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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() | |