Spaces:
Sleeping
Sleeping
File size: 1,715 Bytes
b9a5258 eb22f3c b9a5258 ce2e5dd eb22f3c b9a5258 ce2e5dd b9a5258 eb22f3c fdf02d9 b9a5258 eb22f3c 1fc09bd eb22f3c fdf02d9 eb22f3c b9a5258 ce2e5dd b9a5258 eb22f3c |
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 |
import streamlit as st
import io
from contextlib import redirect_stdout
from dataloader import main as dtLoader
from evalue import main2 as run_eval
def main():
st.title("RGB Evaluation Tool")
st.subheader('Load Sample data')
sampleset = st.text_input("Sample data (e.g., en, en_int):", "en")
if st.button("Load Samples"):
st.write("loading samples...")
dataargs = ["--dataset", sampleset]
print('Samples loading.....')
ff = io.StringIO()
with redirect_stdout(ff):
dtLoader(dataargs)
out1 = ff.getvalue()
st.text_area("Execution Output", out1, height=300, disabled=True)
st.success("Samples loading. completed!")
st.subheader('Evaluation')
dataset = st.text_input("Dataset name (e.g., en, en_int):", "en")
model = st.text_input("Model name:", "groq")
plm = st.text_input("Plm:", "moonshotai/kimi-k2-instruct")
temp = st.slider("Temp", 0.0, 1.0, 0.2)
noise = st.slider("Noise Rate", 0.0, 1.0, 0.6)
passsage_num = st.slider("Passage Num", 0, 10, 5)
correct_rate = st.slider("Correct Rate", 0.0, 1.0, 1.0)
if st.button("Run Evaluation"):
args = ["--dataset", dataset, "--modelname", model, "--plm", plm, "--temp", str(temp), "--noise_rate", str(noise), "--passage_num", str(passsage_num), "--correct_rate", str(correct_rate)]
st.write("Running evaluation...")
f = io.StringIO()
with redirect_stdout(f):
run_eval(args)
output = f.getvalue()
st.text_area("Execution Output", output, height=300, disabled=True)
st.success("Evaluation completed!")
if __name__ == "__main__":
main() |