    return paraphrased_text

def process_text(text, task):
    if task == "Translate_English_to_Bengali":
        return translate_text_en_bn(text)
    elif task == "Translate_Bengali_to_English":
        return translate_text_bn_en(text)
    elif task == "Summarize":
        return summarize_text(text)
    elif task == "Paraphrase":
        return paraphrase_text(text)

# Define the Gradio interface
iface = gr.Interface(
    fn=process_text,
    inputs=["text", gr.Dropdown(["Translate_English_to_Bengali", "Translate_Bengali_to_English", "Summarize", "Paraphrase"])],
    outputs="text",
    live=False,
    title="Usage of BanglaT5 Model"
)

# Launch the Gradio app
iface.launch(inline=False)