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import streamlit as st | |
import pandas as pd | |
import numpy as np | |
import datetime | |
import hopsworks | |
from functions import figure, retrieve | |
import os | |
import pickle | |
import plotly.express as px | |
import json | |
from datetime import datetime | |
import os | |
# Real data | |
today = datetime.today().strftime('%Y-%m-%d') | |
df = retrieve.get_merged_dataframe() | |
n = len(df[df['pm25'].isna()]) - 1 | |
# Dummmy data | |
# size = 400 | |
# data = { | |
# 'date': pd.date_range(start='2023-01-01', periods=size, freq='D'), | |
# 'pm25': np.random.randint(50, 150, size=size), | |
# 'predicted_pm25': np.random.randint(50, 150, size=size) | |
# } | |
# df = pd.DataFrame(data) | |
# Page configuration | |
st.set_page_config( | |
page_title="Air Quality Prediction", | |
page_icon="🧊", | |
layout="wide", | |
initial_sidebar_state="expanded", | |
menu_items={ | |
'About': "# Air Quality Prediction" | |
} | |
) | |
st.title('Lahore Air Quality') | |
st.subheader('Forecast and hindcast') | |
st.subheader('Unit: PM25 - particle matter of diameter < 2.5 micrometers') | |
# Plotting | |
fig = figure.plot(df, n=n) | |
st.plotly_chart(fig, use_container_width=True) |