app.py

from transformers import pipeline
import gradio as gr

# Load the text-to-text generation pipeline using the FLAN-T5 model
pipe = pipeline("text2text-generation", model="google/flan-t5-large")

# Function to generate text based on user input
def generate_text(input_text):
    # Generate output text using the pipeline
    generated = pipe(input_text)
    return generated[0]['generated_text']

# Set up the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Text Generation using FLAN-T5 (google/flan-t5-large)")

    # Input for user to provide text
    text_input = gr.Textbox(label="Enter Text Prompt", placeholder="Type your prompt here...", value="Translate English to French: 'Hello, how are you?'")
    
    # Output to display the generated text
    output_text = gr.Textbox(label="Generated Text", interactive=False)

    # Button to trigger text generation
    generate_button = gr.Button("Generate Text")

    # Link button click to text generation function
    generate_button.click(fn=generate_text, inputs=text_input, outputs=output_text)

# Launch the Gradio app
demo.launch()