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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import gradio as gr
# Load fine-tuned BanglaT5 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("skl25/banglat5_xlsum_fine-tuned")
summarization_tokenizer = AutoTokenizer.from_pretrained("skl25/banglat5_xlsum_fine-tuned")
paraphrase_model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/banglat5_banglaparaphrase")
paraphrase_tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/banglat5_banglaparaphrase")
# Define task functions
def translate_text_en_bn(input_text):
inputs = translation_tokenizer_en_bn(input_text, return_tensors="pt")
outputs = translation_model_en_bn.generate(**inputs)
return translation_tokenizer_en_bn.decode(outputs[0], skip_special_tokens=True)
def translate_text_bn_en(input_text):
inputs = translation_tokenizer_bn_en(input_text, return_tensors="pt")
outputs = translation_model_bn_en.generate(**inputs)
return translation_tokenizer_bn_en.decode(outputs[0], skip_special_tokens=True)
def summarize_text(input_text):
inputs = summarization_tokenizer(input_text, return_tensors="pt")
outputs = summarization_model.generate(**inputs)
return summarization_tokenizer.decode(outputs[0], skip_special_tokens=True)
def paraphrase_text(input_text):
inputs = paraphrase_tokenizer(input_text, return_tensors="pt")
outputs = paraphrase_model.generate(**inputs)
return paraphrase_tokenizer.decode(outputs[0], skip_special_tokens=True)
# Process input based on task
def process_text(text, task):
task_funcs = {
"Translate English to Bengali": translate_text_en_bn,
"Translate Bengali to English": translate_text_bn_en,
"Summarize": summarize_text,
"Paraphrase": paraphrase_text
}
return task_funcs.get(task, lambda x: "Invalid Task")(text)
# Task-specific examples
examples_en_bn_translation = [
["The sky is blue, and the weather is nice."],
["Artificial intelligence is shaping the future."],
["Bangladesh is known for its rich culture and heritage."]
]
examples_bn_en_translation = [
["বাংলাদেশ দক্ষিণ এশিয়ার একটি সার্বভৌম রাষ্ট্র।"],
["ঢাকা বাংলাদেশের রাজধানী।"],
["রবীন্দ্রনাথ ঠাকুরের গান বাংলা সংস্কৃতির একটি অবিচ্ছেদ্য অংশ।"]
]
examples_summarization = [
["The Department of Computer Science and Engineering, established in 1982, was the first of its kind in Bangladesh. "
"Attracting top students from all over the country, it offers both undergraduate and postgraduate degrees."],
["Climate change is one of the biggest challenges we face today. With rising temperatures and unpredictable weather, "
"the world needs to come together to find sustainable solutions."],
["Technology has advanced rapidly over the past decade, with innovations in fields like AI, robotics, and quantum computing."]
]
examples_paraphrasing = [
["The cat is sitting on the mat."],
["He was very happy to receive the award."],
["The weather today is sunny and warm."]
]
# Define the Gradio interface with enhanced visuals
iface = gr.Interface(
fn=process_text,
inputs=[
"text",
gr.Dropdown(
["Translate English to Bengali", "Translate Bengali to English", "Summarize", "Paraphrase"],
label="Choose Task",
elem_id="dropdown-task"
)
],
outputs="text",
title="BanglaT5 Model Hub",
description="A multi-functional tool for translation, summarization, and paraphrasing using BanglaT5 models.",
theme="finlaydog/seafoam",
examples=[
*examples_en_bn_translation, # Adding 3 examples for English to Bengali translation
*examples_bn_en_translation, # Adding 3 examples for Bengali to English translation
*examples_summarization, # Adding 3 examples for Summarization
*examples_paraphrasing # Adding 3 examples for Paraphrasing
],
allow_flagging="auto",
)
# Launch the Gradio app
iface.launch(inline=False)
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