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import gradio as gr
from transformers import pipeline
import csv
#model_id = "pszemraj/long-t5-tglobal-base-16384-book-summary"
#summarizer = pipeline("summarization", model=model_id)
model_id = "google/flan-t5-large"
summarizer = pipeline("text2text-generation", model=model_id)
def summarize(text):
text = str(text)
#if text == "showdata":
# lines = "(lines)"
# with open('input.csv',"r") as f:
# lines = f.readlines()
# return str(lines)
#generated_summary_short = summarizer(text, max_length=40, min_length=10)[0]['summary_text']
#generated_summary = summarizer(text, max_length=80, min_length=20)[0]['summary_text']
#generated_summary = summarizer(text, max_length=200, min_length=40)[0]['summary_text']
generated_summary = summarizer(text, max_length=200, min_length=40)[0]['generated_text']
#fields = [str(text), str(generated_summary)]
#with open('input.csv','a', newline='') as f:
# writer = csv.writer(f)
# writer.writerow(fields)
#return "Summary: " + str(generated_summary) + "\n\n" + "shorter: " + str(generated_summary_short)+ "\n\n" + "Longer: " + str(generated_summary_long)
return str(generated_summary)
iface = gr.Interface(fn=summarize, inputs="text", outputs="text", allow_flagging="never", queue = True)
if __name__ == "__main__":
iface.launch() |