app.py

import gradio as gr
from transformers import pipeline

# Load the pipeline for text2text-generation using the Blenderbot model
pipe = pipeline(task="text2text-generation", model="facebook/blenderbot-400M-distill")


# Define the text generation function
def generate_text(prompt, max_length=50):
    response = pipe(prompt, max_length=max_length, truncation=True)
    return response[0]['generated_text']

# Create the Gradio app interface
with gr.Blocks() as app:
    gr.Markdown("## Text-to-Text Generation App")
    gr.Markdown(
        "Enter a prompt below and the model will generate text. "
        "This app uses the `facebook/blenderbot-400M-distill` model."
    )
    
    with gr.Row():
        input_prompt = gr.Textbox(label="Input Prompt", placeholder="Type your prompt here...")
        output_text = gr.Textbox(label="Generated Text")
    
    max_length = gr.Slider(label="Max Length", minimum=10, maximum=200, step=10, value=50)
    
    submit_button = gr.Button("Generate")
    
    submit_button.click(
        fn=generate_text,
        inputs=[input_prompt, max_length],
        outputs=output_text
    )

# Launch the app
if __name__ == "__main__":
    app.launch()