def cjson(screen, path):
curses.curs_set(0)
color()
header(screen)
footer(screen)
div, txt = body(screen)
data = get_json(path)
eventloop(screen, div, txt, data)
def about():
layout = html.Div([
navbar(),
html.Br(),
content,
html.Br(),
footer()
])
return layout
def product():
layout = html.Div([
nav,
html.Br(),
survey,
res,
html.Br(),
footer(),
])
return layout
print("<h2>No Test Available for this subject</h2>")
else:
print("""
<h2 class="text-center">Test Available Here </h2>
<table width='100%' border=2 cellpadding=10>
<tr>
<th>Test ID</th>
<th>Subject</th>
<th>Grade</th>
<th>Start Test</th>
""")
for i in range(len(test)):
print("""
<tr>
<td> {} </td>
<td> {} </td>
<td> {} </td>
<td> <a href='testview.py?test_id={}&id={}&name={}' class="btn btn-danger">Start Test</a> </td>
""".format(test[i][0], test[i][2], test[i][3], test[i][0], id, name))
print("</table>")
footer.footer()
print("""
<script src="https://code.jquery.com/jquery-3.4.1.slim.min.js" integrity="sha384-J6qa4849blE2+poT4WnyKhv5vZF5SrPo0iEjwBvKU7imGFAV0wwj1yYfoRSJoZ+n" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/umd/popper.min.js" integrity="sha384-Q6E9RHvbIyZFJoft+2mJbHaEWldlvI9IOYy5n3zV9zzTtmI3UksdQRVvoxMfooAo" crossorigin="anonymous"></script>
<script src="https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/js/bootstrap.min.js" integrity="sha384-wfSDF2E50Y2D1uUdj0O3uMBJnjuUD4Ih7YwaYd1iqfktj0Uod8GCExl3Og8ifwB6" crossorigin="anonymous"></script>
</body>
</html>
""")
future = m.make_future_dataframe(periods=period)
forecast = m.predict(future)
#end loading animation
loading_text.text('')
loading.empty()
if n_years == 1:
st.subheader(f'Predictions for the Next Year')
elif n_years >= 1:
st.subheader(f'Predictions for the Next {n_years} years')
fig1 = plot_plotly(m, forecast)
st.plotly_chart(fig1, use_container_width=True)
# Show and plot forecast
st.subheader('Regression Data on Recent Trading Days')
st.write(forecast.tail())
#Plot open/closes
plot_raw_data(data)
st.subheader("Forecast Components")
fig2 = m.plot_components(forecast)
st.write(fig2)
st.subheader('Raw Data on Recent Trading Days')
st.write(data.tail())
#footer
footer()
