Spaces:
Sleeping
Sleeping
File size: 3,685 Bytes
84ac28f |
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 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
import subprocess
import time
import streamlit as st
import pandas as pd
def fn_process_excel(lv_file_name,ui_status_message,sm):
try:
# Read the Excel file
excel_data = pd.read_excel(lv_file_name,sheet_name=None)
# Iterate over each sheet in the Excel file
for sheet_name, sheet_data in excel_data.items():
# Process the data in each sheet
print(f"Sheet Name: {sheet_name}")
print(sheet_data.head())
# Display a success message
sm.fn_display_status_messages("Excel data processed successfully!", "Success", ui_status_message)
except Exception as e:
# Display an error message if there is an exception
sm.fn_display_status_messages(f"Error: {e}", "Error", ui_status_message)
# Function to start the Flask server
def start_flask_server():
"""
Function to start the Flask server.
Returns:
None
"""
lv_proc = subprocess.Popen(["python", "flask_app.py"])
st.session_state.sv_flask_server_proc = lv_proc
# Function to stop the Flask server
def stop_flask_server():
"""
Function to stop the Flask server.
Returns:
None
"""
lv_proc = st.session_state.sv_flask_server_proc
if lv_proc is not None:
lv_proc.terminate()
lv_proc.wait(timeout=5)
if lv_proc.poll() is None:
lv_proc.kill()
st.session_state.sv_flask_server_proc = None
# Function to configure the sidebar UI elements.
def fn_sidebar_configuration(ui_status_message,sm):
"""
Function to configure the sidebar UI elements.
Parameters:
- ui_status_message (str): The status message to be display in UI.
Returns:
None
"""
try:
if not(st.session_state.sv_flask_server_proc):
# Start Flask Server
start_flask_server()
# Toggle button to enable/disable training mode
ui_training_indicator = st.toggle("Training", value=False)
# Container to hold the UI elements
ui_container = st.container(border=True)
with ui_container:
# Training new domain
if ui_training_indicator:
# File uploader for training file
ui_training_file = st.file_uploader("Training File", type=["xlsx"], accept_multiple_files=False, key="training_file")
# Text input for domain name
ui_domain_name = st.text_input("Domain Name")
# Button to submit the form
if st.button("Submit"):
# Check if the training file is uploaded
if ui_training_file is not None and ui_domain_name != "":
lv_file_name = "storage/"+ui_domain_name+".xlsx"
# Saving File
with open(lv_file_name, "wb") as f:
f.write(ui_training_file.getvalue())
# Process the Excel file
fn_process_excel(lv_file_name, ui_status_message,sm)
else:
# Display an error message if the training file is not uploaded
sm.fn_display_status_messages("Please upload the training file.", "Error", ui_status_message)
# Mapping data to trained domain
else:
# Selectbox for domain selection
ui_training_vector_db = st.selectbox("Domain", ["OFSLL", "OBRL"])
except Exception as e:
# Display an error message if there is an exception
sm.fn_display_status_messages(f"Error: {e}", "Error", ui_status_message)
|