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(input_text):
    inputs = translation_tokenizer_en_bn("translate: " + 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

# Function to perform summarization
def summarize_text(input_text):
    inputs = summarization_tokenizer("summarize: " + 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("paraphrase: " + input_text, return_tensors="pt")
    outputs = paraphrase_model.generate(**inputs)
    paraphrased_text = paraphrase_tokenizer.decode(outputs[0], skip_special_tokens=True)
    return paraphrased_text

# Gradio Interface
iface = gr.Interface(
    fn=translate_text,  # Placeholder function; will be updated dynamically based on task selection
    inputs=gr.Textbox("textarea", label="Input Text"),
    outputs=gr.Textbox("auto", label="Output Text"),
    live=True
)

# Function to update the Gradio interface based on task selection
def update_interface(change):
    selected_task = task_selector.value

    if selected_task == 'Translate':
        iface.fn = translate_text
    elif selected_task == 'Summarize':
        iface.fn = summarize_text
    elif selected_task == 'Paraphrase':
        iface.fn = paraphrase_text

# Dropdown widget to select the task
task_selector = gr.Dropdown(
    ["Translate", "Summarize", "Paraphrase"],
    default="Translate",
    label="Select Task"
)

# Attach the update function to the dropdown widget
task_selector.observe(update_interface, names='value')

# Launch the Gradio app
iface.launch()