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()