app.py

import streamlit as st
import pandas as pd
import plotly.express as px 
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np


### CONFIG
st.set_page_config(
    page_title="Air-Quality",
    page_icon="🌡️",
    layout="wide"
  )

### TITLE AND TEXT
st.title("Air Quality.")
@st.cache # this lets the 
def def_load():
    df = pd.read_csv('AirQuality.xls', sep=';')
    df.dropna(axis=0, how='all', inplace=True)
    df = df.iloc[:,0:-2] # Two col unnamed are dropped
    # Concaténer les colonnes "Date" et "Time" en une seule colonne "Datetime"
    df["Datetime"] = pd.to_datetime(df["Date"] + " " + df["Time"], format="%d/%m/%Y %H.%M.%S")

    # Supprimer les anciennes colonnes si besoin
    df.drop(columns=["Date", "Time"], inplace=True)    

    for col in df.columns[:-2]:
        if df[col].dtype == 'object':
            df[col] = df[col].map(lambda x: x.replace(',', '.')).astype(float)
    return df


data_load_state = st.text('Loading data...')
data = def_load()

data_load_state.text("") # change text from "Loading data..." to "" once the the load_data function has run

## Run the below code if the check is checked ✅
if st.checkbox('Show raw data'):
    st.subheader('Raw data')
    st.write(data) 



# col1, col2 = st.columns(2)

# with col1:
#     a = 5
#     st.write(a)
# with col2:
#     with st.form("average_sales_per_country"):
#         submit = st.form_submit_button("submit")
#         if submit:
#             a += 1
#     # a = 10
#     st.write(a)

#### CREATE TWO COLUMNS
col1, col2 = st.columns(2)

# Initialize plot_data with all data
plot_data = data.copy()

# Define the form first, so we can use the results in both columns
with col2:
    st.markdown("**2️⃣ Example of input form**")
    with st.form("average_sales_per_country"):
        start_period = st.date_input("Select a start date you want to see your metric")
        end_period = st.date_input("Select an end date you want to see your metric")
        submit = st.form_submit_button("submit")
    
    # Create the mask when form is submitted
    if submit:
        start_period, end_period = pd.to_datetime(start_period), pd.to_datetime(end_period)
        mask = (data["Datetime"] > start_period) & (data["Datetime"] < end_period)
        plot_data = data[mask]
        st.write(f"Points in selected range: {mask.sum()}")

# Now use plot_data (which may be filtered) for plotting
with col1:
    st.markdown("** Example of input widget**")
    df = plot_data.copy()  # Use plot_data instead of data
    
    if df['T'].dtype == 'object':
        df['T'] = df['T'].map(lambda x: x.replace(',', '.')).astype(float)
    T_mask = df['T'] > 0
    
    if df['RH'].dtype == 'object':
        df['RH'] = df['RH'].map(lambda x: x.replace(',', '.')).astype(float)
    RH_mask = df['RH'] > 0
    
    # Create figure with secondary y-axis
    fig = make_subplots(specs=[[{"secondary_y": True}]])
    
    # Add traces
    fig.add_trace(
        go.Line(x=df['Datetime'], y=df['T'][T_mask], name="T(°C)"),
        secondary_y=False,
    )
    fig.add_trace(
        go.Line(x=df['Datetime'], y=df['RH'][RH_mask], name="H(%)."),
        secondary_y=True,
    )
    
    # Add figure title
    fig.update_layout(
        title_text="Temperature and Humidity "
    )
    
    # Set x-axis title
    fig.update_xaxes(title_text="Time --->")
    
    # Set y-axes titles
    fig.update_yaxes(title_text="<b>Temperature</b> (°C)", secondary_y=False)
    fig.update_yaxes(title_text="<b>Humidity</b>(%)", secondary_y=True)
    
    st.plotly_chart(fig, use_container_width=True)


    pol = st.selectbox("Select a c", data.drop(["Datetime", "T", "RH", 'AH'], axis=1).columns)


# st.markdown("""
#     Welcome to this awesome `streamlit` dashboard. This library is great to build very fast and
#     intuitive charts and application running on the web. Here is a showcase of what you can do with
#     it. Our data comes from an e-commerce website that simply displays samples of customer sales. Let's check it out.
#     Also, if you want to have a real quick overview of what streamlit is all about, feel free to watch the below video 👇
# """)


# @st.cache # this lets the 
# def load_data(nrows):
#     data = pd.read_csv(DATA_URL, nrows=nrows)
#     data["Date"] = data["Date"].apply(lambda x: pd.to_datetime(",".join(x.split(",")[-2:])))
#     data["currency"] = data["currency"].apply(lambda x: pd.to_numeric(x[1:]))
#     return data

# data_load_state = st.text('Loading data...')
# data = load_data(1000)
# data_load_state.text("") # change text from "Loading data..." to "" once the the load_data function has run

# ## Run the below code if the check is checked ✅
# if st.checkbox('Show raw data'):
#     st.subheader('Raw data')
#     st.write(data) 


# ### SIDEBAR
# st.sidebar.header("Build dashboards with Streamlit")
# st.sidebar.markdown("""
#     * [Load and showcase data](#load-and-showcase-data)
#     * [Charts directly built with Streamlit](#simple-bar-chart-built-directly-with-streamlit)
#     * [Charts built with Plotly](#simple-bar-chart-built-with-plotly)
#     * [Input Data](#input-data)
# """)
# e = st.sidebar.empty()
# e.write("")
# st.sidebar.write("Made with 💖 by [Jedha](https://jedha.co)")

# ### EXPANDER

# with st.expander("⏯️ Watch this 15min tutorial"):
#     st.video("https://youtu.be/B2iAodr0fOo")

# st.markdown("---")

# #### CREATE TWO COLUMNS
# col1, col2 = st.columns(2)

# with col1:
#     st.markdown("First column")
#     country = st.selectbox("Select a country you want to see all time sales", data["country"].sort_values().unique())

# with col2:
#     st.markdown("Second column")
#     with st.form("average_sales_per_country"):
#       country = st.selectbox("Select a country you want to see sales", data["country"].sort_values().unique())
#       start_period = st.date_input("Select a start date you want to see your metric")
#       end_period = st.date_input("Select an end date you want to see your metric")
#       submit = st.form_submit_button("submit")