Update app.py

app.py

@@ -58,308 +58,3 @@ import time
 #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)
-
-
-@st.cache
-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)
-
-        
-        
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