app.py

import gradio as gr
from transformers import pipeline
model_id = "knkarthick/MEETING_SUMMARY"

generator = pipeline(task="text2text-generation", model=model_id)

#def split_paragraph(paragraph, max_chunk_size=1024):
#    words = paragraph.split()
#    chunks = []
#    current_chunk = []
#    current_chunk_size = 0

#    for word in words:
#        word_len = len(word) + 1  # Add 1 for the space
#        if current_chunk_size + word_len <= max_chunk_size:
#            current_chunk.append(word)
#            current_chunk_size += word_len
#        else:
#            chunks.append(' '.join(current_chunk))
#            current_chunk = [word]
#            current_chunk_size = word_len

#    if current_chunk:
#        chunks.append(' '.join(current_chunk))

#    return chunks

def launch(input):
#    if len(input) > 1024:
#        return " ".join([res["generated_text"] for res in generator(split_paragraph(input))])
    return generator(input)[0]["generated_text"]

iface = gr.Interface(launch, inputs="text", outputs="text")
iface.launch()