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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import nltk
nltk.download('punkt')
def generate_subject(model_name,email_body):
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
inputs = ["provide email subject: " + email_body]
inputs = tokenizer(inputs, max_length=512, truncation=True, return_tensors="pt")
output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=1, max_length=10)
decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
predicted_title = nltk.sent_tokenize(decoded_output.strip())[0]
return predicted_title
iface = gr.Interface(
fn=generate_subject,
inputs=[
gr.Dropdown(choices=["anukvma/t5-base-medium-email-subject-generation-v2", "anukvma/bart-base-medium-email-subject-generation-v5"], label="Select Model"),
gr.Textbox(lines=5, label="Email Body")
],
outputs=gr.Textbox(label="Model Summary")
)
iface.launch() |