import gradio as gr
from transformers import pipeline, BartTokenizer, BartForConditionalGeneration

# Load the BART model and tokenizer for text generation
model_name = "facebook/bart-base"
tokenizer = BartTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)

def detect_questions(email_text):
    # Use the BART model to generate questions from the email text
    inputs = tokenizer("generate questions: " + email_text, return_tensors="pt", max_length=1024, truncation=True)
    questions = model.generate(inputs["input_ids"], num_beams=4, max_length=50, early_stopping=True)
    questions = tokenizer.decode(questions[0], skip_special_tokens=True)
    return questions.split("##")

def generate_responses(questions):
    responses = {}
    for question in questions:
        # Generate a response for each question using the BART model
        inputs = tokenizer(question, return_tensors="pt", max_length=1024, truncation=True)
        response = model.generate(inputs["input_ids"], num_beams=4, max_length=200, early_stopping=True)
        response = tokenizer.decode(response[0], skip_special_tokens=True)
        responses[question] = response
    return responses

def process_email(email_text):
    # Detect questions from the email
    questions = detect_questions(email_text)
    
    # Generate responses to the detected questions
    responses = generate_responses(questions)
    
    return responses

iface = gr.Interface(
    fn=process_email,
    inputs="textbox",
    outputs="text",
    title="Email Question Detector and Responder",
    description="Input an email, and the AI will detect questions and provide possible responses.",
)

iface.launch()