| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| import nltk | |
| nltk.download('punkt') | |
| tokenizer = AutoTokenizer.from_pretrained("anukvma/bart-base-medium-email-subject-generation-v5") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("anukvma/bart-base-medium-email-subject-generation-v5") | |
| text = """ | |
| Harry - I got kicked out of the system, so I'm sending this from Tom's account. | |
| He can fill you in on the potential deal with STEAG. | |
| I left my resume on your chair. | |
| I'll e-mail a copy when I have my home account running. | |
| My contact info is: | |
| """ | |
| inputs = ["provide email subject: " + text] | |
| 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] | |
| print(predicted_title) | |
| def generate_subject(text): | |
| inputs = ["provide email subject: " + text] | |
| 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 | |
| import gradio as gr | |
| gr.Interface(fn = generate_subject, inputs="text",outputs="text").launch() |