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import streamlit as st | |
import pandas as pd | |
import numpy as np | |
import datetime | |
import hopsworks | |
from functions import figure, util | |
import os | |
import pickle | |
import plotly.express as px | |
import json | |
# Set up | |
api_key = os.getenv('HOPSWORKS_API_KEY') | |
project_name = os.getenv('HOPSWORKS_PROJECT') | |
project = hopsworks.login(project=project_name, api_key_value=api_key) | |
fs = project.get_feature_store() | |
secrets = util.secrets_api(project.name) | |
feature_view = fs.get_feature_view( | |
name='air_quality_fv', | |
version=1, | |
) | |
df = feature_view.get_batch_data(start_time=None, end_time=None, read_options=None).sort_values(by='date') | |
today = datetime.datetime.now() - datetime.timedelta(0) | |
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') | |
#pickle_file_path = 'air_quality_df.pkl' | |
pickle_file_path = 'outcome_df.pkl' | |
with open(pickle_file_path, 'rb') as file: | |
st.session_state.df = pickle.load(file).sort_values(by="date") | |
fig = figure.plot(st.session_state.df) | |
# Render the chart in Streamlit | |
st.plotly_chart(fig) |