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import pandas as pd
import numpy as np
import json
import streamlit as st
from ConsoleApp import main
# from ConsoleApp import main_filter
# from ConsoleApp import get_filter
import sys
import hashlib
import json
import ast
import traceback
import hashlib
from PIL import Image
from itertools import chain
from bs4 import BeautifulSoup
# from session_state import SessionState
def load_file(df2, df_room):
list_course = []
index_count_course_id = 0
list_prof = []
index_count_prof_id = 0
for index1, row1 in df2.iterrows():
if row1['Course_Name'] not in list_course:
df2.at[index1, 'Course_id'] = index_count_course_id + 1
index_count_course_id += 1
list_course.append(row1['Course_Name'])
else:
df2.at[index1, 'Course_id'] = index_count_course_id
if row1['Prof_Name'] not in list_prof:
df2.at[index1, 'Prof_id'] = index_count_prof_id + 1
index_count_prof_id += 1
list_prof.append(row1['Prof_Name'])
else:
df2.at[index1, 'Prof_id'] = index_count_prof_id
# create list of dictionaries representing each object in the JSON file
objects = []
for index, row in df2.iterrows():
if row['Prof_id'] != '':
# create professor object
prof = {
"prof": {
"id": row['Prof_id'],
"name": row['Prof_Name']
}
}
if prof not in objects:
objects.append(prof)
if row['Course_id'] != '':
# create course object
course = {
"course": {
"id": row['Course_id'],
"name": row['Course_Name']
}
}
if course not in objects:
objects.append(course)
if row['Group_id'] != '':
# create group object
group = {
"group": {
"id": row['Group_id'],
"size": row['Size_Course']
}
}
# if group not in objects:
objects.append(group)
if row['Prof_id'] != '' and row['Course_id'] != '':
# create class object
class_ = {
"class": {
"professor": row['Prof_id'],
"course": row['Course_id'],
"duration": row['Duration'],
"group": row['Group_id'],
"lab": row['Lab']
}
}
if class_ not in objects:
objects.append(class_)
for index, row in df_room.iterrows():
if row['Room'] != '':
# create room object
room = {
"room": {
"name": row['Room'],
"lab": row['Lab'],
"size": row['Size_Room']
}
}
objects.append(room)
# create JSON object with list of objects
json_data = json.dumps(objects, sort_keys=False)
# write JSON object to file
with open('GaSchedule1.json', 'w') as f:
f.write(json_data)
file_name = "/GaSchedule1.json"
return file_name
def for_stu():
df_stu = pd.read_csv("data_stu.csv")
df_ctdt = pd.read_csv("ctdt_ds.csv")
df_ctdt = df_ctdt[['MaMH', 'Course Name', 'Sem', 'Expect Year', 'Credits', 'Elective']]
df_stu = df_stu[['MaSV', 'NHHK', 'HK', 'MaMH', 'TenMH', 'SoTinChi', 'DiemHP']]
df_stu = df_stu.dropna()
df_stu['NHHK'] = df_stu['NHHK'].astype(str).str[:-1]
input = st.text_input("Type Student ID", value="")
col1, col2, col3 = st.columns(3)
with col1:
if input:
# Convert 'DiemHP' column to numeric type, ignoring non-numeric values
df_stu['DiemHP'] = pd.to_numeric(df_stu['DiemHP'], errors='coerce')
list_subject_have_done = df_stu.loc[(df_stu['MaSV'].str.lower() == input.lower()) & (df_stu['DiemHP'].gt(50))]
list_subject_have_done[''] = np.arange(1, len(list_subject_have_done) + 1)
list_subject_have_done = list_subject_have_done.reindex(columns=['', 'MaMH', 'TenMH','HK', 'NHHK', 'SoTinChi'])
list_subject_have_done = list_subject_have_done.rename(columns={'NHHK': 'Actual Year', 'HK': 'Sem', 'TenMH': 'Course Name', 'SoTinChi': 'Credits'})
with st.expander("List of subjects have done"):
st.dataframe(list_subject_have_done.set_index(''))
with col2:
if input:
list_subject_havent_done_yet = df_ctdt[~df_ctdt['MaMH'].isin(list_subject_have_done['MaMH'])]
list_subject_havent_done_yet['Expect Year'] = list_subject_havent_done_yet['Expect Year'].astype(str)
list_subject_havent_done_yet['Elective'] = list_subject_havent_done_yet['Elective'].astype(bool)
list_subject_havent_done_yet[''] = np.arange(1, len(list_subject_havent_done_yet) + 1)
list_subject_havent_done_yet = list_subject_havent_done_yet.reindex(columns=['', 'MaMH', 'Course Name', 'Credits', 'Elective', 'Expect Year', 'Sem'])
with st.expander("List of subjects haven't done yet"):
st.dataframe(list_subject_havent_done_yet.set_index(''))
with col3:
if input:
df_unique = df2[df2['Group_Lab'] == 1.0]
df_unique = df_unique[['MaMH', 'Course_Name', 'Prof_Name', 'Duration', 'Group_Lab', 'Size_Course']]
list_recommend_subjects = df_unique[df_unique['MaMH'].isin(list_subject_havent_done_yet['MaMH'])]
list_recommend_subjects[''] = np.arange(1, len(list_recommend_subjects) + 1)
list_recommend_subjects = list_recommend_subjects.reindex(columns=['', 'MaMH', 'Course_Name', 'Prof_Name', 'Duration', 'Group_Lab', 'Size_Course'])
with st.expander("List of recommend subjects in this semester"):
st.dataframe(list_recommend_subjects.set_index(''))
def get_filter(html, list_filter):
# Parse the HTML
soup = BeautifulSoup(html, 'html.parser')
# Find all div elements with id starting with 'room_'
div_elements = soup.find_all('div', id=lambda x: x and x.startswith('room_'))
# Filter and display the schedule for specific rooms
filtered = ""
for div in div_elements:
room_id = div['id'].replace('room_', '') # Extract the room ID from the div's id attribute
if room_id in list_filter:
filtered += str(div)
return filtered
def data_display():
uploaded_file = st.file_uploader('')
if uploaded_file is not None:
df = pd.read_csv(uploaded_file)
else:
df = [['Data Mining', 1, 35, 4, 'Nguyen Thi Thanh Sang', 'IT132IU'],
['Analytics for Observational Data', 2, 35, 4, 'Nguyen Thi Thanh Sang', 'IT142IU'],
['Fundamentals of Programming', 0, 90, 3, 'Dao Tran Hoang Chau', 'IT149IU'],
['Object-Oriented Analysis and Design', 0, 90, 4, 'Ha Viet Uyen Synh', 'IT090IU']]
room_columns = ['TenMH', 'ToTH', 'TongSoSV', 'SoTiet', 'TenDayDuNV', 'MaMH']
df = pd.DataFrame(df, columns=room_columns)
df1 = df[['TenMH', 'ToTH', 'TongSoSV', 'SoTiet', 'TenDayDuNV', 'MaMH']]
df1 = df1.rename(columns={'TenMH': 'Course_Name', 'ToTH': 'Group_Lab', 'TongSoSV': 'Size_Course', 'SoTiet': 'Duration', 'TenDayDuNV': 'Prof_Name', 'MaMH': 'MaMH'})
df1['Lab'] = df1['Group_Lab']
# df1['Lab'] = df1['Lab'].astype(str)
for index, row in df1.iterrows():
if row['Lab'] == 1 or row['Lab'] == 2 or row['Lab'] == 3 or row['Lab'] == 4:
df1.at[index, 'Lab'] = 'True'
else:
df1.at[index, 'Lab'] = ''
df1['Lab'] = df1['Lab'].astype(bool)
## create default room
room_default = [['A1.309', 90, 0],
['L107', 90, 0],
['LA1.605', 60, 1],
['La1.607', 60, 1]
]
room_columns = ['Room', 'Size_Room', 'Lab']
df_room = pd.DataFrame(room_default, columns=room_columns)
df_room['Lab'] = df_room['Lab'].astype(str)
for index, row in df_room.iterrows():
if row['Lab'] == '1':
df_room.at[index, 'Lab'] = 'True'
else:
df_room.at[index, 'Lab'] = ''
df_room['Lab'] = df_room['Lab'].astype(bool)
# col1, col2, col3, col4 = st.columns([0.5,7,2.4,0.5])
col1, col2 = st.columns([9,4])
with col1:
df2 = st.experimental_data_editor(df1, num_rows="dynamic")
df2['Size_Course'] = df2['Size_Course'].astype(int)
df2['Duration'] = df2['Duration'].astype(int)
df2['Group_id'] = np.arange(1, len(df2) + 1)
df_prof_filter = df2['Prof_Name'].drop_duplicates().tolist()
with col2:
df_room = st.experimental_data_editor(df_room, num_rows="dynamic")
df_room['Size_Room'] = df_room['Size_Room'].astype(int)
filter = df_room['Room'].to_list()
# df_room_filter = df_room[df_room['Room'].isin(list_filter)]
return df2, df_room, filter, df_prof_filter
st.set_page_config(layout="wide")
if __name__ == "__main__":
st.markdown("<h1 style='text-align: center; color: white;'>Time Scheduling Engine</h1>", unsafe_allow_html=True)
image = Image.open('logo-vector-IU-01.png')
st.sidebar.image(image, width=240)
tab1, tab2 = st.tabs(["Schedule", "Student"])
with tab1:
df2, df_room, filter, df_prof_filter = data_display()
file_name = load_file(df2, df_room)
# html_result_filter = main_filter(file_name)
html_result = main(file_name)
if 'html_result' not in st.session_state:
st.session_state.html_result = []
list_room_filter = st.sidebar.multiselect('Room Filter', filter, filter)
# list_prof_filter = st.sidebar.multiselect('Prof Filter', df_prof_filter, df_prof_filter)
if st.button('Generate'):
st.session_state.html_result = html_result
st.markdown(st.session_state.html_result, unsafe_allow_html=True)
if st.sidebar.button('Room Filter'):
filtered1 = get_filter(st.session_state.html_result, list_room_filter)
st.markdown(filtered1, unsafe_allow_html=True)
# if len(sys.argv) > 1:
# file_name = sys.argv[1]
# try:
# if st.button('Generate'):
# temp = session_state['html_result']
# filtered1 = get_filter(temp, list_filter)
# st.markdown(filtered1, unsafe_allow_html=True)
# except:
# traceback.print_exc()
with tab2:
for_stu()
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