Update app.py
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app.py
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import gradio as gr
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from transformers import
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# Load the BART model and tokenizer
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model_name = "facebook/bart-large-cnn"
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tokenizer = BartTokenizer.from_pretrained(model_name)
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model = BartForConditionalGeneration.from_pretrained(model_name)
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# Create a Gradio interface
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iface = gr.Interface(
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fn=
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inputs="textbox",
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outputs="
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title="Email Question
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description="Input an email, and the AI will
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# Launch the interface
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import re
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import gradio as gr
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from transformers import pipeline, BartTokenizer, BartForConditionalGeneration
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# Load the BART model and tokenizer for text generation (answer suggestions)
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model_name = "facebook/bart-large-cnn"
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tokenizer = BartTokenizer.from_pretrained(model_name)
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model = BartForConditionalGeneration.from_pretrained(model_name)
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# Question detection function
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def detect_questions(email_text):
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# Use regex to find questions in the email
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questions = re.findall(r'([^\.\!\?]*\?)', email_text)
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return questions
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# Generate answers using the BART model
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def generate_answers(question):
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# Use the BART model to generate a response
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inputs = tokenizer(question, return_tensors="pt", max_length=1024, truncation=True)
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summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=50, early_stopping=True)
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answer = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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return answer
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# Main function to handle the email input
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def process_email(email_text):
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questions = detect_questions(email_text)
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responses = {}
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for question in questions:
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response = generate_answers(question)
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responses[question] = response
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return responses
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# Create a Gradio interface
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iface = gr.Interface(
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fn=process_email,
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inputs="textbox",
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outputs="json",
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title="Email Question Detector and Responder",
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description="Input an email, and the AI will detect questions and provide suggested responses.",
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)
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# Launch the interface
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