"""
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 torch.cuda import CudaError

from hanlde_form_submit import on_form_submit
from on_server_start import main as on_server_start_main

on_server_start_main()

st.title("Grouped Sampling Demo")

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"Supported models are all the models in:"
             f" https://huggingface.co/models?pipeline_tag=text-generation&library=pytorch",
    )

    output_length: int = st.number_input(
        label="Number of word pieces in the generated text, 1-4096 (default: 100)",
        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, It is highly recommended to write an English prompt.",
        help="Enter the prompt for the model. The model will generate a response based on this prompt.",
        value="Instruction: Answer in yes or no.\n"
              "Question: Is this a prompt?\n"
              "Answer: ",
        max_chars=2048,
    )

    web_search: bool = st.checkbox(
        label="Web search",
        value=True,
        help="If checked, the model will get your prompt as well as some web search results."
    )

    submitted: bool = st.form_submit_button(
        label="Generate",
        help="Generate the output text.",
        disabled=False,
    )

    if submitted:
        try:
            output = on_form_submit(
                selected_model_name,
                output_length,
                submitted_prompt,
                web_search,
            )
        except CudaError as e:
            st.error("Out of memory. Please try a smaller model, shorter prompt, or a smaller output length.")
        except (ValueError, TypeError, RuntimeError) as e:
            st.error(e)
        st.write(f"Generated text: {output}")


with open("user_instructions_hebrew.md", "r") as fh:
    long_description = fh.read()
st.markdown(long_description)

await on_server_start_main()