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main.py
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main.py
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import stqdm
import pickle
import pandas as pd
from PIL import Image
from time import sleep
import streamlit as st
from stqdm import stqdm
from streamlit_option_menu import option_menu
@st.cache_resource
def load_model():
with open('assets/model.pkl', 'rb') as f:
return pickle.load(f)
st.set_page_config(page_title="Omdena Rwanda", page_icon="🇷🇼", initial_sidebar_state="expanded")
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
css_style = {
"icon": {"color": "white"},
"nav-link": {"--hover-color": "grey"},
"nav-link-selected": {"background-color": "#FF4C1B"},
}
# Loading assets
img_banner = Image.open("assets/images/banner.png")
img_banner2 = Image.open("assets/images/banner2.png")
img_rwanda = Image.open("assets/images/rwanda-logo.png")
def home_page():
st.write(f"""# Water Inspection System""", unsafe_allow_html=True)
st.image(img_banner)
st.write(f"""<h2>The Problem</h2>
<p>Access to clean water is a critical challenge in many parts of the world,
including Rwanda. Water quality prediction is important for ensuring the availability of safe and clean water for
drinking, agriculture, and other purposes. However, traditional methods for water quality prediction are often
time-consuming and costly, and they may not provide accurate and timely information. To address this challenge,
the Omdena Rwanda Chapter has initiated a project to develop an automated water quality prediction system using
machine learning.</p> """, unsafe_allow_html=True)
st.write(f"""<h2>Project goals</h2> <p>In this project, the Omdena Rwanda Chapter’s primary goal in this project
is to develop an accurate and efficient machine learning model that can predict water quality based on a range of
parameters such as Electrical conductivity of water, Amount of organic carbon in ppm, Amount of Trihalomethanes
in μg/L, and turbidity. The model will be trained on a large dataset of historical water quality data and will be
designed to provide predictions for water quality..</p> """, unsafe_allow_html=True)
def about_page():
st.write("""<h1>Project background</h1>""", unsafe_allow_html=True)
st.image(img_banner2)
st.write("""
<p>Rwanda is a landlocked country located in East Africa,
with a population of approximately 13 million people. Despite efforts to improve access to clean water,
access remains a critical challenge, particularly in rural areas. According to UNICEF, only 47% of the population
has access to basic water services, and only 32% have access to safely managed drinking water services. One of
the challenges in ensuring access to clean water is predicting and monitoring water quality. Traditional water
quality prediction and monitoring methods are often time-consuming, costly, and may not provide timely and
accurate information. This can lead to delays in identifying and addressing water quality issues, putting public
health and agricultural productivity at risk. <br> <br> Machine learning has the potential to revolutionize water
quality prediction and monitoring by providing a faster, more accurate, and cost-effective method for predicting
water quality. By analyzing large datasets of water quality parameters, machine learning models can identify
patterns and relationships between different parameters, enabling accurate predictions of water quality.</p><br>
""", unsafe_allow_html=True)
def model_section():
st.write("""<h1>Predict Water Quality</h1>
<p>Enter these values of the parameters to know if the water quality is suitable to drink or not.</p><hr>
""", unsafe_allow_html=True)
col1, col2, col3 = st.columns(3, gap="large")
with col1:
ColourTCU = st.number_input(label="Colour (TCU)", min_value=0.0, max_value=1000.0, step=50.0, format="%f",
key="test_slider0")
TurbidityNTU = st.number_input(label="Turbidity (NTU)", min_value=0.0, max_value=1000.0, step=50.0, format="%f",
key="test_slider1")
pH = st.number_input(label="pH", min_value=0.0, max_value=1000.0, step=50.0, format="%f", key="test_slider2")
ConductivityuS = st.number_input(label="Conductivity (uS/cm)", min_value=0.0, max_value=1000.0, step=50.0,
format="%f", key="test_slider3")
TotalDissolvedSolids = st.number_input(label="Total Dissolved Solids (mg/l)", min_value=0.0, max_value=1000.0,
step=50.0, format="%f", key="test_slider4")
TotalHardness = st.number_input(label="Total Hardness (mg/l as CaCO3)", min_value=0.0, max_value=1000.0,
step=50.0, format="%f", key="test_slider5")
with col2:
Aluminium = st.number_input(label="Aluminium (mg/l)", min_value=0.0, max_value=1000.5, step=50.1, format="%f",
key="test_slider6")
Chloride = st.number_input(label="Chloride (mg/l)", min_value=0.0, max_value=1000.0, step=50.0, format="%f",
key="test_slider7")
Iron = st.number_input(label="Iron (mg/l)", min_value=0.0, max_value=1000.5, step=50.1, format="%f",
key="test_slider8")
Sodium = st.number_input(label="Sodium (mg/l)", min_value=0.0, max_value=1000.0, step=50.0, format="%f",
key="test_slider9")
Sulphate = st.number_input(label="Sulphate (mg/l)", min_value=0.0, max_value=1000.0, step=50.0, format="%f",
key="test_slider10")
Zinc = st.number_input(label="Zinc (mg/l)", min_value=0.0, max_value=1000.0, step=50.0, format="%f",
key="test_slider11")
with col3:
Magnesium = st.number_input(label="Magnesium (mg/l)", min_value=0.0, max_value=1000.0, step=50.0, format="%f",
key="test_slider12")
Calcium = st.number_input(label="Calcium (mg/l)", min_value=0.0, max_value=1000.0, step=50.0, format="%f",
key="test_slider13")
Potassium = st.number_input(label="Potassium (mg/l)", min_value=0.0, max_value=1000.0, step=50.0, format="%f",
key="test_slider14")
Nitrate = st.number_input(label="Nitrate (mg/l)", min_value=0.0, max_value=1000.0, step=50.0, format="%f",
key="test_slider15")
Phosphate = st.number_input(label="Phosphate (mg/l)", min_value=0.0, max_value=1000.2, step=50.1, format="%f",
key="test_slider16")
st.write("<br>", unsafe_allow_html=True)
predict_button = st.button(' Predict Water Quality ')
dataframe = pd.DataFrame({'Colour (TCU)': [ColourTCU], 'Turbidity (NTU)': [TurbidityNTU], 'pH': [pH],
'Conductivity (uS/cm)': [ConductivityuS],
'Total Dissolved Solids (mg/l)': [TotalDissolvedSolids],
'Total Hardness (mg/l as CaCO3)': [TotalHardness], 'Aluminium (mg/l)': [Aluminium],
'Chloride (mg/l)': [Chloride], 'Total Iron (mg/l)': [Iron],
'Sodium (mg/l)': [Sodium], 'Sulphate (mg/l)': [Sulphate], 'Zinc (mg/l)': [Zinc],
'Magnesium (mg/l)': [Magnesium], 'Calcium (mg/l)': [Calcium],
'Potassium (mg/l)': [Potassium], 'Nitrate (mg/l)': [Nitrate],
'Phosphate (mg/l)': [Phosphate]})
if predict_button:
model = load_model()
result = model.predict(dataframe)
for _ in stqdm(range(50)):
sleep(0.015)
if result[0] == 1.0:
st.error("This Water Quality is Non-Potable")
else:
st.success('This Water Quality is Potable')
def contributors_page():
st.write("""
<h1 style="text-align: center; color:#FFF6F4;">A heartfelt thankyou to all our contributors ❤️</h1><hr>
<div style="text-align:center;">
<table>
<tr>
<th width="20%" style="font-size: 140%;">Chapter Name</th>
<th width="20%" style="font-size: 140%;">Chapter Lead</th>
</tr>
<tr>
<td>Kigali, Rwanda Chapter</td>
<td>Samiratu Ntohsi</td>
</tr>
</table>
<br>
<table>
<tbody>
<tr>
<th width="20%" style="font-size: 140%;">Task Name</th>
<th width="20%" style="font-size: 140%;">Task Lead</th>
</tr>
<tr>
<td>Knowledge</td>
<td>Pritam Bhakta</td>
</tr>
<tr>
<td>Data Collection</td>
<td>Swamesh Lotlikar</td>
</tr>
<tr>
<td>Data Preprocessing</td>
<td>Qutaiba Ahmed Ansari</td>
</tr>
<tr>
<td>Data Analysis</td>
<td>Qutaiba Ahmed Ansari</td>
</tr>
<tr>
<td>Modeling</td>
<td>Bibhuti Baibhav Borah</td>
</tr>
<tr>
<td>Deployment</td>
<td>Vinod Cherian</td>
</tr>
<tr>
<td>Documentation & Articles</td>
<td>Fen</td>
</tr>
</tbody>
</table>
<br>
<table>
<tbody>
<tr>
<th width="20%" style="font-size: 140%;" colspan="2">Contributors</th>
</tr>
<tr>
<td width="20%"> Akunna Anyamkpa</td>
<td width="20%"> Alka Gupta</td>
</tr>
<tr>
<td>Alpha Lossangoyi Nanga</td>
<td>Arpit Sengar </td>
</tr>
<tr>
<td>Ekta</td>
<td>Indrajith C</td>
</tr>
<tr>
<td>Istiaque Ahamed</td>
<td>Marwan Ashraf</td>
</tr>
<tr>
<td>Lael</td>
<td>Leonardo Goliatt</td>
</tr>
<tr>
<td>Nermin Elgrawany</td>
<td>Okwor Danielchinedu</td>
</tr>
<tr>
<td>Ranjana Sundar</td>
<td>Saurabh Bhardwaj</td>
</tr>
<tr>
<td>Shnehi Karki</td>
<td>Ye Bhone Lin</td>
</tr>
</tbody>
</table>
</div>
<hr>
""", unsafe_allow_html=True)
with st.sidebar:
st.image(img_rwanda)
selected = option_menu(
menu_title=None,
options=["Home", "Check Water Quality", "About", "Contributors"],
icons=["house", "droplet", "info-circle", "people"],
styles=css_style
)
if selected == "Home":
home_page()
elif selected == "Check Water Quality":
model_section()
elif selected == "About":
about_page()
elif selected == "Contributors":
contributors_page()