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import streamlit as st
from datetime import datetime
from huggingface_hub import HfApi
from src.text_content import REVIEW_TEXT, INTRO_TEXT_AR, INTRO_TEXT_EN, INTRO_TEXT_FR
from src.dataframes import INTERVENTIONS_PROCESSED_URL, VERIFIED_REQUESTS_PROCESSED_URL
from src.utils import parse_gg_sheet
import plotly.express as px
def id_review_submission(api: HfApi):
"""Id review submission form"""
# collapse the text
with st.expander("🔍 Review of requests | مراجعة طلب مساعدة"):
st.markdown(REVIEW_TEXT)
id_to_review = st.number_input("Enter id / أدخل الرقم", min_value=0, value=0, step=1)
reason_for_review = st.text_area("Explain why / أدخل سبب المراجعة")
if st.button("Submit / أرسل"):
if reason_for_review == "":
st.error("Please enter a reason / الرجاء إدخال سبب")
else:
filename = f"review_id_{id_to_review}_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.txt"
with open(filename, "w") as f:
f.write(f"id: {id_to_review}, explanation: {reason_for_review}\n")
api.upload_file(
path_or_fileobj=filename,
path_in_repo=filename,
repo_id="nt3awnou/review_requests",
repo_type="dataset",
)
st.success("Submitted at https://huggingface.co/datasets/nt3awnou/review_requests/ تم الإرسال")
def show_embed_code():
with st.expander(_("💻 For Developers only, embed code for the map")):
st.code(
"""
<iframe id="nt3awnou-map"
src="https://nt3awnou-embed-rescue-map.hf.space/?embed=true" width="1200" height="720"
frameborder="0"
width="850"
height="450"
title="Nt3awno Rescue Map">
</iframe>
<script src="https://cdn.jsdelivr.net/npm/iframe-resizer@4.3.4/js/iframeResizer.min.js"></script>
<script>
iFrameResize({}, "#nt3awnou-map");
</script>
""",
language="html",
)
def show_dataframes_metrics(len_requests, len_interventions, len_solved_verified_requests, lang, show_col_3=False):
if lang == "en":
# with st.expander("📝 Nt3awnou Platform Description"):
st.markdown(INTRO_TEXT_EN, unsafe_allow_html=True)
if show_col_3:
col1, col2, col3 = st.columns([1, 1, 1])
else:
col1, col2 = st.columns([1, 1])
with col1:
st.metric(
"# Number of help requests",
len_requests,
)
with col2:
st.metric(
"# Number of interventions",
len_interventions + len_solved_verified_requests,
)
if show_col_3:
with col3:
st.metric(
"# Number of solved requests",
len_solved_verified_requests,
)
elif lang == "ar":
# with st.expander("📝 شرح منصة نتعاونو"):
st.markdown(INTRO_TEXT_AR, unsafe_allow_html=True)
if show_col_3:
col1, col2, col3 = st.columns([1, 1, 1])
else:
col1, col2 = st.columns([1, 1])
with col1:
st.metric(
"# عدد طلبات المساعدة",
len_requests,
)
with col2:
st.metric(
"# عدد التدخلات",
len_interventions + len_solved_verified_requests,
)
if show_col_3:
with col3:
st.metric(
"# عدد الطلبات المستجاب لها",
len_solved_verified_requests,
)
elif lang == "fr":
# with st.expander("📝 Description de la plateforme Nt3awnou"):
st.markdown(INTRO_TEXT_FR, unsafe_allow_html=True)
if show_col_3:
col1, col2, col3 = st.columns([1, 1, 1])
else:
col1, col2 = st.columns([1, 1])
with col1:
st.metric(
"# Nombre de demandes d'aide",
len_requests,
)
with col2:
st.metric(
"# Nombre d'interventions",
len_interventions + len_solved_verified_requests,
)
if show_col_3:
with col3:
st.metric(
"# Nombre de demandes résolues",
len_solved_verified_requests,
)
@st.cache_data(ttl=60*60*24)
def cached_parse_gg_sheet(url):
return parse_gg_sheet(url)
def show_charts():
st.subheader(_("📊 **Charts**"))
col1, col2 = st.columns([1, 1])
# interventions_categories
interventions_processed_df = cached_parse_gg_sheet(INTERVENTIONS_PROCESSED_URL)
supply_data = interventions_processed_df["supplies_category"].str.split(",").explode().str.strip('[] \'').dropna().astype("category")
interv_fig = px.pie(supply_data, names='supplies_category', title=_('Supplies Categories'))
with col1:
st.plotly_chart(interv_fig)
# requests_categories
requests_processed_df = cached_parse_gg_sheet(VERIFIED_REQUESTS_PROCESSED_URL)
need_data = requests_processed_df["need_category"].str.split(",").explode().str.strip('[] \'').dropna().astype("category")
req_fig = px.pie(need_data, names='need_category', title=_('Needs Categories'))
with col2:
st.plotly_chart(req_fig)
def show_donations(lang):
st.subheader(_("📝 **Donations**"))
if lang == "en":
st.markdown(
"""
<div style="text-align: center;">
<h4>The official bank account dedicated to tackle the consequences of the earthquake is:</h4>
<b>Account number:</b>
<h2>126</h2>
<b>RIB:</b> 001-810-0078000201106203-18
<br>
<b>For the money transfers coming from outside Morocco</b>
<br>
<b>IBAN:</b> MA64001810007800020110620318
<br>
""",
unsafe_allow_html=True,
)
elif lang == "ar":
st.markdown(
"""
<div style="text-align: center;">
<h4>الحساب البنكي الرسمي المخصص لمواجهة عواقب الزلزال</h4>
<b>رقم الحساب</b>
<h2>126</h2>
<b>RIB:</b> 001-810-0078000201106203-18
<br>
<b>للتحويلات القادمة من خارج المغرب</b>
<br>
<b>IBAN:</b> MA64001810007800020110620318
<br>
</div>
""",
unsafe_allow_html=True,
)
elif lang == "fr":
st.markdown(
"""
<div style="text-align: center;">
<h4>Le compte bancaire officiel dédié à la lutte contre les conséquences du séisme est le suivant:</h4>
<b>Numéro de compte:</b>
<h2>126</h2>
<b>RIB:</b> 001-810-0078000201106203-18
<br>
<b>Pour les transferts d'argent en provenance de l'étranger</b>
<br>
<b>IBAN:</b> MA64001810007800020110620318
<br>
""",
unsafe_allow_html=True,
) |