Kamanda/Streamlit_Machine_Learning

1import streamlit as st2st.set_option('deprecation.showPyplotGlobalUse', False)3#st.markdown()4st.title("Document Title")5st.header("Article header")6st.subheader("Article subheader")7st.code("y = mx + c")8st.latex("\ int a y^2 \ , dy")9st.text("This is a chair!")10st.markdown('Staying hydrated is **_very_ cool**.')11 12students = ["Amelia Kami", "Antoinne Mark", "Peter Zen", "North Kim"]13 14marks = [82, 76, 96, 68]15 16import pandas as pd17 18df = pd.DataFrame()19 20df["Student Name"] = students21 22df["Marks"] = marks23#save to dataframe24df.to_csv("students.csv", index = False)25#display26st.dataframe(df)27 28#Static table29st.table(df)30 31#Metrics32st.metric("KPI", 56, 3)33#Json34st.json(df.to_dict())35 36#Code37#average of a list38code = '''def cal_average(numbers):39    sum_number = 040    for t in numbers:41        sum_number = sum_number + t           42 43    average = sum_number / len(numbers)44    return average'''45st.code(code, language='python')46#progress bar47 48import streamlit as st49import time50 51# Sample Progress bar52#bar_p = st.progress(0)53 54#for percentage_complete in range(100):55    #time.sleep(0.1)56    #bar_p.progress(percentage_complete + 1)57 58#with st.spinner('Please wait...'):59    #time.sleep(5)60#st.write('Complete!')61 62 63#Displaying an image using Streamlit64from PIL import Image65image = Image.open('media/ann-savchenko-H0h_89iFsWs-unsplash.jpg')66 67#st.image(image, caption='Sunset grass backgrounds')68 69#plotly70import plotly.express as px71# This dataframe has 244 rows, but 4 unique entries for the `day` variable72df = px.data.tips()73figx = px.pie(df, values='tip', names='day', title='Tips per day')74# Plot!75st.plotly_chart(figx, use_container_width=True)76 77#Altair78import altair as alt79import streamlit as st80import numpy as np81 82df = pd.DataFrame(83     np.random.randn(300, 4),84     columns=['a', 'b', 'c', 'd'])85 86chrt = alt.Chart(df).mark_circle().encode(87     x='a', y='b', size='c', color='c', tooltip=['a', 'b', 'c', 'd'])88 89st.altair_chart(chrt, use_container_width=True)90 91#Matplotlib92import matplotlib.pyplot as plt93import numpy as np94 95arr = np.random.normal(1, 1, size=1000)96fig, ax = plt.subplots()97ax.hist(arr, bins=30)98plt.grid()99st.pyplot(fig)100 101#Interactive widgets102st.button("Click here")103#st.download_button("Download audio", file)104selected = st.checkbox("Accept terms")105choice = st.radio("Select one", ["Apples", "Oranges"])106 107 108option = st.selectbox(109     'How would you like to receive your package?',110     ('By air', 'By sea', 'By rail'))111 112st.write('You selected:', option)113import datetime114day = st.date_input(115     "When is your birthday?",116     datetime.date(2022, 7, 6))117st.write('Your birthday is:', day)118 119color = st.color_picker('Choose A Color', '#00FFAA')120st.write('The selected color is', color)121 122 123@st.cache124def fetch_data():125    df = pd.read_csv("students.csv")126    return df127 128#data = fetch_data()129 130#Visualization131 132import matplotlib.pyplot as plt133import numpy as np134 135#Matplotlib136 137import matplotlib.pyplot as plt138import numpy as np139fig = plt.figure()140ax = fig.add_axes([0,0,1,1])141animals = ["Zebras", "Elephants", "Rhinos", "Leopards"]142number = [65, 72, 77, 59]143ax.bar(animals, number)144fig = plt.show()145st.pyplot(fig)146#Seaborn147import seaborn as sns148fig = plt.figure()149ax = sns.barplot(x = animals, y = number)150fig = plt.show()151st.pyplot(fig)152 153#Altair154#define data155df = pd.DataFrame()156 157df["Animals"] = animals158df["Number"] = number159#create chart160chrt = alt.Chart(df, title="Ploting using Altair in Streamlit").mark_bar().encode(161    x='Animals',162    y='Number'163)164#render with Streamlit165st.altair_chart(chrt, use_container_width=True)166#Plotly167#define data168df = pd.DataFrame()169df["Animals"] = animals170df["Number"] = number171#create plot172fig1 = px.bar(df, x='Animals', y='Number', title="Ploting using Plotly in Streamlit")173# Plot!174st.plotly_chart(fig1, use_container_width=True)175 176#data177df = pd.DataFrame()178df["Animals"] = animals179df["Number"] = number180#visualization181st.vega_lite_chart(df, {182     'mark': {'type': 'bar', 'tooltip': True},183     'encoding': {184         'x': {'field': 'Animals', 'type': 'nominal'},185         'y': {'field': 'Number', 'type': 'quantitative'},186     },187 }, use_container_width=True)188 189#Maps190import pandas as pd191states = pd.read_html('https://developers.google.com/public-data/docs/canonical/states_csv')[0]192states.columns = ['state', 'lat', 'lon', 'name']193states = states.drop(['state', 'name'], axis = 1)194 195st.map(states)196 197#Components198from st_aggrid import AgGrid199AgGrid(df)200 201#Statefulnness202import streamlit as st203 204st.title('Streamlit Counter Example')205count = 0206 207add = st.button('Addition')208if add:209    count += 1210 211st.write('Count = ', count)212 213 214import streamlit as st215 216st.title('Counter Session State')217if 'count' not in st.session_state:218    st.session_state.count = 0219 220increment = st.button('Add')221if increment:222    st.session_state.count += 1223 224st.write('Count = ', st.session_state.count)225 226#Layout227col1, col2 = st.columns(2)228 229with col1:230    st.altair_chart(chrt)231with col2:232    st.plotly_chart(fig1, use_container_width=True)233with st.beta_container():234    st.plotly_chart(figx, use_container_width=True)235 236 237 238#Add side widget239 240def your_widget(key):241    st.subheader('Hi! Welcome')242    return st.button(key + "Step")243 244# Displayed in the main area245clicked = your_widget("First")246 247# Shown within an expander248your_expander = st.expander("Expand", expanded=True)249with your_expander:250    clicked = your_widget("Second")251 252# Shown in the st.sidebar!253with st.sidebar:254    clicked = your_widget("Last")255#Session State256# Initialization257if 'key' not in st.session_state:258    st.session_state['key'] = 'value'259   260    261 262# Session State also supports attribute based syntax263if 'key' not in st.session_state:264    st.session_state.key = 'value'265    266st.session_state.key = 'value x'     # New Attribute API267st.session_state['key'] = 'value x'  # New Dictionary like API268 269st.write(st.session_state)270 271#Uploading files272 273import streamlit as st274 275#upload single file276file = st.file_uploader("Please select a file to upload")277if file is not None:278    #Can be used wherever a "file-like" object is accepted:279    df= pd.read_csv(file)280    st.dataframe(df)281 282#Multiple files283#adding a file uploader to accept multiple CSV file284uploaded_files = st.file_uploader("Please select a CSV file", accept_multiple_files=True)285for file in uploaded_files:286    df = pd.read_csv(file)287    st.write("File uploaded:", file.name)288    st.dataframe(df)289#Uploading and Processing290#upload single file291from PIL import Image292from PIL import ImageEnhance293def load_image(image):294    img = Image.open(image)295    return img296 297file = st.file_uploader("Please select image to upload and process")298if file is not None:299    image = Image.open(file) 300    fig = plt.figure()301    st.subheader("Original Image")302    plt.imshow(image)303    st.pyplot(fig)304    fig = plt.figure()305    contrast = ImageEnhance.Contrast(image).enhance(12)306    plt.imshow(contrast)307    st.subheader("Preprocessed Image")308    st.pyplot(fig)309 310    311#Image classification312import keras313from PIL import Image, ImageOps314import numpy as np315 316 317 318import streamlit as st319import streamlit as st320from transformers import pipeline321 322'''Hugging Face'''323 324import streamlit as st325from transformers import pipeline326 327if __name__ == "__main__":328 329    # Define the title of the and its description330    st.title("Answering questions using NLP through Streamlit interface")331    st.write("Pose questions, get answers")332 333    # Load file334    335    raw_text = st.text_area(label="Enter a text here")336    if raw_text != None and raw_text != '':337 338        # Display text339        with st.expander("Show question"):340            st.write(raw_text)341 342        # Conduct question answering using the pipeline343        question_answerer = pipeline('question-answering')344 345        answer = ''346        question = st.text_input('Ask a question')347 348        if question != '' and raw_text != '':349            answer = question_answerer({350                'question': question,351                'context': raw_text352            })353 354        st.write(answer)355 356        357        358 359 360 361 362 363 364 365