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import gradio as gr
from huggingface_hub import InferenceClient
import gradio as gr
from transformers import pipeline
# Load the summarization pipeline with your pre-trained model
pipe = pipeline("summarization", model="paramasivan27/t5small_for_email_summarization_enron")
# Function to summarize email
def summarize_email(email_body):
# Tokenize the input text
pipeline = pipe
input_tokens = pipeline.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(10, int(input_length * 0.6)) # Ensure a minimum length
# Generate summary with dynamic max_length
gen_kwargs = {
"length_penalty": 2.0,
"num_beams": 4,
"max_length": adjusted_max_length,
"min_length": 3
}
summary = pipeline(email_body, **gen_kwargs)[0]['summary_text']
return summary
# Create the Gradio interface
iface = gr.Interface(
fn=summarize_email,
inputs=gr.Textbox(lines=10, placeholder="Enter the email body here..."),
outputs="text",
title="Email Subject Line Generator",
description="Generate a subject line from an email body using T5 Small"
)
# Launch the interface
iface.launch()
if __name__ == "__main__":
demo.launch() |