NumHDonors_cutoff = st.sidebar.slider(
label="NumHDonors",
min_value=0,
max_value=15,
value=5,
step=10,
)
NumHAcceptors_cutoff = st.sidebar.slider(
label="NumHAcceptors",
min_value=0,
max_value=20,
value=10,
step=1,
)
df_result = df[df["MW"] < weight_cutoff]
df_result2 = df_result[df_result["LogP"] < logp_cutoff]
df_result3 = df_result2[df_result2["NumHDonors"] < NumHDonors_cutoff]
df_result4 = df_result3[df_result3["NumHAcceptors"] < NumHAcceptors_cutoff]
st.write(df_result4.shape)
st.write(df_result4)
raw_html = mols2grid.display(
df_result4,
subset=["Name", "img", "MW", "LogP", "NumHDonors", "NumHacceptors"],
mapping={
"smiles": "SMILES",
"generic_name": "Name"
})._repr_html()
components.html(raw_html, width=900, height=1100, scrolling=False)
def write(text, c='black', height=50):
html = f"""
{bs()}
<p style='color:{c}'>{text}</p>"""
return components.html(html, height=height)
train_int_col.image(train_inp_example, caption="INPUT EXAMPLE", width=250)
train_inp_example = torch.tensor(train_inp_example)
train_out_example = preprocess(uploaded_out_example,
image_size=TRAINING_IMAGE_SIZE[0:2],
gray_scale=False)
train_out_col.image(train_out_example, caption="OUTPUT EXAMPLE", width=250)
train_out_example = torch.tensor(train_out_example)
# image = preprocess(uploaded_file, image_size=IMAGE_SIZE[0:2], gray_scale=False)
image = preprocess(uploaded_file,
image_size=OUTPUT_IMAGE_SIZE[0:2],
gray_scale=False)
input_col.image(image, width=250, caption='input image')
net.add(train_inp_example, train_out_example)
output = net(torch.tensor(image)).numpy()
output_col.image(output, width=250, caption='output image')
# st.write(net.graph)
net.graph.write_html('graph.html')
# components.html(net.graph.html, height)
components.html(net.graph.html, height=600)
# st.write(html)
#
# image = torch.rand(IMAGE_SIZE)
# rand_input_col.image(image.numpy(), width=250, caption='random input image')
# output = net(torch.tensor(image)).numpy()
# rand_output_col.image(output, width=250, caption='output image')
ql.update(best_models)
#n_iter = 2
#for i in range(n_iter):
# qg.fit(train, threads=8, show=None)
# model_graph_holder.markdown(render_svg(qg[0]._repr_svg_()),unsafe_allow_html=True)
# model_iter_holder.write(f"Iteration:{i+1}/{n_iter}")
# ql.update(qg.best())
# my_bar.progress(i/n_iter + 1/n_iter)
model_iter_holder.write("Done")
with st.beta_expander("Model Validation on the Training Data"):
# Plot Summary
components.html(models[0].plot(train)._repr_html_(),
height=600,
width=800)
#col1, col2 = st.beta_columns(2)
#fig_roc, ax = plt.subplots()
#models[0].plot_roc_curve(train)
#col1.pyplot(fig_roc)
#fig_confusion, ax = plt.subplots()
#models[0].plot_confusion_matrix(train)
#col2.pyplot(fig_confusion)
with st.beta_expander("Model Validation on the Test Data"):
# Plot Summary
components.html(models[0].plot(test)._repr_html_(),
height=600,
width=800)
def display(text, c='black', s=1, height=70):
html = f"""
{bs()}
<h1 class='display-{s}' style='color:{c}'>{text}</h1>"""
return components.html(html, height=height)
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
if page == 'Home':
# Additional sidebar
st.sidebar.markdown('### FAQ')
if st.sidebar.button('Not sure how to pronounce my name?'):
st.sidebar.audio('media/sound-name.mp3')
# Main page
avatar = "<img src='data:image/png;base64,{}' class='img-fluid rounded-circle mx-auto d-block' style='max-width:25%'>".format(
img_to_bytes("media/avatar.png"))
st.write(avatar, unsafe_allow_html=True)
st.write(read_markdown_file("content/intro.md"), unsafe_allow_html=True)
components.html(socia_media_links, height=40)
elif page == 'Projects':
# Additional sidebar
st.sidebar.markdown('### Tech Demo')
sentiment_test = TextBlob(
st.sidebar.text_input('Sentiment Polarity & Subjectivity'))
if sentiment_test:
st.sidebar.markdown(f"""
Polarity: {round(sentiment_test.sentiment.polarity,2)}
Subjectivity: {round(sentiment_test.sentiment.subjectivity,2)}
""")
st.sidebar.markdown('---')
feedback = st.sidebar.text_input('Feedback')
def render_entities(raw_text):
docx = nlp(raw_text)
html = displacy.render(docx, style='ent')
html = html.replace("\n\n", "\n")
result = HTML_WRAPPER.format(html)
stc.html(result, height=1000)
from jinja2 import Environment, FileSystemLoader
import streamlit.components.v1 as components
import streamlit as st
st.title("Hello Lottie-web !")
loop = st.checkbox("Loop animation", True)
with open('data.json') as json_file:
lottie_data = json_file.read()
env = Environment(loader=FileSystemLoader('./'),
trim_blocks=True,
lstrip_blocks=True)
template = env.get_template('template.html.jinja')
html_data = template.render(data=lottie_data, loop=str(loop).lower())
components.html(html_data, height=800)
def main():
"""A Simple Summarization NLP App"""
st.title("NLP App with Streamlit")
menu = ["Home", "NLP(files)", "About"]
choice = st.sidebar.selectbox("Menu", menu)
if choice == "Home":
st.subheader("Home: Analyse Text")
raw_text = st.text_area("Enter Text Here")
num_of_most_common = st.sidebar.number_input("Most Common Tokens", 5, 15)
if st.button("Analyze"):
with st.beta_expander("Original Text"):
st.write(raw_text)
with st.beta_expander("Text Analysis"):
token_result_df = text_analyzer(raw_text)
st.dataframe(token_result_df)
with st.beta_expander("Entities"):
# entity_result = get_entities(raw_text)
# st.write(entity_result)
entity_result = render_entities(raw_text)
# st.write(entity_result)
stc.html(entity_result, height=1000, scrolling=True)
# Layouts
col1, col2 = st.beta_columns(2)
with col1:
with st.beta_expander("Word Stats"):
st.info("Word Statistics")
docx = nt.TextFrame(raw_text)
st.write(docx.word_stats())
with st.beta_expander("Top Keywords"):
st.info("Top Keywords/Tokens")
processed_text = nfx.remove_stopwords(raw_text)
keywords = get_most_common_tokens(
processed_text, num_of_most_common
)
st.write(keywords)
with st.beta_expander("Sentiment"):
sent_result = get_sentiment(raw_text)
st.write(sent_result)
with col2:
with st.beta_expander("Plot Word Freq"):
fig = plt.figure()
# sns.countplot(token_result_df['Token'])
top_keywords = get_most_common_tokens(
processed_text, num_of_most_common
)
plt.bar(keywords.keys(), top_keywords.values())
plt.xticks(rotation=45)
st.pyplot(fig)
with st.beta_expander("Plot Part of Speech"):
try:
fig = plt.figure()
sns.countplot(token_result_df["PoS"])
plt.xticks(rotation=45)
st.pyplot(fig)
except:
st.warning("Insufficient Data: Must be more than 2")
with st.beta_expander("Plot Wordcloud"):
try:
plot_wordcloud(raw_text)
except:
st.warning("Insufficient Data: Must be more than 2")
with st.beta_expander("Download Text Analysis Result"):
make_downloadable(token_result_df)
elif choice == "NLP(files)":
st.subheader("NLP Task")
text_file = st.file_uploader("Upload Files", type=["pdf", "docx", "txt"])
num_of_most_common = st.sidebar.number_input("Most Common Tokens", 5, 15)
if text_file is not None:
if text_file.type == "application/pdf":
raw_text = read_pdf(text_file)
# st.write(raw_text)
elif text_file.type == "text/plain":
# st.write(text_file.read()) # read as bytes
raw_text = str(text_file.read(), "utf-8")
# st.write(raw_text)
else:
raw_text = docx2txt.process(text_file)
# st.write(raw_text)
with st.beta_expander("Original Text"):
st.write(raw_text)
with st.beta_expander("Text Analysis"):
token_result_df = text_analyzer(raw_text)
st.dataframe(token_result_df)
with st.beta_expander("Entities"):
# entity_result = get_entities(raw_text)
# st.write(entity_result)
entity_result = render_entities(raw_text)
# st.write(entity_result)
stc.html(entity_result, height=1000, scrolling=True)
# Layouts
col1, col2 = st.beta_columns(2)
with col1:
with st.beta_expander("Word Stats"):
st.info("Word Statistics")
docx = nt.TextFrame(raw_text)
st.write(docx.word_stats())
with st.beta_expander("Top Keywords"):
st.info("Top Keywords/Tokens")
processed_text = nfx.remove_stopwords(raw_text)
keywords = get_most_common_tokens(
processed_text, num_of_most_common
)
st.write(keywords)
with st.beta_expander("Sentiment"):
sent_result = get_sentiment(raw_text)
st.write(sent_result)
with col2:
with st.beta_expander("Plot Word Freq"):
fig = plt.figure()
# sns.countplot(token_result_df['Token'])
top_keywords = get_most_common_tokens(
processed_text, num_of_most_common
)
plt.bar(keywords.keys(), top_keywords.values())
plt.xticks(rotation=45)
st.pyplot(fig)
with st.beta_expander("Plot Part of Speech"):
try:
fig = plt.figure()
sns.countplot(token_result_df["PoS"])
plt.xticks(rotation=45)
st.pyplot(fig)
except:
st.warning("Insufficient Data")
with st.beta_expander("Plot Wordcloud"):
try:
plot_wordcloud(raw_text)
except:
st.warning("Insufficient Data")
with st.beta_expander("Download Text Analysis Result"):
make_downloadable(token_result_df)
else:
st.subheader("About")
background-image: none;
color: #ffffff
}</style>""", unsafe_allow_html=True)
#st.title("Record and Analyze Blast Operation Data")
menu = ["Home","Login","SignUp"]
choice = st.sidebar.selectbox("Menu",menu)
if choice == "Home":
components.html("""
<style>
.city {
background-color: rgb(159, 177, 188);
color: white;
border: 1px solid black;
margin: 5px;
padding: 5px;
text-align: center;
font-size:35px !important;
font-family:"B mitra", serif;
}
</style>
<body>
<div class="city"><h2>واحد آموزش و نیروی انسانی</h2></div>
""",width=750, height=200, scrolling=False)
pic = Image.open('LOGO.jpg')
st.image(pic,width=500)
#pi = Image.open('p.jpg')
#st.image(pi, use_column_width=True)
st.markdown("""
<style>
def displayData(split_data):
# Function to display data which has been split
global html_text
process_data = True
processing_info = st.empty()
progress_bar = st.empty()
cancel_button = st.empty()
bar = progress_bar.progress(0)
cancel = cancel_button.button('Cancel Processing')
if cancel:
process_data = False
html_text = """<!DOCTYPE html>
<html>
<head>
<style>
.pass {
color:black
}
.fail {
color:red
}
.text-left {
text-align: left;
}
table {
font-family: arial, sans-serif;
border-collapse: collapse;
width: 100%;
}
td, th {
border: 1px solid #dddddd;
text-align: center;
padding: 5px;
}
/*table thead tr{
display:block;
}
table tbody{
display:block;
height:380px;
overflow:auto;//set tbody to auto
}
*/
.headers {
background-color: #dddddd;
}
</style>
</head>
<body>
<table width="100%">
<thead>
<tr>
<th width="64%">Clause</th>
<th width="11%">Acceptance Probability (%)</th>
<th width="12%">Acceptable(0), Unacceptable(1)</th>
<th width="11%">Disagree?</th>
</tr></thead>
<tbody>"""
index_for_display = 0
for data in split_data:
index_for_display = index_for_display + 1
if len(data) <= 0:
continue
if not process_data:
break
if data.find("Title:") >= 0:
html_text = html_text + """
<tr class="headers">
<td colspan="4" class="text-left"> """ + data[
6:] + """</td>
</tr>"""
else:
processing_info.text("Processing " + str(index_for_display) +
" of " + str(len(split_data)) + " clauses")
bar.progress(index_for_display / len(split_data))
bar.progress(index_for_display / len(split_data))
prediction_result = predictor.predict(data)
df2 = prediction_result.iloc[[-1]]
row_class = "pass"
if df2['prediction'].values[0] == 1:
row_class = "fail"
html_text = html_text + """
<tr class=""" + row_class + """>
<td width="65%" class="text-left"> """ + html.escape(
data) + """</td>
<td width="11%">
""" + str(df2['acceptable_probability'].values[0]) + """</td>
<td width="12%">""" + str(df2['prediction'].values[0]) + """</td>
<td><span id='recording_""" + str(
index_for_display
) + """' style='display:none'>Recorded</span><button
type="button" class="btn btn-warning" onclick="this.style.display = 'none';document.getElementById(
'recording_""" + str(
index_for_display
) + """').style.display = 'block'">Disagree</button></td>
</tr>"""
html_text = html_text + """
</tbody>
</table>
</body>
</html>
"""
processing_info.empty()
progress_bar.empty()
cancel_button.empty()
st.subheader('Assessment Result')
components.html(html_text, width=None, height=700, scrolling=True)
import streamlit.components.v1 as components
import qrcode
from PIL import Image
from PIL import Image
img = Image.open("qrcode.jpg")
st.image(img, width=700)
html_code = """
<div style="background-color: #3cba54 ; padding: 10px; border-radius: 10px">
<h1 style="color:white; text-align: center">QR Code Generator</h1>
</div>
"""
components.html(html_code)
# Add the data to QR
data = st.text_input("Enter the data you want to embed")
data_title = data.title()
# Number Inputs
box_size = st.number_input("Enter the box size")
border = st.number_input("Enter the border spacing value")
# Select Box Inputs
from matplotlib import colors as mcolors
colors = mcolors.CSS4_COLORS
def st_dtree(plot, height=None):
dtree_html = f'<body>{viz.svg()}</body>'
components.html(dtree_html, height=height)
import streamlit as st
import streamlit.components.v1 as components
# bootstrap 4 collapse example
components.html(
"""
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN"
"http://www.w3.org/TR/html4/strict.dtd">
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<title>Тег SCRIPT</title>
</head>
<body>
<script type="text/javascript">
document.write ('<table width="100%" border="1">');
for (i=1; i<6; i++) {
document.writeln("<tr>");
for (j=1; j<6; j++) document.write("<td>" + i + j + "<\/td>");
document.writeln("<\/tr>");
}
document.write ("<\/table> ");
</script>
</body>
</html>
""",
height=100,
)
#components.iframe("https://docs.streamlit.io/en/latest")
st.markdown('Streamlit is **_really_ cool**.')
def main():
menu = ["Home", "Pandas Profile", "Sweetviz", "About"]
choice = st.sidebar.selectbox("Menu", menu)
if choice == "Pandas Profile":
st.subheader("Automated EDA with Pandas Profile")
data_file = st.file_uploader("Upload CSV", type=['csv'])
st.set_option('deprecation.showfileUploaderEncoding', False)
if data_file is not None:
df = pd.read_csv(data_file)
st.dataframe(df.head())
profile = ProfileReport(df)
st_profile_report(profile)
elif choice == "Sweetviz":
st.subheader("Automated EDA with Sweetviz")
data_file = st.file_uploader("Upload CSV", type=['csv'])
st.set_option('deprecation.showfileUploaderEncoding', False)
if data_file is not None:
df = pd.read_csv(data_file)
st.dataframe(df.head())
if st.button("Generate Sweetviz Report"):
st_display_sweetviz("SWEETVIZ_REPORT.html")
#Normal Workflow (save .html)
#report = sv.analyze(df)
#report.show_html()
elif choice == "About":
st.subheader("Onur Erdogan tarafından yapılmıstır")
#components.iframe('https://www.google.com)
else:
st.subheader("Home")
components.html("""
<style>
* {box-sizing: border-box}
body {font-family: Verdana, sans-serif; margin:0}
.mySlides {display: none}
img {vertical-align: middle;}
/* Slideshow container */
.slideshow-container {
max-width: 1000px;
position: relative;
margin: auto;
}
/* Next & previous buttons */
.prev, .next {
cursor: pointer;
position: absolute;
top: 50%;
width: auto;
padding: 16px;
margin-top: -22px;
color: white;
font-weight: bold;
font-size: 18px;
transition: 0.6s ease;
border-radius: 0 3px 3px 0;
user-select: none;
}
/* Position the "next button" to the right */
.next {
right: 0;
border-radius: 3px 0 0 3px;
}
/* On hover, add a black background color with a little bit see-through */
.prev:hover, .next:hover {
background-color: rgba(0,0,0,0.8);
}
/* Caption text */
.text {
color: #f2f2f2;
font-size: 15px;
padding: 8px 12px;
position: absolute;
bottom: 8px;
width: 100%;
text-align: center;
}
/* Number text (1/3 etc) */
.numbertext {
color: #f2f2f2;
font-size: 12px;
padding: 8px 12px;
position: absolute;
top: 0;
}
/* The dots/bullets/indicators */
.dot {
cursor: pointer;
height: 15px;
width: 15px;
margin: 0 2px;
background-color: #bbb;
border-radius: 50%;
display: inline-block;
transition: background-color 0.6s ease;
}
.active, .dot:hover {
background-color: #717171;
}
/* Fading animation */
.fade {
-webkit-animation-name: fade;
-webkit-animation-duration: 1.5s;
animation-name: fade;
animation-duration: 1.5s;
}
@-webkit-keyframes fade {
from {opacity: .4}
to {opacity: 1}
}
@keyframes fade {
from {opacity: .4}
to {opacity: 1}
}
/* On smaller screens, decrease text size */
@media only screen and (max-width: 300px) {
.prev, .next,.text {font-size: 11px}
}
</style>
</head>
<body>
<div class="slideshow-container">
<div class="mySlides fade">
<div class="numbertext">1 / 3</div>
<img src="https://www.w3schools.com/howto/img_snow_wide.jpg" style="width:100%">
<div class="text">Caption Text</div>
</div>
<div class="mySlides fade">
<div class="numbertext">2 / 3</div>
<img src="https://www.w3schools.com/howto/img_nature_wide.jpg" style="width:100%">
<div class="text">Caption Two</div>
</div>
<div class="mySlides fade">
<div class="numbertext">3 / 3</div>
<img src="https://www.w3schools.com/howto/img_mountains_wide.jpg" style="width:100%">
<div class="text">Caption Three</div>
</div>
<a class="prev" onclick="plusSlides(-1)">❮</a>
<a class="next" onclick="plusSlides(1)">❯</a>
</div>
<br>
<div style="text-align:center">
<span class="dot" onclick="currentSlide(1)"></span>
<span class="dot" onclick="currentSlide(2)"></span>
<span class="dot" onclick="currentSlide(3)"></span>
</div>
<script>
var slideIndex = 1;
showSlides(slideIndex);
function plusSlides(n) {
showSlides(slideIndex += n);
}
function currentSlide(n) {
showSlides(slideIndex = n);
}
function showSlides(n) {
var i;
var slides = document.getElementsByClassName("mySlides");
var dots = document.getElementsByClassName("dot");
if (n > slides.length) {slideIndex = 1}
if (n < 1) {slideIndex = slides.length}
for (i = 0; i < slides.length; i++) {
slides[i].style.display = "none";
}
for (i = 0; i < dots.length; i++) {
dots[i].className = dots[i].className.replace(" active", "");
}
slides[slideIndex-1].style.display = "block";
dots[slideIndex-1].className += " active";
}
</script>
""")
import streamlit.components.v1 as components
from pyecharts import options as opts
from pyecharts.charts import Bar
c = (
Bar().add_xaxis(
["Microsoft", "Amazon", "IBM",
"Oracle", "Google", "Alibaba"]).add_yaxis(
'2017-2018 Revenue in (billion $)',
[21.2, 20.4, 10.3, 6.08, 4, 2.2]).set_global_opts(
title_opts=opts.TitleOpts(title="Top cloud providers 2018",
subtitle="2017-2018 Revenue"),
toolbox_opts=opts.ToolboxOpts()).render_embed(
) # generate a local HTML file
)
components.html(c, width=1000, height=1000)
def st_display_sweetviz(report_html, width=1000, height=500):
report_file = codecs.open(report_html, 'r')
page = report_file.read()
components.html(page, width=width, height=height, scrolling=True)
return img
if fn == "Clifford":
st.markdown("""## Clifford Attractor""")
st.latex("x_{n +1} = \sin(%2.1f y_{n}) + %2.1f \cos(%2.1f x_{n})" %
(a, c, a))
st.latex("y_{n +1} = \sin(%2.1f x_{n}) + %2.1f \cos(%2.1f y_{n})" %
(b, d, b))
g = dsplot(Clifford, (0, 0, a, b, c, d),
plotsize,
cmap=palette[cmap][::-1])
components.html(g._repr_html_(), height=plotsize + 10, width=plotsize + 10)
elif fn == "De Jong":
st.markdown("""## De Jong Attractor""")
st.latex("x_{n +1} = \sin(%2.1f y_{n}) - \cos(%2.1f x_{n})" % (a, b))
st.latex("y_{n +1} = \sin(%2.1f x_{n}) - \cos(%2.1f y_{n})" % (c, d))
dg = dsplot(De_Jong, (0, 0, a, b, c, d),
plotsize,
cmap=palette[cmap][::-1])
components.html(dg._repr_html_(),
height=plotsize + 10,
width=plotsize + 10)
def st_shap(plot, height=None):
shap_html = f"<head>{shap.getjs()}</head><body>{plot.html()}</body>"
components.html(shap_html, height=height)
def write():
st.subheader("Data Cleaning")
df = pd.read_csv('data/ljubljana_categories.csv', sep=',')
# Drop location duplicates
st.success(
'If multiple rows have the same GPS coordinates, only the last one is kept.'
)
# Renaming id column
df.columns = df.columns.str.lower()
df = df.drop_duplicates(subset=['latitude', 'longitude'], keep='last')
df = df.reset_index(drop=True)
# Adding a distance
df_d = df.assign(distance=0)
for i in range(len(df_d) - 1):
j = i + 1
df_d.loc[j, 'distance'] = round(
distance((df_d.latitude[i], df_d.longitude[i]),
(df_d.latitude[j], df_d.longitude[j])) * 1000, 2)
# Filtering for min. and max. distances
st.subheader('Plausibility tests')
data_points = len(df_d)
slide_max = int(df_d['distance'].max()) + 10
dist = st.slider(
'Select a range of plausible distances beween observations to be included: ',
0, slide_max, (0, 200))
df_d = df_d[(df_d['distance'] >= dist[0]) & (df_d['distance'] <= dist[1])]
filtered_points = len(df_d)
message = 'Minimum distance is ' + str(dist[0]) + ' meters and maximum distance is ' + str(dist[1]) + ' meters. Your data are reduced by: ' + \
str(round(100 * (1 - filtered_points / data_points), 2)) + \
' %. Using the slider, you can change these values and see the effect in the table below. There are ' + \
str(filtered_points) + ' data rows now.'
st.success(message)
# Display DataFrame
df_d = df_d.reset_index(drop=True)
st.dataframe(df_d[[
'time', 'latitude', 'longitude', 'distance', 'sats', 'precision',
'category'
]].style.background_gradient(subset=['distance', 'precision'],
cmap='Blues'))
# Safe new data frame
st.markdown(get_table_download_link(df_d), unsafe_allow_html=True)
# Pairwise Viz
st.subheader("Pairwise comparison of mapping data")
fig2 = plt.subplots()
fig2 = sns.pairplot(
df_d[['latitude', 'longitude', 'rawtime', 'category', 'distance']],
markers=["o", "s", "D", "*"]) # hue='Category'
st.pyplot(fig2)
# Use of categories
col1, col2 = st.beta_columns(2)
col1.info('This bar chart shows the usage frequency per category.')
cat = df_d[['category']].groupby(['category']).size()
catSeries = pd.Series([0, 0, 0, 0, 0, 0])
catSeries = catSeries[1:6]
for x in cat.index:
catSeries[x] = cat[x]
fig, ax = plt.subplots()
ax = sns.barplot(x=catSeries.index, y=catSeries)
col1.pyplot(fig)
# GPS quality
col2.info(
'This bar chart indicates the quality of your GPS coordinates. 5+ is good.'
)
satDistribution = df_d.groupby(
df_d['sats']).size().sort_values(ascending=False)
satNum = list(satDistribution.keys())
satFreq = list(satDistribution)
fig2, ax2 = plt.subplots()
ax2 = sns.barplot(x=satNum, y=satFreq)
col2.pyplot(fig2)
# category coloring
# tracks_d = categories.assign(Distance=0)
df_d['color'] = ""
df_d['color'] = df_d['color'].astype('object')
cat_color = {
1: [0, 0, 255, 250], # Blue
2: [255, 0, 0, 250], # Red
3: [0, 255, 0, 250], # Green
4: [255, 106, 0, 250], # Orange
5: [0, 88, 0, 250] # Dark Green
}
for i in range(len(df_d)):
df_d.at[i, 'color'] = cat_color[df_d.at[i, 'category']]
st.subheader("Data Map")
st.info(
"... let's draw maps! You can choose the size of markers (4-12 is recommended) and the map's background."
)
marker = st.number_input('Marker Size: ', min_value=2, value=8)
MapColor = {
"Dark": 'mapbox://styles/innodesign/ckl128luk027z17mwftpdxyg1',
"Light": 'mapbox://styles/mapbox/streets-v8',
}
map_col = st.radio("Map Background: ", list(MapColor.keys()))
data = df_d[['latitude', 'longitude', 'color']]
midpoint = (np.average(data['latitude']), np.average(data['longitude']))
st.info('Centre coordinates of the map: ' + str(round(midpoint[0], 4)) +
' and ' + str(round(midpoint[1], 4)))
components.html("""
<h4 style="font-family: Verdana, sans-serif">Color coding of categories:</h4>
<ul style="font-family: Verdana, sans-serif; background-color: rgb(219, 213, 213)">
<li style="color: rgb(0, 0, 255); list-style: none; background-color: rgb(219, 213, 213)"> Category 1: Blue</li>
<li style="color: rgb(255, 0, 0); list-style: none; background-color: rgb(219, 213, 213)">Category 2: Red</li>
<li style="color: rgb(0, 255, 0); list-style: none; background-color: rgb(219, 213, 213)">Category 3: Green</li>
<li style="color: rgb(255, 106, 0); list-style: none; background-color: rgb(219, 213, 213)">Category 4: Orange</li>
<li style="color: rgb(0, 88, 0); list-style: none; background-color: rgb(219, 213, 213)">Category 5: Dark Green</li>
<li style="color: rgb(219, 213, 213); list-style: none; background-color: rgb(219, 213, 213)"..<li>
</ul>
""",
height=180)
st.pydeck_chart(
pdk.Deck(
# map_style='mapbox://styles/mapbox/dark-v9',
# map_style='mapbox://styles/mapbox/streets-v8',
map_style=MapColor[map_col],
initial_view_state=pdk.ViewState(
latitude=midpoint[0],
longitude=midpoint[1],
zoom=13,
pitch=0,
),
layers=pdk.Layer(
"ScatterplotLayer",
# type='HexagonLayer' / ScatterplotLayer / PointCloudLayer,
data=data,
get_position='[longitude, latitude]',
elevation_scale=4,
get_fill_color='color',
get_radius=marker,
opacity=0.5,
filled=True)))
st.info(
'The heatmap below gives an impression of those areas, where most observations have been mapped. The more you zoom in, the more fine grained the aggregation becomes.'
)
st.pydeck_chart(
pdk.Deck(map_style=MapColor[map_col],
initial_view_state=pdk.ViewState(
latitude=midpoint[0],
longitude=midpoint[1],
zoom=13,
pitch=0,
),
layers=pdk.Layer(
"HeatmapLayer",
data=data,
get_position='[longitude, latitude]',
get_fill_color='color',
opacity=0.9,
aggregation='MEAN',
)))
return y_pred.pooler_output.tolist()
def rgb_to_hex(rgb):
r, g, b = int(min(rgb[0], 1) * 255), int(min(rgb[1], 1) * 255), int(
min(rgb[2], 1) * 255)
return '#{:02x}{:02x}{:02x}'.format(r, g, b)
def code_to_rgb(code):
return [int(code[1:3], 16), int(code[3:5], 16), int(code[5:7], 16)]
def render_color_markdown(texts, colors):
span_tags = [
f'<span style="font-weight: bold; color: {rgb_to_hex(color)}">{text}</span>'
for text, color in zip(texts, colors)
]
return ' '.join(span_tags)
tokenizer, model = tokenizer(), load_model()
text = st.text_input('text',
'apple cherry peach grape orange watermelon strawberry')
if len(text) != 0:
texts = text.split(' ')
rgbs = texts_to_words(texts, model)
components.html(render_color_markdown(texts, rgbs))
def st_shap(plot, height=200,width=870):
shap_html = f"<head>{shap.getjs()}</head><body>{plot.html()}</body>"
components.html(shap_html, height=height,width=width)
def header(text, c='black', s=1, height=50):
html = f"""
{bs()}
<h{s} style='color:{c}'>{text}</h{s}>"""
return components.html(html, height=height)
#Adding the data
kepler_map.add_data(data=gdf_city_edge, name='city lockdown')
kepler_map.add_data(data=gdf.drop(['loc'], axis = 1), name='location')
kepler_map.add_data(data=gdf_lockdown.drop(['loc'], axis = 1), name = 'lockdown')
#Adding the config
kepler_map.config = config
#Transform to a html file
kepler_map_html = kepler_map._repr_html_()
return(kepler_map_html)
except :
st.text('location not found ! 😲')
#Header
st.title('Interactive lockdown map from your location')
#Subheader
st.subheader('Made with ❤️ by Basile Goussard')
#User input
user_input = st.text_input("Your location", 'Planches, Trouville')
#Building the map
components.html(main(user_input), width=700, height=700)
def lead(text, c='black', height=50):
html = f"""
{bs()}
<p class='lead' style='color:{c}'>{text}</p>"""
return components.html(html, height=height)
import streamlit.components.v1 as components # type: ignore
from jinja2 import Template
with open("src/apps/components/action_button.html") as fo:
button_template = Template(fo.read())
components.html(button_template.render(state=None), height=64)
df = pd.read_csv(url)
#df = df.set_index(pd.Datetimeindex('Date')
#df.set_index(pd.DatetimeIndex('Date'), inplace=True)
return df
#-------------------------------------------------#
# Title
HTML_BANNER = """
<div style="background-color:#8B0000;padding:10px;border-radius:10px">
<h1 style="color:white;text-align:center;">Crypto Currency Analysis</h1>
<p style="color:white;text-align:center;">Built with: [email protected]</p>
</div>
"""
stc.html(HTML_BANNER)
st.markdown('''
**Description:** This is web app `Crypto Coins` currency Analysis
''')
#-----------------------------------------------------------------#
#main
df = load_dataset()
df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)
menu = ['Analysis', 'Visualizations']
choice = st.sidebar.selectbox('Menu', menu)
analysis_menu = ['Show Data Structure', 'Daily Analysis']
components.html(
"""
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" integrity="sha384-TX8t27EcRE3e/ihU7zmQxVncDAy5uIKz4rEkgIXeMed4M0jlfIDPvg6uqKI2xXr2" crossorigin="anonymous">
<script src="https://code.jquery.com/jquery-3.5.1.slim.min.js" integrity="sha384-DfXdz2htPH0lsSSs5nCTpuj/zy4C+OGpamoFVy38MVBnE+IbbVYUew+OrCXaRkfj" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/umd/popper.min.js" integrity="sha384-9/reFTGAW83EW2RDu2S0VKaIzap3H66lZH81PoYlFhbGU+6BZp6G7niu735Sk7lN" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.min.js" integrity="sha384-w1Q4orYjBQndcko6MimVbzY0tgp4pWB4lZ7lr30WKz0vr/aWKhXdBNmNb5D92v7s" crossorigin="anonymous"></script>
</head>
<body>
<div class="p-3 mb-2 bg-light text-dark">
<nav class="navbar navbar-expand-lg navbar-light bg-light">
<svg width="1em" height="1em" viewBox="0 0 16 16" class="bi bi-caret-right-fill" fill="currentColor" xmlns="http://www.w3.org/2000/svg">
<path d="M12.14 8.753l-5.482 4.796c-.646.566-1.658.106-1.658-.753V3.204a1 1 0 0 1 1.659-.753l5.48 4.796a1 1 0 0 1 0 1.506z"/>
</svg>
<a class="navbar-brand" href="#">My Portfolio</a>
<button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
<span class="navbar-toggler-icon"></span>
</button>
<div class="collapse navbar-collapse" id="navbarNav">
<ul class="navbar-nav">
<li class="nav-item active">
<a class="nav-link" href="#">Home <span class="sr-only">(current)</span></a>
</li>
<li class="nav-item">
<a class="nav-link" href="#academicdetails">Education</a>
</li>
<li class="nav-item">
<a class="nav-link" href="#skills">Skill Set</a>
</li>
<li class="nav-item">
<a class="nav-link" href="#certs">Workshops</a>
</li>
<li class="nav-item">
<a class="nav-link" href="#project">Hobbies</a>
</li>
<li class="nav-item">
<a class="nav-link" href="#project">contact Me</a>
</li>
</ul>
</nav>
<hr>
<h6 class="mt-0">Sravani Bommireddy</h6>
<button type="button" class="btn btn-dark">BSC-MSCs</button>
</div>
<hr>
<h6>Career Objective </h6>
<p>Looking for a challenging career to show the best of my professional ability, skills and techniques to
enhance my knowledge and growth in Electronics and communication industry.</p>
<hr>
<div id = "academicdetails">
<u>
<h6>Academic Details </h6>
</u>
<table class="table table-hover table-white">
<thead>
<tr>
<th scope="col">Qualification</th>
<th scope="col">College/School</th>
<th scope="col">GPA/Percentage</th>
<th scope="col">Passing Year</th>
</tr>
</thead>
<tbody>
<tr>
<th scope="row">BSC-MSCs</th>
<td>St.Ann's College for Women.</td>
<td>6.7</td>
<td>2020</td>
</tr>
<tr>
<th scope="row">InterMediate</th>
<td>Sri Gayatri </td>
<td>80%</td>
<td>2017</td>
</tr>
<tr>
<th scope="row">SSC</th>
<td>St Anns High School</td>
<td>8.3</td>
<td>2015</td>
</tr>
</tbody>
</table>
</div>
<hr>
<div id = "skills">
<u>
<h6> Skill Set (on scale of 5)</h6>
</u>
<ul class="list-group">
<li class="list-group-item d-flex justify-content-between align-items-center">
C-programming
<span class="badge badge-primary badge-pill">4</span>
</li>
<li class="list-group-item d-flex justify-content-between align-items-center">
Java
<span class="badge badge-primary badge-pill">3</span>
</li>
<li class="list-group-item d-flex justify-content-between align-items-center">
Python
<span class="badge badge-primary badge-pill">4.5</span>
</li>
<li class="list-group-item d-flex justify-content-between align-items-center">
HTML
<span class="badge badge-primary badge-pill">4</span>
</li>
</ul>
</ul>
</div>
<hr>
<div id= "worsh">
<u>
<h6> Workshops </h6>
</u>
<p>
<ul>
<li> Presented a project titled Advance Cyber Attacks and its threats 2017.</li>
<li> Participated in a workshop on Appreciation and Applications of Data Science, Hyderabad in
2018.</li>
<li> Participated in a workshop Buisness Data Analysis Management and Innovation, Hyderabad
in 2019.</li>
<li> Participated in a National Colloquium on works of a Giant Statistician Prof.C.R.Rao
AIMSCS, UoH campus, Hyderabad in 2019. </li>
</ul>
</div>
<hr>
<div id="hob">
<u>
<h6>Hobbies</h6>
</u>
<p>
<ul>
<li> Listening to music </li>
<li> Watching movies </li>
<li> Reading books </li>
</ul>
</p>
</div>
<hr>
</nav>
</div>
<div class="card">
<button type="button" class="btn btn-light">Contact Me</button>
<div class="card-body">
<span style="padding-left:200px">
<p>Email : [email protected]
</p>
<span style="padding-left:200px">
<p>
Mobile no :+91 7287035697
</p>
<span style="padding-left:200px">
<p>
LinkedIn Profile :sravanibommireddy
</p>
</div>
</div>
</body>
""",
height=2200,
)
def app():
st.write("""
# S&P 500 Stock Analyzer
Shown below are the **Fundamentals**, **News Sentiment**, **Bollinger Bands** and **Comprehensive Report (Compared with SPY as a whole as benchmark)** of your selected stock!
""")
st.markdown("***")
st.sidebar.header('User Input Parameters')
symbol, start, end = user_input_features()
start = pd.to_datetime(start)
end = pd.to_datetime(end)
#symbol1 = get_symbol(symbol.upper())
#st.subheader(symbol1)
stock = finvizfinance(symbol.lower())
stock_chart = stock.ticker_charts()
st.image(stock_chart)
# Read data
data = yf.download(symbol, start, end, threads=False)
# ## SMA and EMA
#Simple Moving Average
data['SMA'] = talib.SMA(data['Adj Close'], timeperiod=20)
# Exponential Moving Average
data['EMA'] = talib.EMA(data['Adj Close'], timeperiod=20)
# Plot
st.subheader(f"""
Simple Moving Average vs. Exponential Moving Average\n {symbol}
""")
st.line_chart(data[['Adj Close', 'SMA', 'EMA']])
# Bollinger Bands
data['upper_band'], data['middle_band'], data['lower_band'] = talib.BBANDS(
data['Adj Close'], timeperiod=20)
# Plot
st.subheader(f"""
Bollinger Bands\n {symbol}
""")
st.line_chart(
data[['Adj Close', 'upper_band', 'middle_band', 'lower_band']])
# ## MACD (Moving Average Convergence Divergence)
# MACD
data['macd'], data['macdsignal'], data['macdhist'] = talib.MACD(
data['Adj Close'], fastperiod=12, slowperiod=26, signalperiod=9)
# Plot
st.subheader(f"""
Moving Average Convergence Divergence\n {symbol}
""")
st.line_chart(data[['macd', 'macdsignal']])
# ## RSI (Relative Strength Index)
# RSI
data['RSI'] = talib.RSI(data['Adj Close'], timeperiod=14)
# Plot
st.subheader(f"""
Relative Strength Index\n {symbol}
""")
st.line_chart(data['RSI'])
st.markdown("***")
st.subheader("Fundamentals: ")
st.dataframe(get_fundamentals(symbol))
st.markdown("***")
# ## Latest News
st.subheader("Latest News: ")
st.table(get_news(symbol).head(5))
st.markdown("***")
# ## Recent Insider Trades
st.subheader("Recent Insider Trades: ")
st.table(get_insider(symbol).head(5))
st.markdown("***")
st.write("Generating comprehensive stock report...")
st.write("**please wait for some time... **")
st.write(
"This section will compare the historical performance of your selected stock with SPDR S&P 500 Trust ETF (Ticker: SPY) as benchmark."
)
stock_report(symbol)
# ## Stock report
st.subheader(f"""**{symbol} Stock Report**""")
#st.header(symbol + " Stock Report")
HtmlFile = open("report.html", 'r', encoding='utf-8')
source_code = HtmlFile.read()
#print(source_code)
components.html(source_code, height=9000)
st.write(
"Disclaimer: The data are collected from Google, Yahoo Finance and Finviz"
)
#PIL or pillow will be used to perform different image transformation operations
from PIL import Image
#streamlit is the latest webapp framework for data driven apps
import streamlit as st
#Drawable canvas will be used to get the data/image from the canvas
from streamlit_drawable_canvas import st_canvas
#components will be used to render html
import streamlit.components.v1 as components
# Render the h1 block, contained in a frame of size 200xY.
#st.markdown method will be used to get settings from style.css file
st.markdown('<style>' + open('style.css').read() + '</style>', unsafe_allow_html=True)
components.html(f""" <html><meta name="viewport" content="width=device-width, initial-scale=1"><body style ="font-size:30px;color:green;" ><center>Mindreader</center><center>Personal Art Therapist</center></body></html>""", height=111)
#""" ignore below imports used for different experiments """
#import base64
#from io import BytesIO
#from pydrive.auth import GoogleAuth
#from pydrive.drive import GoogleDrive
from google.cloud import storage
from google.oauth2 import service_account
import tempfile
#import os
#import shutil
dirpath = tempfile.mkdtemp()
# ... do stuff with dirpath
