| """ | |
| The Streamlit app for the project demo. | |
| In the demo, the user can write a prompt | |
| and the model will generate a response using the grouped sampling algorithm. | |
| """ | |
| import streamlit as st | |
| from hanlde_form_submit import on_form_submit | |
| from on_server_start import main as on_server_start_main | |
| AVAILABLE_MODEL_NAMES = "https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads" | |
| with st.form("request_form"): | |
| selected_model_name: str = st.text_input( | |
| label="Model name", | |
| value="gpt2", | |
| help=f"The name of the model to use." | |
| f" Must be a model from this list:" | |
| f" {AVAILABLE_MODEL_NAMES}" | |
| ) | |
| output_length: int = st.number_input( | |
| label="Output Length in tokens", | |
| min_value=1, | |
| max_value=4096, | |
| value=100, | |
| help="The length of the output text in tokens (word pieces)." | |
| ) | |
| submitted_prompt: str = st.text_area( | |
| label="Input for the model", | |
| help="Enter the prompt for the model. The model will generate a response based on this prompt.", | |
| max_chars=16384, | |
| ) | |
| submitted: bool = st.form_submit_button( | |
| label="Generate", | |
| help="Generate the output text.", | |
| disabled=False | |
| ) | |
| if submitted: | |
| output = on_form_submit(selected_model_name, output_length, submitted_prompt) | |
| st.write(f"Generated text: {output}") | |