def main():
# setup a title
st.title('Select a tool')
# Once we have the dependencies, add a selector for the app mode on the sidebar.
model_option = st.selectbox(
'',
(
'Daily geoactivity tool',\
'GPS TEC tool',\
'AMPERE FACs forecast',\
'Substorm onset forecast'
)
)
# we'll need a session state to switch between dates
# basically the prev day and next day buttons!
# session state details for the geo_activity_tool page
# session state details for the geo_activity_tool page
geo_all_param_list = [ "DSCOVER", "OMNI", "STORM",\
"SUBSTORM", "SUPERDARN" ]
nhours_plot_default = 0
ndays_plot_default = 1
inp_start_date = datetime.date(2018, 1, 2)
inp_start_time = datetime.time(0, 0)
# session state details for the geo_activity_tool page
# session state details for the geo_activity_tool page
# session state details for the sson_model page
# session state details for the sson_model page
data_obj = dwnld_sw_imf_rt.DwnldRTSW()
url_data = data_obj.dwnld_file()
if url_data is not None:
data_obj.read_url_data(url_data)
# repeat the operations we do with sson_model calc
sw_imf_df = data_obj.read_stored_data()
sw_imf_df.set_index('propagated_time_tag', inplace=True)
sw_imf_df = sw_imf_df.resample('1min').median()
# linearly interpolate data
sw_imf_df.interpolate(method='linear', axis=0, inplace=True)
omn_end_time = sw_imf_df.index.max()
# session state details for the sson_model page
# session state details for the sson_model page
# common session state details for the all pages
state = session_state.get(\
plot_start_date=inp_start_date,\
plot_start_time=inp_start_time,\
plot_param_list=geo_all_param_list,\
plot_nhours_plot=nhours_plot_default,\
plot_ndays_plot=ndays_plot_default,\
date_sson_hist_plot=omn_end_time
)
if model_option == 'Daily geoactivity tool':
geo_activity_page(
state,\
local_data_store="./geo_tool/data/sqlite3/",\
plot_style="classic",\
inp_start_date=inp_start_date,\
inp_start_time=inp_start_time,\
all_param_list=geo_all_param_list,\
nhours_plot_default=nhours_plot_default,\
ndays_plot_default=ndays_plot_default
)
elif model_option == 'GPS TEC tool':
gps_tec_page()
elif model_option == 'AMPERE FACs forecast':
fac_model_page()
else:
ss_onset_page(state)
import os
import uuid
import numpy as np
import streamlit as st
from additional_utils import load_augmentations_config
from augmentation import apply_changes, dict_update, select_next_aug
from code_generator import build_code
from files_uploaders import image_uploader
from layout import return_layout
from session_state import get
from state_dict import aug_dict, clear_dict, oneof_dict, state_dict
from string_builders import build_string
session_state = get(id=uuid.uuid4())
root_path = os.path.dirname(os.path.abspath(__file__))
config_path = os.path.join(root_path, 'augmentation.json')
clear_dict(session_state)
image_uploader()
st.text('Upload an image, then select transformation from the \
list.\nTo apply OneOf use OneOf at the beginning and StopOneOf\
to close it.')
if 'image' in list(state_dict.keys()): # noqa: C901
st.image(state_dict['image'])
image_params = {
'width': state_dict['image_array'].shape[1],
'height': state_dict['image_array'].shape[0],
}
remove_bom=False,
normalization="NFC",
)
stream_iterator = iter(stream)
while stream_iterator:
try:
line = next(stream_iterator)
out_file.write(line)
except StopIteration:
break
output_file.close()
if __name__ == "__main__":
# Session initialization
session = get(count=1, expected="", successes=0)
with st.spinner("Loading neural network..."):
mnist = Net()
mnist.load_state_dict(
torch.load("models/mnist_cnn.pt", map_location="cpu"))
# Read text file
input_file = open("texts.json", "r")
output_file = open("texts_unicode.json", "w")
fix_file_encoding(input_file, output_file)
with open("texts_unicode.json") as json_file:
texts = json.load(json_file)
languages = texts["languages"]
# language choice
language = st.sidebar.radio(" ", list(languages.keys()))
# Run the predict page
st.title('Machine Learning Web App - Image Captioning')
st.header("Final Project - Advanced Statistics Topics: ML and DS")
st.header(
"Click [here](https://github.com/juanse1608/AST-ImageCaptioning/blob/main/README.md) to know more about the project!"
)
st.write('''Upload a photo and see the predicted caption for it''')
# File uploader allows user to add their own image
uploaded_file = st.file_uploader(label="Upload Image",
type=["png", "jpeg", "jpg"])
# Setup session state to remember state of app so refresh isn't always needed
# See: https://discuss.streamlit.io/t/the-button-inside-a-button-seems-to-reset-the-whole-app-why/1051/11
session_state = session_state.get(pred_button=False)
# Create logic for app flow
if not uploaded_file:
st.warning("Please upload an image.")
session_state.pred_button = False
st.stop()
else:
session_state.uploaded_image = uploaded_file.read()
st.image(session_state.uploaded_image, use_column_width=True)
value = st.selectbox(
"Select Prediction Type", ("Argmax", "Random"),
help='''__Argmax__ picks the value/token with the highest probability.
__Random__ picks the value/token randomly using the distribution of the predictions.'''
)
import streamlit as st
import home
import annotator_tool
import session_state
PAGES = {"Home": home, "Annotation tool": annotator_tool}
session = session_state.get(message_value=None)
def main():
st.sidebar.title('Navigation')
selection = st.sidebar.radio("Go to", list(PAGES.keys()))
page = PAGES[selection]
with st.spinner(f'Loading {selection} ...'):
if page == annotator_tool:
page.main(session)
else:
page.main()
st.sidebar.info('This tool is an example.')
if __name__ == "__main__":
main()
import streamlit as st
import pandas as pd
import random
import sys
import urllib.parse
from package import visuals
from package.reports import reports
sys.path.append('..')
import session_state
from shared import load_explanation_group
state = session_state.get(sample_id=0)
def download_report_url(report_html, report_name):
report_html = urllib.parse.quote(report_html, safe='')
report_name = f'{report_name}.html'
href = f'<a href="data:text/html,{report_html}" download="{report_name}">Download Report</a>'
st.markdown(href, unsafe_allow_html=True)
def show(explanation_group_path):
explanation_group = load_explanation_group(explanation_group_path)
sample_id_placeholder = st.sidebar.empty()
state.sample_id = sample_id_placeholder.text_input(
label='Select individual (by ID):',
value=state.sample_id
import streamlit as st
import requests
import time
import subprocess
import session_state
current_session = session_state.get(load=False)
st.title('NYC Taxi Demand Prediction')
st.sidebar.subheader("Predict Option")
month = st.sidebar.number_input('Month', min_value=1, max_value=12, value=1)
day = st.sidebar.number_input('Day', min_value=1, max_value=31, value=14)
weekday = st.sidebar.number_input('weekday', min_value=0, max_value=6, value=2)
hour = st.sidebar.number_input('Hour', min_value=0, max_value=23, value=17)
is_weekend = st.sidebar.number_input('Is Weekend',
min_value=0,
max_value=1,
value=0)
lag_1h_cnt = st.sidebar.number_input('lag_1h_cnt', min_value=0, value=1103)
lag_1d_cnt = st.sidebar.number_input('lag_1d_cnt', min_value=0, value=1189)
lag_7d_cnt = st.sidebar.number_input('lag_7d_cnt', min_value=0, value=1730)
lag_14d_cnt = st.sidebar.number_input('lag_14d_cnt', min_value=0, value=1800)
avg_14d_cnt = st.sidebar.number_input('avg_14d_cnt',
min_value=0.0,
value=969.17)
avg_21d_cnt = st.sidebar.number_input('avg_21d_cnt',
min_value=0.0,
value=879.14)
std_14d_cnt = st.sidebar.number_input('std_14d_cnt', min_value=0, value=507)