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app.py
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app.py
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import streamlit as st
import pandas as pd
import numpy as np
import streamlit.components.v1 as components
st.set_page_config(
page_title="MMP Screening App",
page_icon="⛵️",
layout="wide",
initial_sidebar_state="expanded",
menu_items={
"About": """Find more about TYRA
https://linktr.ee/projecttyra
If you find this project useful, please consider giving us a star on GitHub:
https://github.com/ChenHsieh/MMP_screening
"""
},
)
st.title("project Tyra - Mentor Dashboard")
mentee_response_sheet_url = st.secrets["mentee_response_sheet_url"]
mentee_sheet_url = st.secrets["mentee_sheet_url"]
mentor_sheet_url = st.secrets["mentor_sheet_url"]
mentee_response_sheet_url = mentee_response_sheet_url.replace(
"/edit#gid=", "/export?format=csv&gid="
)
mentee_sheet_url = mentee_sheet_url.replace("/edit#gid=", "/export?format=csv&gid=")
mentor_sheet_url = mentor_sheet_url.replace("/edit#gid=", "/export?format=csv&gid=")
mentee_table = pd.read_csv(mentee_sheet_url)
mentor_table = pd.read_csv(mentor_sheet_url)
# mentor_table = pd.read_csv("mentors_processed.csv")
mentor_table.set_index("verification_code2", inplace=True)
@st.cache_data
def load_mentee_data(mentee_name_list):
data = pd.read_csv(mentee_response_sheet_url)
data = data.loc[data["中文姓名"].isin(mentee_name_list)]
return data
@st.cache_data
def convert_df(df):
# IMPORTANT: Cache the conversion to prevent computation on every rerun
return df.to_csv().encode("utf-8")
display_columns = [
"志願序",
"中文姓名",
"申請年份",
"欲申請學位",
"最高學歷",
"學士就讀/畢業學校",
"學士就讀/畢業系所",
"碩士就讀/畢業學校",
"碩士就讀/畢業系所",
"廣義研究領域",
"專業領域",
"主要申請系所所在國家",
"欲申請學校",
"欲申請系所/program",
"相關的選擇學校、系所的理由",
"欲尋求之協助/建議(至多三個)",
"請問您目前準備進度為何？是否已經完成選校？",
"提供簡歷與相關資料",
"是否已經參加留學國的語文程度測驗？",
"目前申請文件的準備進度？",
"目前是否已開始聯繫請求推薦信。",
"是否已經參加標準化入學考試？",
"任何想對未來導師說的話",
"任何公開資訊 （選填）",
"是否為家族中第一代高等教育子女（選填）",
"國際活動經驗（選填）",
"學術領域外相關特殊專長、經驗或成就（選填）",
"家人、伴侶等狀態（選填）",
"目前的生涯規劃，或主要申請目的與動機（選填）",
"您認為可能會影響申請過程或結果的身份、背景、經歷，而您希望導師知道的（選填）",
"任何想補充給導師知道的（選填）",
"電子郵件地址",
"其餘聯絡方式 (非必填)",
]
mentor_verification_code = st.text_input(
'Please input your latest "verification code" from the email. Please note that the "verification code" is case-sensitive.',
"",
)
if mentor_verification_code == "":
st.warning(f"The input is empty!")
st.stop()
elif (
(mentor_verification_code in mentor_table["name"].values)
| (mentor_verification_code in mentor_table["mentor_id"].values)
| (mentor_verification_code in mentor_table["combined_mentor_id"].values)
| (mentor_verification_code in mentor_table["email"].values)
):
st.warning(
f"Please input the verification code instead of personal information. Please check your verification code from the email we sent to you."
)
st.stop()
elif mentor_verification_code in mentor_table.index:
st.success(
f"Hola {mentor_table.loc[mentor_verification_code]['name']}! Welcome to the mentor dashboard!"
)
mentor_table["mentee_MSc"].fillna("", inplace=True)
mentor_table["mentee_PhD"].fillna("", inplace=True)
# f"You have been matched with {len(mentor_table.loc[mentor_verification_code]['mentee_MSc'].split(' '))} mentees for master program."
# f"You have been matched with {len(mentor_table.loc[mentor_verification_code]['mentee_PhD'].split(' '))} mentees for PhD program."
else:
st.warning(
f"Oops! We cannot find any results for the current input. Please check your verification code."
)
st.stop()
mentee_id_list = mentor_table.loc[mentor_verification_code]["mentee_MSc"].split(
" "
) + mentor_table.loc[mentor_verification_code]["mentee_PhD"].split(" ")
if "" in mentee_id_list:
mentee_id_list.remove("")
mentee_table.set_index("mentee_id", inplace=True)
mentee_name_list = mentee_table.loc[mentee_id_list, "name"]
mentor_name = mentor_table.loc[mentor_verification_code, "combined_mentor_id"]
mentee_response = load_mentee_data(mentee_name_list)
candidate_mentee_number = mentee_response.shape[0]
if candidate_mentee_number == 0:
st.warning(
f"Oops! We cannot find any results for the current input. Please check your verification code."
)
st.stop()
elif candidate_mentee_number == 1:
st.success(f'Great! {"、".join(mentee_response["中文姓名"].values)} matched with you!')
else:
st.success(f'Great! {"、".join(mentee_response["中文姓名"].values)} matched with you!')
# extract the ranking of the mentor from the mentee response
mentee_response.loc[mentee_response["希望配對的導師（第五志願）.1"] == mentor_name, "志願序"] = "5"
mentee_response.loc[mentee_response["希望配對的導師（第四志願）.1"] == mentor_name, "志願序"] = "4"
mentee_response.loc[mentee_response["希望配對的導師（第三志願）.1"] == mentor_name, "志願序"] = "3"
mentee_response.loc[mentee_response["希望配對的導師（第二志願）.1"] == mentor_name, "志願序"] = "2"
mentee_response.loc[mentee_response["希望配對的導師（第一志願）"] == mentor_name, "志願序"] = "1"
# filter the columns to be shown
mentee_response = mentee_response[display_columns].sort_values(by="志願序")
if st.checkbox("Show and download raw mentee response"):
st.write(mentee_response.set_index("中文姓名"))
csv = convert_df(mentee_response.set_index("中文姓名"))
st.download_button(
label="Download data as CSV",
data=csv,
file_name=f"{mentor_name}_candidate_mentee.csv",
mime="text/csv",
)
st.divider()
st.header("Know your mentee better!")
if candidate_mentee_number == 1:
viewing_mode = "Single Mentee Info"
elif candidate_mentee_number > 1:
viewing_mode = st.radio(
"Choose your mentee data display mode",
["Single Mentee Info", "Multiple Mentee Info"],
# key="viewing_mode",
horizontal=True,
)
if viewing_mode == "Single Mentee Info":
if candidate_mentee_number == 1:
mentee_name = mentee_response["中文姓名"].values[0]
if candidate_mentee_number > 1:
mentee_name = st.selectbox(
"You can select one of the candidate mentees to see their profile.",
mentee_response["中文姓名"].values,
)
current_mentee = mentee_response.loc[mentee_response["中文姓名"] == mentee_name]
col1, col2 = st.columns(2)
with col1:
st.metric(
f"選擇您為第 {current_mentee['志願序'].values[0]} 志願",
current_mentee["中文姓名"].values[0],
)
st.write(
f"申請 {current_mentee['申請年份'].values[0]} {current_mentee['欲申請學位'].values[0]}"
)
st.subheader("學歷資料")
for degree in ["學士", "碩士"]:
if pd.isna(current_mentee[f"{degree}就讀/畢業學校"].values[0]):
continue
st.write(
f"{current_mentee[f'{degree}就讀/畢業學校'].values[0]} {current_mentee[f'{degree}就讀/畢業系所'].values[0]} {degree}"
)
st.subheader("申請目標")
goal_columns = [
"主要申請系所所在國家",
"欲申請學校",
"欲申請系所/program",
"相關的選擇學校、系所的理由",
]
for column in goal_columns:
if pd.isna(current_mentee[column]).any():
continue
st.caption(column)
st.write(current_mentee[column].values[0])
st.subheader("任何想對未來導師說的話")
current_mentee["任何想對未來導師說的話"].values[0]
with col2:
st.subheader("基本資料")
st.caption(f"廣義研究領域")
current_mentee["廣義研究領域"].values[0]
st.caption(f"專業領域")
current_mentee["專業領域"].values[0]
st.subheader("目前申請準備進度")
progress_columns = [
"欲尋求之協助/建議(至多三個)",
"請問您目前準備進度為何？是否已經完成選校？",
"提供簡歷與相關資料",
"是否已經參加留學國的語文程度測驗？",
"目前申請文件的準備進度？",
"目前是否已開始聯繫請求推薦信。",
"是否已經參加標準化入學考試？",
]
for column in progress_columns:
if pd.isna(current_mentee[column]).any():
continue
st.caption(column)
st.write(current_mentee[column].values[0])
st.subheader("選填背景資訊")
background_columns = [
"是否為家族中第一代高等教育子女（選填）",
"國際活動經驗（選填）",
"學術領域外相關特殊專長、經驗或成就（選填）",
"家人、伴侶等狀態（選填）",
"目前的生涯規劃，或主要申請目的與動機（選填）",
"您認為可能會影響申請過程或結果的身份、背景、經歷，而您希望導師知道的（選填）",
"任何想補充給導師知道的（選填）",
"任何公開資訊 （選填）",
]
for column in background_columns:
if pd.isna(current_mentee[column]).any():
continue
st.caption(column)
st.write(current_mentee[column].values[0])
st.subheader("聯絡方式")
contact_columns = [
"電子郵件地址",
"其餘聯絡方式 (非必填)",
]
for column in contact_columns:
if pd.isna(current_mentee[column]).any():
continue
st.caption(column)
st.write(current_mentee[column].values[0])
elif viewing_mode == "Multiple Mentee Info":
st.subheader("Multiple Mentee Info")
options = st.multiselect(
"You can select multiple mentees to compare their profiles.",
mentee_response["中文姓名"].values,
)
st.dataframe(
mentee_response.loc[mentee_response["中文姓名"].isin(options), display_columns]
.set_index("中文姓名")
.transpose(),
height=696,
use_container_width=True,
)