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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):
# Simple heuristic to detect questions
questions = [sentence.strip() + "?" for sentence in email_text.split(".") if "?" in sentence]
return questions
def generate_answers(question):
# Generate an answer for the given question using the BART model
inputs = tokenizer(question, return_tensors="pt", max_length=1024, truncation=True)
summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=50, early_stopping=True)
answer = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
return answer
def process_email(email_text):
questions = detect_questions(email_text)
responses = {}
for question in questions:
response = generate_answers(question)
responses[question] = response
return responses
iface = gr.Interface(
fn=process_email,
inputs="textbox",
outputs="text",
title="Email Question Responder",
description="Input an email, and the AI will detect questions and provide possible answers.",
)
iface.launch()
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