app.py

import gradio as gr
from transformers import BartForConditionalGeneration, BartTokenizer

# Load the BART model and tokenizer
model_name = "facebook/bart-large-cnn"
tokenizer = BartTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)

def generate_questions(email_text):
    # Preprocess the email text for the BART model
    inputs = tokenizer(email_text, return_tensors="pt", max_length=1024, truncation=True)

    # Generate questions
    summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=50, early_stopping=True)
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

    return summary

# Create a Gradio interface
iface = gr.Interface(
    fn=generate_questions,
    inputs="textbox",
    outputs="textbox",
    title="Email Question Generator",
    description="Input an email, and the AI will generate the biggest questions that probably need to be answered.",
)

# Launch the interface
iface.launch()