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import streamlit as st
from streamlit.logger import get_logger
from transformers import pipeline
import datasets
import pandas as pd
from huggingface_hub import login
LOGGER = get_logger(__name__)
@st.cache_data
def get_df() ->object:
ds = datasets.load_dataset('sivan22/orach-chaim')
df = ds['train'].to_pandas()
def clean(s)->str:
return s.replace(" ","")
df['seif']= df['seif'].apply(clean)
return df
@st.cache_resource
def get_model()->object:
model = "sivan22/halacha-siman-seif-classifier"
classifier = pipeline("text-classification",model=model,top_k=None)
return classifier
def get_predicts(classifier,input)->str:
predicts = classifier(input)
return predicts
def run():
st.set_page_config(
page_title="讞讬驻讜砖 讞讻诐 讘砖讜诇讞谉 注专讜讱",
page_icon="馃摎",
)
st.write("# 讞讬驻讜砖 讞讻诐 讘砖讜诇讞谉 注专讜讱")
classifier = get_model()
df = get_df()
user_input = st.text_input('讻转讜讘 讻讗谉 讗转 砖讗诇转讱', placeholder='讻诪讛 谞专讜转 诪讚诇讬拽讬诐 讘讻诇 诇讬诇讛 诪诇讬诇讜转 讛讞谞讜讻讛')
num_of_results = st.sidebar.slider('诪住驻专 讛转讜爪讗讜转 砖讘专爪讜谞讱 诇讛爪讬讙:',1,25,5)
if st.button('讞驻砖') and user_input!="":
for prediction in get_predicts(classifier,user_input)[0][:num_of_results]:
siman = prediction['label'].split(' ')[0]
seif = prediction['label'].split(' ')[1]
rows = df[((df["bookname"] == " 砖诇讞谉 注专讜讱 - 讗讜专讞 讞讬讬诐 ") | (df["bookname"] ==" 诪砖谞讛 讘专讜专讛")) &
(df["siman"] == siman) &
(df["seif"] == seif) ]
rows = rows.sort_values(["bookname"],ascending=False)
st.write(('住讬诪谉 ' + siman + ' 住注讬祝 ' + seif), rows[['text','bookname','sek','seif','siman',]])
if __name__ == "__main__":
run()
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