File size: 2,306 Bytes
826e275 7a75a15 826e275 d73a8e9 826e275 7a75a15 7b34e37 826e275 e0bb50d b598d9f 826e275 7a75a15 0b321da 826e275 0b321da 826e275 b1dd47e 826e275 0e07a66 d73a8e9 826e275 0499581 826e275 b1dd47e 826e275 b1dd47e 0499581 d73a8e9 b1dd47e d73a8e9 9c8dd72 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
"""
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)
|