How to use the streamlit.text function in streamlit

with st.echo():
    for percent in [0, 25, 50, 75, 100]:
        st.write("%s%% progress:" % percent)
        st.progress(percent)

st.header("Help")

with st.echo():
    st.help(dir)

st.header("Out-of-Order Writing")

st.write("Placeholders allow you to draw items out-of-order. For example:")

with st.echo():
    st.text("A")
    placeholder = st.empty()
    st.text("C")
    placeholder.text("B")

st.header("Exceptions")
st.write("You can print out exceptions using `st.exception()`:")

with st.echo():
    try:
        raise RuntimeError("An exception")
    except Exception as e:
        st.exception(e)

st.header("Playing audio")

audio_url = (

#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import streamlit as st

st.text("This text is awesome!")

if st.checkbox("Show Image/Hide Image"):
        my_image = load_image("iris_setosa.jpg")
        enh = ImageEnhance.Contrast(my_image)
        num = st.slider("Set Your Contrast Number", 1.0, 3.0)
        img_width = st.slider("Set Image Width", 300, 500)
        st.image(enh.enhance(num), width=img_width)

    # About
    if st.button("About App"):
        st.subheader("Iris Dataset EDA App")
        st.text("Built with Streamlit")
        st.text("Thanks to the Streamlit Team Amazing Work")

    if st.checkbox("By"):
        st.text("Jesse E.Agbe(JCharis)")
        st.text("Jesus Saves@JCharisTech")

best_model_aic = np.round(model.aic,0)
                                best_model_bic = np.round(model.bic,0)
                                best_model_hqic = np.round(model.hqic,0)
                                best_model_order = (p_, d, q_, P_, D, Q_, s)
                                current_best_model = model
                                resid = np.round(np.expm1(current_best_model.resid).mean(), 3)
                                models.append(model)
                                #st.markdown("------------------")
                                #st.markdown("**Best model so far**: SARIMA {}".format(best_model_order)) 
                                #st.markdown("**AIC**: {} **BIC**: {} **HQIC**: {} **Resid**: {}".format(best_model_aic, best_model_bic, best_model_hqic, resid))
                        except:
                            pass
    st.success('Grid Search done!')
    st.markdown('')
    st.markdown('### Best model results')
    st.text(current_best_model.summary())                
    #return current_best_model, models, best_model_order
    return best_model_order

my_image = load_image('iris_setosa.jpg')
    	enh = ImageEnhance.Contrast(my_image)
    	num = st.slider("Set Your Contrast Number",1.0,3.0)
    	img_width = st.slider("Set Image Width",300,500)
    	st.image(enh.enhance(num),width=img_width)


    # About
    if st.button("About App"):
    	st.subheader("Iris Dataset EDA App")
    	st.text("Built with Streamlit")
    	st.text("Thanks to the Streamlit Team Amazing Work")

    if st.checkbox("By"):
    	st.text("Jesse E.Agbe(JCharis)")
    	st.text("Jesus Saves@JCharisTech")

else:
    		st.write(data.head(2))

    # Show Entire Dataframe
    if st.checkbox("Show All DataFrame"):
    	st.dataframe(data)

    # Show All Column Names
    if st.checkbox("Show All Column Name"):
    	st.text("Columns:")
    	st.write(data.columns)

    # Show Dimensions and Shape of Dataset
    data_dim = st.radio('What Dimension Do You Want to Show',('Rows','Columns'))
    if data_dim == 'Rows':
    	st.text("Showing Length of Rows")
    	st.write(len(data))
    if data_dim == 'Columns':
    	st.text("Showing Length of Columns")
    	st.write(data.shape[1])

    # Show Summary of Dataset
    if st.checkbox("Show Summary of Dataset"):
    	st.write(data.describe())

    # Selection of Columns
    species_option = st.selectbox('Select Columns',('sepal_length','sepal_width','petal_length','petal_width','species'))
    if species_option == 'sepal_length':
    	st.write(data['sepal_length'])
    elif species_option == 'sepal_width':
    	st.write(data['sepal_width'])
    elif species_option == 'petal_length':

#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import streamlit as st
from time import sleep

with st.spinner():
    for i in range(5, 0, -1):
        st.text(i)
        sleep(1)

st.text('done')

#     return im

    # Select Image Type using Radio Button
    species_type = st.radio(
        "What is the Iris Species do you want to see?",
        ("Setosa", "Versicolor", "Virginica"),
    )

    if species_type == "Setosa":
        st.text("Showing Setosa Species")
        st.image(load_image("imgs/iris_setosa.jpg"), width=400)
    elif species_type == "Versicolor":
        st.text("Showing Versicolor Species")
        st.image(load_image("imgs/iris_versicolor.jpg"), width=400)
    elif species_type == "Virginica":
        st.text("Showing Virginica Species")
        st.image(load_image("imgs/iris_virginica.jpg"), width=400)

    # Show Image or Hide Image with Checkbox
    if st.checkbox("Show Image/Hide Image"):
        my_image = load_image("iris_setosa.jpg")
        enh = ImageEnhance.Contrast(my_image)
        num = st.slider("Set Your Contrast Number", 1.0, 3.0)
        img_width = st.slider("Set Image Width", 300, 500)
        st.image(enh.enhance(num), width=img_width)

    # About
    if st.button("About App"):
        st.subheader("Iris Dataset EDA App")
        st.text("Built with Streamlit")
        st.text("Thanks to the Streamlit Team Amazing Work")

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import streamlit as st

st.text("The space between this...")
st.text("..and this should be the same as between this...")
st.empty()
st.text("...and this")

# Show Image or Hide Image with Checkbox
    if st.checkbox("Show Image/Hide Image"):
    	my_image = load_image('iris_setosa.jpg')
    	enh = ImageEnhance.Contrast(my_image)
    	num = st.slider("Set Your Contrast Number",1.0,3.0)
    	img_width = st.slider("Set Image Width",300,500)
    	st.image(enh.enhance(num),width=img_width)


    # About
    if st.button("About App"):
    	st.subheader("Iris Dataset EDA App")
    	st.text("Built with Streamlit")
    	st.text("Thanks to the Streamlit Team Amazing Work")

    if st.checkbox("By"):
    	st.text("Jesse E.Agbe(JCharis)")
    	st.text("Jesus Saves@JCharisTech")