import gradio as gr
from transformers import pipeline

# Create a text generation pipeline using Transformers
text_generation_pipeline = pipeline("text-generation", model="nlpaueb/legal-bert-base-uncased")  # Replace with the actual model name

def generate_explanation(input_text):
    # Generate a paragraph explaining the details of the input text
    explanation = text_generation_pipeline(input_text, max_length=200)  # Adjust max_length as needed
    return explanation[0]["generated_text"]

# Create a Gradio interface
iface = gr.Interface(
    fn=generate_explanation,
    inputs=gr.Textbox("text", label="Legal Document", lines=5),
    outputs=gr.Textbox(label="Explanation"),

)

if __name__ == "__main__":
    iface.launch()