def main(): state = _get_state() stt.set_theme({'primary': '#1b3388'}) state.newsapi = NewsApiClient(api_key='68353e14ce514929ac111b8b0f24556e') #state.model = Summarizer() pages = { "Login": page_login, "Home": page_home, "Signup": page_signup, } st.sidebar.title(":newspaper: SummarizeR") page = st.sidebar.radio("Select your page", tuple(pages.keys())) # Display the selected page with the session state pages[page](state) # Mandatory to avoid rollbacks with widgets, must be called at the end of your app state.sync()
from sklearn.linear_model import SGDClassifier from xgboost import XGBClassifier from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score import datetime import random from sklearn import preprocessing from sklearn.metrics import balanced_accuracy_score import streamlit_theme as stt import streamlit.components.v1 as components from datetime import date random.seed(10) stt.set_theme({'primary': '#1b3388'}) st.title("Cardiovascular Disease Alert") st.write("Created by Barış Can Tayiz") components.html(""" <div style="background-color:black;height:10px;border-radius:10px;margin-bottom:0px;"> </div><hr>""") st.header("Variables") st.write("""* Age | Objective Feature | age | int (days) \n * Height | Objective Feature | height | int (cm) | \n * Weight | Objective Feature | weight | float (kg) | \n * Gender | Objective Feature | gender | categorical code | \n * Systolic blood pressure | Examination Feature | ap_hi | int | \n * Diastolic blood pressure | Examination Feature | ap_lo | int | \n
import streamlit as st from PIL import Image import base64 import cv2 import numpy as np from keras.models import model_from_json import imutils import urllib.request from sklearn.preprocessing import LabelEncoder from IPython.display import Image as IPythonImage from imageai.Detection.Custom import CustomObjectDetection from tempfile import NamedTemporaryFile import streamlit_theme as stt stt.set_theme({'primary': '#262730', 'textColor': '#FFFFFF'}) #main_bg = "background.jpg" main_bg = 'https://previews.123rf.com/images/eric4094/eric40940903/eric4094090300005/4570324-abstract-design-yellow-colour-background.jpg' main_bg_ext = "jpg" weburl = "https://capstoneprojectmksk.s3.amazonaws.com/detection_model-ex-015--loss-0006.450.h5" filename = weburl.split('/')[-1] urllib.request.urlretrieve(weburl, filename) def detector_model(): #model_path = 'model/detection_model-ex-005--loss-0003.767.h5' model_path = filename json_path = 'model/detection_config.json' detector = CustomObjectDetection() detector.setModelTypeAsYOLOv3()
import os import pickle import warnings import altair as alt import streamlit as st import streamlit_theme as stt stt.set_theme({ 'primary': '#00cc99', }) SAMPLES_TO_DISPALY = 20 X_LIM_MIN = -20 X_LIM_MAX = 20 Y_LIM_MIN = -20 Y_LIM_MAX = 22 seed = 42 warnings.filterwarnings(action="ignore") LABELS = [ "ADJ", "ADP", "ADV", "AUX", "CCONJ", "DET", "INTJ", "NOUN", "NUM", "PART", "PRON", "PROPN", "PUNCT", "SCONJ", "SYM", "VERB" ] def _set_block_container_style( max_width: int = 1200, max_width_100_percent: bool = False, padding_top: int = 0, padding_right: int = 1, padding_left: int = 1,
import streamlit as st import streamlit_theme as stt stt.set_theme({'primary': '#f63366'}) import random import math import statistics from scipy.stats import norm import numpy as np import re from testcode import * import pandas as pd from wellrng import random as pr PAGES = [ "Runs Test Calculator", "Test for Random Number Generator", "Ranji Trophy Data", "Gold Prices", "Air Quality Index Data", "Runs Test Exception" ] #runs test for binary sequence #text templates html_temp = """ <div style="background-color:black;padding:10px"> <i><b><u><h1 style="color:{};text-align:center;">{}</h1></u></b></i> </div> """ html_temp1 = """ <div style="background-color:black;padding:10px">
body { background-image: url("data:image/png;base64,%s"); background-size: cover; } </style> ''' % bin_str st.markdown(page_bg_img, unsafe_allow_html=True) return def local_css(file_name): with open(file_name) as f: st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True) local_css("style.css") set_png_as_page_bg('bg2.png') stt.set_theme({'primary': '#F794B9'}) st.markdown("<h1 style='text-align: center;'> FNA Result Analyser</h1>", unsafe_allow_html=True) st.markdown("<p style='text-align: center;'> An Accurate Tool for Breast Cancer Prediction </p>", unsafe_allow_html=True) col1,col2,col3 = st.beta_columns(3) with col1: area_se = st.number_input('Enter Area Standard Error') with col2: area_mean = st.number_input('Enter Area Mean') with col3: concavity_mean = st.number_input('Enter Concavity Mean') col7,col8,col9 = st.beta_columns(3) with col7:
import streamlit as st import numpy as np import pandas as pd import pydeck as pdk import streamlit_theme as stt from PIL import Image import urllib.request import plotly.express as px from plotly.subplots import make_subplots import plotly.graph_objects as go df = pd.read_csv("https://raw.githubusercontent.com/chrisbaugh-user/USWTDB/master/uswtdb_v3_1_20200717.csv") stt.set_theme({'primary': '#064658'}) sidebar_selector = st.sidebar.selectbox('Select Category:', ('Project Information', 'Deep Dive', 'Wind Turbine Detailed Aggregation', 'US Turbine Map')) def get_cp_agg(years, slider_choice): cp_df = df[(df['p_year'] >= years[0]) & (df['p_year'] <= years[1])] cp_df['capacity_MW'] = cp_df['t_cap']/1000 if slider_choice == 't_county': mw_cap = cp_df.groupby([slider_choice, 't_state'])[['capacity_MW']].sum() else: mw_cap = cp_df.groupby(slider_choice)[['capacity_MW']].sum() mw_cap.sort_values(by='capacity_MW', inplace=True, ascending=False) mw_cap['Capacity CP'] = round((mw_cap['capacity_MW'] / mw_cap['capacity_MW'].sum()) * 100, 2) mw_cap['Capacity CP'] = mw_cap['Capacity CP'].astype(int) mw_cap['capacity_MW'] = mw_cap['capacity_MW'].astype(int)