import gradio as gr
from transformers import pipeline

# Initialize the text generation pipeline using the google/flan-t5-base model
pipe = pipeline("text2text-generation", model="google/flan-t5-base")

# Define a function to generate text using the Flan-T5 model
def generate_text(input_text):
    response = pipe(input_text)
    return response[0]['generated_text']

# Set up the Gradio interface
iface = gr.Interface(
    fn=generate_text,  # The function to generate text
    inputs="text",  # Input type is a text field
    outputs="text",  # Output is displayed as text
    title="Flan-T5 Text Generation",  # Title of the interface
    description="Enter text to generate a response using the google/flan-t5-base model."  # Description
)

# Launch the Gradio interface
if __name__ == "__main__":
    iface.launch(share=True)