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import gradio as gr
from transformers import pipeline
import re
pipe = pipeline("summarization", model="kkasiviswanath/bart_summarizer_deploy_v1")
def summarize_email(email_body, pipe):
# Tokenize the input text
input_tokens = pipe.tokenizer(email_body, return_tensors='pt', truncation=False)
input_length = input_tokens['input_ids'].shape[1]
# Adjust max_length to be a certain percentage of the input length
adjusted_max_length = max(3, int(input_length * 0.6)) # Ensure a minimum length
# Generate summary with dynamic max_length
gen_kwargs = {
"length_penalty": 2.0,
"num_beams": 5,
"max_length": adjusted_max_length,
"min_length": 3
}
summary = pipe(email_body, **gen_kwargs)[0]['summary_text']
return summary
# Generate summaries for the test dataset
def generate_summary(text):
email_body = re.sub(r'\s+', ' ', re.sub(r'[^\w\s]', '', text).strip())
summary = summarize_email(email_body, pipe)
return summary
def greet(name):
return "Hello " + name + "!!"
demo = gr.Interface(fn=generate_summary, inputs="text", outputs="text")
demo.launch(share=True) |