Data Applications
Data Applications Applications ¶
- CRUD Application
import streamlit as st
import pandas as pd
import base64
from PIL import Image
import json
import urllib.request
# Database Creation & Connection
import sqlite3
from sqlite3 import Connection
import streamlit as st
import hashlib
URI_SQLITE_DB = "Users.db"
html_temp = """
<body style="background-color:red;">
<div style="background-color:tomato;padding:10px">
<h2 style="color:white;text-align:center;">Simple Login Application Code</h2>
</div>
</body>
"""
st.set_page_config(
page_title = 'Apps',
page_icon = "✌",
layout = "centered",
initial_sidebar_state = "expanded"
)
def make_hashes(password):
return hashlib.sha256(str.encode(password)).hexdigest()
def check_hashes(password,hashed_text):
if make_hashes(password) == hashed_text:
return hashed_text
return False
# """
# check_same_thread = False is added to avoid same thread issue
# """
conn = sqlite3.connect(URI_SQLITE_DB, check_same_thread=False)
c = conn.cursor()
@st.cache(hash_funcs={Connection: id})
def get_connection(path: str):
"""Put the connection in cache to reuse if path does not change between Streamlit reruns.
NB : https://stackoverflow.com/questions/48218065/programmingerror-sqlite-objects-created-in-a-thread-can-only-be-used-in-that-sa
"""
return sqlite3.connect(path, check_same_thread=False)
def create_usertable(conn: Connection):
c.execute('CREATE TABLE IF NOT EXISTS userstable(username TEXT PRIMARY KEY,password TEXT)')
def add_userdata(conn: Connection,username,password):
c.execute('INSERT INTO userstable(username,password) VALUES (?,?)',(username,password))
conn.commit()
def login_user(conn: Connection,username,password):
c.execute('SELECT * FROM userstable WHERE username =? AND password = ?',(username,password))
data = c.fetchall()
return data
def main():
"""Simple Login Application"""
st.markdown(html_temp, unsafe_allow_html=True)
menu = ["Home","Login","SignUp","Others","About"]
st.markdown(
"""
<style>
.sidebar .sidebar-content {
# background-image: linear-gradient(#264D59, #43978D, #F9E07F, #F9AD6A, #D46C4E);
background-image: linear-gradient(to right top, #ebe9f3, #b7bdcc, #8094a5, #496e7c, #0c4950);
color: blue;
}
</style>
""",
unsafe_allow_html=True,
)
choice = st.sidebar.selectbox("Menu", menu)
if choice == "Home":
st.subheader("Home Page:")
elif choice == "Login":
st.subheader("**Please Login to the page !**")
username = st.sidebar.text_input("Username","")
password = st.sidebar.text_input("Password",type='password')
if st.sidebar.checkbox("Login"):
hashed_pswd = make_hashes(password)
result = login_user(conn,username,check_hashes(password,hashed_pswd))
if result:
st.success("Logged In as {0!s}".format(username))
else:
st.warning("Incorrect Adhar/Passport/VoterID/Password")
elif choice == "SignUp":
st.subheader("Create New Account")
new_user = st.text_input("Username","")
new_password = st.text_input("Password",type='password')
if st.button("Signup"):
create_usertable(conn)
add_userdata(conn,new_user,make_hashes(new_password))
st.success("Account created successfully. Please Login to application.")
elif choice == "Others":
st.info("Please visit to nearest adhar or passport or voterid location to get any of the ID.")
elif choice == "About":
st.sidebar.title("App Version details")
st.sidebar.info("**App version 1.0**")
if __name__ == "__main__":
main()
- PencilSketcher App
import streamlit as st
import numpy as np
from PIL import Image
import cv2
st.set_option('deprecation.showfileUploaderEncoding', False)
def dodgeV2(x, y):
return cv2.divide(x, 255 - y, scale=256)
def pencilsketch(inp_img):
img_gray = cv2.cvtColor(inp_img, cv2.COLOR_BGR2GRAY)
img_invert = cv2.bitwise_not(img_gray)
img_smoothing = cv2.GaussianBlur(img_invert, (21, 21),sigmaX=0, sigmaY=0)
final_img = dodgeV2(img_gray, img_smoothing)
return(final_img)
st.title("PencilSketcher App")
st.write("This Web App is to help convert your photos to realistic Pencil Sketches")
file_image = st.sidebar.file_uploader("Upload your Photos", type=['jpeg','jpg','png'])
if file_image is None:
st.write("You haven't uploaded any image file")
else:
input_img = Image.open(file_image)
final_sketch = pencilsketch(np.array(input_img))
st.write("**Input Photo**")
st.image(input_img, use_column_width=True)
st.write("**Output Pencil Sketch**")
st.image(final_sketch, use_column_width=True)
- IrisPrediction App
import streamlit as st
import pandas as pd
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
import urllib.request
from PIL import Image
html_temp = """
<body style="background-color:red;">
<div style="background-color:tomato;padding:10px">
<h2 style="color:white;text-align:center;"> Classifier(SL) - Iris Flower Prediction App</h2>
</div>
</body>
"""
st.beta_set_page_config(
page_title = 'Iris',
page_icon = "🌺",
layout = "centered",
initial_sidebar_state = "expanded"
)
def inputFeatures():
sepal_length = st.sidebar.slider('Sepal length', 4.3, 7.9, 5.4)
sepal_width = st.sidebar.slider('Sepal width', 2.0, 4.4, 3.4)
petal_length = st.sidebar.slider('Petal length', 1.0, 6.9, 1.3)
petal_width = st.sidebar.slider('Petal width', 0.1, 2.5, 0.2)
data = {'sepal_length': sepal_length,
'sepal_width': sepal_width,
'petal_length': petal_length,
'petal_width': petal_width}
features = pd.DataFrame(data, index=[0])
return features
def main():
st.markdown(html_temp, unsafe_allow_html=True)
st.sidebar.info("""
## Packages:
- Streamlit, pandas & sklearn
- How to install?
> 1. **pip install streamlit**
> 2. **pip install pandas**
> 3. **pip install scikit-learn**
""")
st.sidebar.header('Input Features')
df = inputFeatures()
st.sidebar.info("#### Iris Flower species : setosa, versicolor, virginica")
img = Image.open(urllib.request.urlopen("https://raw.githubusercontent.com/SurendraRedd/MachineLearningCode/master/Iris_Species.png")) # Opens the image from the url
st.sidebar.image(img, width=300, caption="")
st.sidebar.info("#### Features : setosa, versicolor, virginica")
img = Image.open(urllib.request.urlopen("https://raw.githubusercontent.com/SurendraRedd/MachineLearningCode/master/features.png")) # Opens the image from the url
st.sidebar.image(img, width=400, caption="")
st.subheader('Input Features')
st.write(df)
iris = datasets.load_iris()
X = iris.data
Y = iris.target
clf = RandomForestClassifier()
clf.fit(X, Y)
prediction = clf.predict(df)
prediction_proba = clf.predict_proba(df)
st.subheader('labels & index number')
st.write(iris.target_names)
st.subheader('Prediction')
st.write(iris.target_names[prediction])
#st.write(prediction)
st.subheader('Prediction Probability')
st.write(prediction_proba)
if __name__ == '__main__':
main()