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| import streamlit as st | |
| st.set_option('deprecation.showPyplotGlobalUse', False) | |
| #st.markdown() | |
| st.title("Document Title") | |
| st.header("Article header") | |
| st.subheader("Article subheader") | |
| st.code("y = mx + c") | |
| st.latex("\ int a y^2 \ , dy") | |
| st.text("This is a chair!") | |
| st.markdown('Staying hydrated is **_very_ cool**.') | |
| students = ["Amelia Kami", "Antoinne Mark", "Peter Zen", "North Kim"] | |
| marks = [82, 76, 96, 68] | |
| import pandas as pd | |
| df = pd.DataFrame() | |
| df["Student Name"] = students | |
| df["Marks"] = marks | |
| #save to dataframe | |
| df.to_csv("students.csv", index = False) | |
| #display | |
| st.dataframe(df) | |
| #Static table | |
| st.table(df) | |
| #Metrics | |
| st.metric("KPI", 56, 3) | |
| #Json | |
| st.json(df.to_dict()) | |
| #Code | |
| #average of a list | |
| code = '''def cal_average(numbers): | |
| sum_number = 0 | |
| for t in numbers: | |
| sum_number = sum_number + t | |
| average = sum_number / len(numbers) | |
| return average''' | |
| st.code(code, language='python') | |
| #progress bar | |
| import streamlit as st | |
| import time | |
| # Sample Progress bar | |
| #bar_p = st.progress(0) | |
| #for percentage_complete in range(100): | |
| #time.sleep(0.1) | |
| #bar_p.progress(percentage_complete + 1) | |
| #with st.spinner('Please wait...'): | |
| #time.sleep(5) | |
| #st.write('Complete!') | |
| #Displaying an image using Streamlit | |
| from PIL import Image | |
| image = Image.open('media/ann-savchenko-H0h_89iFsWs-unsplash.jpg') | |
| #st.image(image, caption='Sunset grass backgrounds') | |
| #plotly | |
| import plotly.express as px | |
| # This dataframe has 244 rows, but 4 unique entries for the `day` variable | |
| df = px.data.tips() | |
| figx = px.pie(df, values='tip', names='day', title='Tips per day') | |
| # Plot! | |
| st.plotly_chart(figx, use_container_width=True) | |
| #Altair | |
| import altair as alt | |
| import streamlit as st | |
| import numpy as np | |
| df = pd.DataFrame( | |
| np.random.randn(300, 4), | |
| columns=['a', 'b', 'c', 'd']) | |
| chrt = alt.Chart(df).mark_circle().encode( | |
| x='a', y='b', size='c', color='c', tooltip=['a', 'b', 'c', 'd']) | |
| st.altair_chart(chrt, use_container_width=True) | |
| #Matplotlib | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| arr = np.random.normal(1, 1, size=1000) | |
| fig, ax = plt.subplots() | |
| ax.hist(arr, bins=30) | |
| plt.grid() | |
| st.pyplot(fig) | |
| #Interactive widgets | |
| st.button("Click here") | |
| #st.download_button("Download audio", file) | |
| selected = st.checkbox("Accept terms") | |
| choice = st.radio("Select one", ["Apples", "Oranges"]) | |
| option = st.selectbox( | |
| 'How would you like to receive your package?', | |
| ('By air', 'By sea', 'By rail')) | |
| st.write('You selected:', option) | |
| import datetime | |
| day = st.date_input( | |
| "When is your birthday?", | |
| datetime.date(2022, 7, 6)) | |
| st.write('Your birthday is:', day) | |
| color = st.color_picker('Choose A Color', '#00FFAA') | |
| st.write('The selected color is', color) | |
| def fetch_data(): | |
| df = pd.read_csv("students.csv") | |
| return df | |
| #data = fetch_data() | |
| #Visualization | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| #Matplotlib | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| fig = plt.figure() | |
| ax = fig.add_axes([0,0,1,1]) | |
| animals = ["Zebras", "Elephants", "Rhinos", "Leopards"] | |
| number = [65, 72, 77, 59] | |
| ax.bar(animals, number) | |
| fig = plt.show() | |
| st.pyplot(fig) | |
| #Seaborn | |
| import seaborn as sns | |
| fig = plt.figure() | |
| ax = sns.barplot(x = animals, y = number) | |
| fig = plt.show() | |
| st.pyplot(fig) | |
| #Altair | |
| #define data | |
| df = pd.DataFrame() | |
| df["Animals"] = animals | |
| df["Number"] = number | |
| #create chart | |
| chrt = alt.Chart(df, title="Ploting using Altair in Streamlit").mark_bar().encode( | |
| x='Animals', | |
| y='Number' | |
| ) | |
| #render with Streamlit | |
| st.altair_chart(chrt, use_container_width=True) | |
| #Plotly | |
| #define data | |
| df = pd.DataFrame() | |
| df["Animals"] = animals | |
| df["Number"] = number | |
| #create plot | |
| fig1 = px.bar(df, x='Animals', y='Number', title="Ploting using Plotly in Streamlit") | |
| # Plot! | |
| st.plotly_chart(fig1, use_container_width=True) | |
| #data | |
| df = pd.DataFrame() | |
| df["Animals"] = animals | |
| df["Number"] = number | |
| #visualization | |
| st.vega_lite_chart(df, { | |
| 'mark': {'type': 'bar', 'tooltip': True}, | |
| 'encoding': { | |
| 'x': {'field': 'Animals', 'type': 'nominal'}, | |
| 'y': {'field': 'Number', 'type': 'quantitative'}, | |
| }, | |
| }, use_container_width=True) | |
| #Maps | |
| import pandas as pd | |
| states = pd.read_html('https://developers.google.com/public-data/docs/canonical/states_csv')[0] | |
| states.columns = ['state', 'lat', 'lon', 'name'] | |
| states = states.drop(['state', 'name'], axis = 1) | |
| st.map(states) | |
| #Components | |
| from st_aggrid import AgGrid | |
| AgGrid(df) | |
| #Statefulnness | |
| import streamlit as st | |
| st.title('Streamlit Counter Example') | |
| count = 0 | |
| add = st.button('Addition') | |
| if add: | |
| count += 1 | |
| st.write('Count = ', count) | |
| import streamlit as st | |
| st.title('Counter Session State') | |
| if 'count' not in st.session_state: | |
| st.session_state.count = 0 | |
| increment = st.button('Add') | |
| if increment: | |
| st.session_state.count += 1 | |
| st.write('Count = ', st.session_state.count) | |
| #Layout | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.altair_chart(chrt) | |
| with col2: | |
| st.plotly_chart(fig1, use_container_width=True) | |
| with st.beta_container(): | |
| st.plotly_chart(figx, use_container_width=True) | |
| #Add side widget | |
| def your_widget(key): | |
| st.subheader('Hi! Welcome') | |
| return st.button(key + "Step") | |
| # Displayed in the main area | |
| clicked = your_widget("First") | |
| # Shown within an expander | |
| your_expander = st.expander("Expand", expanded=True) | |
| with your_expander: | |
| clicked = your_widget("Second") | |
| # Shown in the st.sidebar! | |
| with st.sidebar: | |
| clicked = your_widget("Last") | |
| #Session State | |
| # Initialization | |
| if 'key' not in st.session_state: | |
| st.session_state['key'] = 'value' | |
| # Session State also supports attribute based syntax | |
| if 'key' not in st.session_state: | |
| st.session_state.key = 'value' | |
| st.session_state.key = 'value x' # New Attribute API | |
| st.session_state['key'] = 'value x' # New Dictionary like API | |
| st.write(st.session_state) | |
| #Uploading files | |
| import streamlit as st | |
| #upload single file | |
| file = st.file_uploader("Please select a file to upload") | |
| if file is not None: | |
| #Can be used wherever a "file-like" object is accepted: | |
| df= pd.read_csv(file) | |
| st.dataframe(df) | |
| #Multiple files | |
| #adding a file uploader to accept multiple CSV file | |
| uploaded_files = st.file_uploader("Please select a CSV file", accept_multiple_files=True) | |
| for file in uploaded_files: | |
| df = pd.read_csv(file) | |
| st.write("File uploaded:", file.name) | |
| st.dataframe(df) | |
| #Uploading and Processing | |
| #upload single file | |
| from PIL import Image | |
| from PIL import ImageEnhance | |
| def load_image(image): | |
| img = Image.open(image) | |
| return img | |
| file = st.file_uploader("Please select image to upload and process") | |
| if file is not None: | |
| image = Image.open(file) | |
| fig = plt.figure() | |
| st.subheader("Original Image") | |
| plt.imshow(image) | |
| st.pyplot(fig) | |
| fig = plt.figure() | |
| contrast = ImageEnhance.Contrast(image).enhance(12) | |
| plt.imshow(contrast) | |
| st.subheader("Preprocessed Image") | |
| st.pyplot(fig) | |
| #Image classification | |
| import keras | |
| from PIL import Image, ImageOps | |
| import numpy as np | |
| import streamlit as st | |
| import streamlit as st | |
| from transformers import pipeline | |
| '''Hugging Face''' | |
| import streamlit as st | |
| from transformers import pipeline | |
| if __name__ == "__main__": | |
| # Define the title of the and its description | |
| st.title("Answering questions using NLP through Streamlit interface") | |
| st.write("Pose questions, get answers") | |
| # Load file | |
| raw_text = st.text_area(label="Enter a text here") | |
| if raw_text != None and raw_text != '': | |
| # Display text | |
| with st.expander("Show question"): | |
| st.write(raw_text) | |
| # Conduct question answering using the pipeline | |
| question_answerer = pipeline('question-answering') | |
| answer = '' | |
| question = st.text_input('Ask a question') | |
| if question != '' and raw_text != '': | |
| answer = question_answerer({ | |
| 'question': question, | |
| 'context': raw_text | |
| }) | |
| st.write(answer) | |