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("

Time Scheduling Engine

", 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()