| from transformers import pipeline | |
| import gradio as gr | |
| # Load the desired model using Hugging Face's model hub | |
| model = pipeline(model="philschmid/bart-large-cnn-samsum") | |
| def generate_text(input_text): | |
| # Set the maximum response size to 100 characters | |
| output = model(input_text, max_length=100, do_sample=True) | |
| # Access the generated response | |
| response = output[0]['summarized_text'] | |
| return response | |
| iface = gr.Interface( | |
| fn=generate_text, | |
| inputs=gr.inputs.Textbox("Input Text"), | |
| outputs="text", | |
| title="Text Generation App", | |
| description="Enter an input text and get a generated response (limited to 100 characters)." | |
| ) | |