/
app.py
50 lines (37 loc) · 1.59 KB
/
app.py
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import streamlit.components.v1 as components
import csv
import re
import streamlit as st
import zipfile
import pandas as pd
import os
from model import model
import base64
import warnings
def app():
path=os.getcwd()
st.set_option('deprecation.showfileUploaderEncoding', False)
data_file = st.file_uploader('Upload', type="zip", encoding="latin1")
st.set_option('deprecation.showfileUploaderEncoding', False)
#text_io = io.TextIOWrapper(data_file)
warnings.filterwarnings(action= 'ignore')
if st.button("Classify"):
if (data_file is not None):
zf = zipfile.ZipFile(data_file)
file_path=zipfile.Path(data_file)
file_path_name=str(file_path)
files=zf.extractall()
folder_path=path + "\\" + file_path_name
folder_path=folder_path.replace(".zip/","")
extract(folder_path)
os.chdir(path)
csv_path=folder_path +"\\"+ "test.csv"
df_test=pd.read_csv(csv_path)
st.dataframe(df_test)
final_df = model(df_test )
csv=final_df.to_csv(index = False)
st.dataframe(final_df)
b64 = base64.b64encode(csv.encode()).decode()
st.markdown('### **⬇️ Download output CSV File **')
st.markdown(f'<a href="data:file/csv;base64,{b64}" download="Prediction.csv">Download the output file</a>', unsafe_allow_html=True)
st.balloons()