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import gradio as gr
from transformers import pipeline
from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("kriton/greek-text-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("kriton/greek-text-summarization")
generator = pipeline("summarization", model="kriton/greek-text-summarization")
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("kriton/greek-text-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("kriton/greek-text-summarization")
# Define the summary generation function
def genarate_summary(article):
inputs = tokenizer(
'summarize: ' + article,
return_tensors="pt",
max_length=1024,
truncation=True,
padding="max_length",
)
outputs = model.generate(
inputs["input_ids"],
max_length=512,
min_length=130,
length_penalty=3.0,
num_beams=8,
early_stopping=True,
repetition_penalty=3.0,
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Set up Gradio Interface
iface = gr.Interface(
fn=genarate_summary,
inputs="text",
outputs="text",
title="Greek Text Summarizer",
description="Enter an article in Greek, and this tool will generate a summary."
)
# Launch the Gradio Interface
iface.launch()
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