| 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() |