def main():
st.title("Streamlit Forms & Salary Calculator")
menu = ["Home","About"]
choice = st.sidebar.selectbox("Menu",menu)
if choice == "Home":
st.subheader("Forms Tutorial")
# Salary Calculator
# Combine forms + columns
with st.form(key='salaryform'):
col1,col2,col3 = st.beta_columns([3,2,1])
with col1:
amount = st.number_input("Hourly Rate in $")
with col2:
hour_per_week = st.number_input("Hours Per Week",1,120)
with col3:
st.text("Salary")
submit_salary = st.form_submit_button(label='Calculate')
if submit_salary:
with st.beta_expander("Results"):
daily = [amount * 8]
weekly = [amount * hour_per_week]
df = pd.DataFrame({'hourly':amount,'daily':daily,'weekly':weekly})
st.dataframe(df.T)
# Method 1: Context Manager Approach (with)
with st.form(key='form1'):
firstname = st.text_input("Firstname")
lastname = st.text_input("lastname")
dob = st.date_input("Date of Birth")
# Important
submit_button = st.form_submit_button(label='SignUp')
# Results Can be either form or outside
if submit_button:
st.success("Hello {} you ve created an account".format(firstname))
# Method 2:
form2 = st.form(key='form2')
username = form2.text_input("Username")
jobtype = form2.selectbox("Job",["Dev","Data Scientist","Doctor"])
submit_button2 = form2.form_submit_button("Login")
if submit_button2:
st.write(username.upper())
else:
st.subheader("About")
def main():
st.title("Image to LaTeX")
with st.form(key="imputs"):
st.markdown("## Upload an Image")
image = st.file_uploader("", type=["jpg", "png"])
st.form_submit_button(label="Upload")
with st.form(key="outputs"):
st.markdown("## Convert to LaTeX")
st.text("Uploaded image:")
if image is not None:
st.image(image)
inference_button = st.form_submit_button(label="Infer")
if inference_button and image is not None:
with st.spinner("Wait for it..."):
b64_image = base64.b64encode(image.read())
response = requests.post(
API_PREDICT_URL,
json={"image": f"data:image/png;base64,{b64_image.decode()}"})
if response.status_code == 200:
st.markdown("**Inferred LaTex:**")
st.code(response.json()["pred"], language="latex")
st.markdown("**Render Inferred LaTeX:**")
st.latex(response.json()["pred"])
else:
st.error("An error occurred in the model server.")
def test_submit_button_outside_form(self):
"""Test that a submit button is not allowed outside a form."""
with self.assertRaises(StreamlitAPIException) as ctx:
st.form_submit_button()
assert "`st.submit_button()` must be used inside an `st.form()`" in str(
ctx.exception)
def test_submit_button_inside_form(self):
"""Test that a submit button is allowed inside a form."""
with st.form("foo"):
st.form_submit_button()
last_delta = self.get_delta_from_queue()
self.assertEqual("foo", last_delta.new_element.button.form_id)
def main():
menu = ["Enter Details", "Search"]
choice = st.sidebar.selectbox("Menu", menu)
if choice == "Enter Details":
st.subheader("Enter Details")
with st.form(key='my_form1'):
col1, col2 = st.columns(2)
dob = col1.date_input('Enter DOB')
First_name = col1.text_input("First Name", )
Last_name = col1.text_input("Last Name", )
col1.write('Select Skill')
option_1 = col1.checkbox('Java')
option_2 = col1.checkbox('Shell script')
option_3 = col1.checkbox('Python')
option_4 = col1.checkbox('SQL')
submit_button = st.form_submit_button(label='Submit')
if submit_button:
update_df['dob'] = [dob]
update_df['First_name'] = [First_name.strip()]
update_df['Last_name'] = [Last_name.strip()]
update_df['Java'] = [option_1]
update_df['Shell Script'] = [option_2]
update_df['Python'] = [option_3]
update_df['SQL'] = [option_4]
sh = gc.open('prototype')
wks = sh[0]
df = wks.get_as_df()
df = df.append(update_df)
wks.set_dataframe(df, (1, 1))
elif choice == "Search":
st.subheader("Search")
with st.form(key='my_form2'):
col1, col2 = st.columns(2)
First_name = col1.text_input("First Name", )
Last_name = col1.text_input("Last Name", )
submit_button = st.form_submit_button(label='Submit')
if submit_button:
sh = gc.open('prototype')
wks = sh[0]
df = wks.get_as_df()
if (First_name != "") and (Last_name != ""):
filtered_df = df[
(df['First_name'].str.strip() == (First_name.strip()))
& (df['Last_name'].str.strip() == Last_name.strip())]
if (Last_name) == "":
filtered_df = df[(df['First_name'].str.strip() == (
First_name.strip()))]
if (First_name) == "":
filtered_df = df[(df['Last_name'].str.strip() == (
Last_name.strip()))]
st.write(filtered_df.reset_index(drop=True))
def continuar(msg, state):
with st.form(key='continue'):
st.text(msg)
state.uploaded_files.pop(0)
state.opened_file = False
state.checked_file = False
state.processed_file = False
state.selected_option = False
state.appended_file = False
st.form_submit_button(label='Continuar')
st.stop()
def main():
st.title("Lyrica")
def searchlyrics():
song = lyrics(Artist_name, Song_name)
for words in song:
return (words)
def parse_lyrics():
profane_words = "4r5e5h1t,f**k,bustin\',busting,f****d,5hit,a55,anal,anus,ar5e,arrse,arse,ass,ass-f****r,asses,assfucker,assfukka,asshole,assholes,asswhole,a_s_s,b!tch,b00bs,b17ch,b1tch,ballbag,balls,ballsack,bastard,beastial,beastiality,bellend,bestial,bestiality,bi+ch,biatch,bitch,bitcher,bitchers,bitches,bitchin,bitching,bloody,blow job,b*****b,blowjobs,boiolas,bollock,bollok,boner,boob,boobs,booobs,boooobs,booooobs,booooooobs,breasts,buceta,bugger,bum,bunny f****r,butt,butthole,buttmuch,b******g,c0ck,c0cksucker,carpet muncher,cawk,chinkcipa,cl1t,c**t,clitoris,clits,cnut,c**k,c**k-sucker,cockface,cockhead,cockmunch,cockmuncher,cocks,cocksuck, cocksucked, c********r,cocksucking,cocksucks,cocksuka,cocksukka,c*k,cokmuncher,coksucka,coon,cox,crap,cum,cummer,cumming,cums,cumshot,cunilingus,cunillingus,cunnilingus,c**t,cuntlick, cuntlicker, cuntlicking, cunts,cyalis,cyberfuc,cyberfuck, cyberfucked, cyberfucker,cyberfuckerscyberfucking,d1ck,damn,dick,dickhead,d***o,dildos,dink,dinks,dirsa,dlck,dog-f****r,doggin,dogging,donkeyribber,doosh,duche,dyke,e*******e,ejaculated,ejaculates,ejaculating, ejaculatings,e*********n,ejakulate,f u c k,f u c k e r,f4nny,f*g,fagging,faggitt,f****t,faggs,fagot,fagots,fags,fanny,fannyflaps,fannyfucker,fanyy,fatass,fcuk,fcuker,fcuking,feck,fecker,f******g,f*****e,f******o,fingerfuck, fingerfucked, fingerfucker,fingerfuckers,fingerfucking,fingerfucks,fistfuck,fistfucked, fistfucker,fistfuckers, fistfucking, fistfuckings, fistfucks, flangefook,fookefuck,f***a,f****d,f****r,fuckers,fuckhead,fuckheads,f****n,f*****g,f******s,fuckingshitmotherfucker,f****e, f***s,fuckwhit,f*****t,fudge packer,f*********r,fuk,fukerfukker,fukkin,fuks,fukwhit,fukwit,f*x,fux0r,f_u_c_k,g******g,gangbanged, gangbangs, gaylord,gaysex,g****e,God,god-dam,god-damned,goddamn,goddamned,hardcoresex, hell,heshe,hoar,hoare,hoer,h**o,hore,horniest,horny,hotsex,jack-off, jackoff,jap,jerk-off, jism,j*z,jizm, j**z,kawk,knob,knobeadknobed,k*****d,knobhead,knobjocky,knobjokey,kock,kondum,kondums,kum,kummer,kumming,kums,kunilingus,l3i+ch,l3itch,labia,lmfao,lust,lusting,m0f0,m0fo,m45terbate,ma5terb8,ma5terbate,m*******t,master-bate,masterb8,masterbat*,masterbat3,masterbate,masterbation,masterbations,m********e,mo-fo,mof0,mofo,mothafuck,m********a,mothafuckas,mothafuckaz,mothafucked, mothafucker,mothafuckers,mothafuckin,mothafucking, mothafuckings,mothafucks,mother f****r,motherfuck,motherfucked,m**********r,motherfuckers,motherfuckin,motherfucking,motherfuckings,motherfuckka,motherfucks,muff,mutha,muthafecker,muthafuckker,muther,mutherfucker,n1gga,n1gger,nazi,nigg3r,nigg4h,n***a,niggah,n****s,niggaz,nigger,niggers, nob,nob jokey,nobhead,nobjocky,nobjokey,numbnuts,nutsack,orgasim,orgasims, o****m,o*****s, p0rn,pawn,pecker,penis,penisfucker,phonesex,phuck,phuk,phuked,phuking,phukked,phukking,phuks,phuq,pigfucker,pimpis,piss,pissed,pisser,pissers,pisses,pissflaps,pissin, pissing,pissoff,poop,p**n,porno,pornography,pornos,prick,pricks, pron,pube,p***e,pussi,pussies,pussy,pussys, rectum,retard,rimjaw,rimming,s hit,s.o.b.,s****t,schlong,screwing,scroat,scrote,scrotum,s***n,sex,sh!+,sh!t,sh1tshag,shagger,shaggin,shagging,shemale,shi+,shit,shitdick,s***e,shited,shitey,shitfuck,shitfull,s******d,shiting,shitings,shits,s*****d,shitter,shitters, s******g,s*******s,shitty,skank,s**t,s***s,s****a,smut,snatch,son-of-a-bitch,spac,spunk,s_h_i_t,t1tt1e5,t1tties,teets,teez,testical,testicle,tit,titfuck,t**s,titt,tittie5,tittiefucker,titties,tittyfuck,tittywank,titwank,tosser,turd,tw4t,twat,twathead,twatty,twunt,twunter,v14gra,v1gra,v****a,viagra,vulva,w00se,wang,wank,wanker,wanky,whoar,w***e,willies,willy,xrated,xxx"
profane = profane_words.replace(",", " ")
profane = profane.split(" ")
profane = unique.union(set(profane))
stpwrds = stopwords.words("english")
tokens = word_tokenize(searchlyrics())
removing_stopwords = [
words for words in tokens if not words in stpwrds
]
new_stopwords = stopwords.words("english")
new_stopwords = new_stopwords.extend(profane)
removing = [word for word in removing_stopwords if word in profane]
return removing
menu = ["Home", "About"]
choice = st.sidebar.selectbox("Menu", menu)
if choice == "Home":
st.subheader("Forms Tutorial")
with st.form(key="searcher"):
Artist_name = st.text_input("Artist")
Song_name = st.text_input("Song")
Fetch = st.form_submit_button("Fetch Lyrics")
if Fetch:
with st.beta_expander("Lyrics"):
st.write(searchlyrics())
st.success("Lyrics fetched successfully")
with st.form(key="form1"):
# Lyrics = st.text_area("Lyrics", height=300)
lyrica = st.empty()
Analyze = st.form_submit_button("Analyze")
if Analyze:
with st.beta_expander("Keywords"):
st.write(parse_lyrics())
else:
st.subheader("About")
def main():
st.set_page_config(layout='wide')
state = _get_state()
placeholder = st.empty()
if state.degiro is None:
with placeholder.form('2FA'):
username = st.text_input('Username', '')
password = st.text_input('Password', '', type='password')
totp = st.text_input('2FA - Leave empty if not needed', '')
if st.form_submit_button('Submit'):
state.degiro = degiroapi.DeGiro()
try:
state.degiro.login(username, password, totp)
except Exception:
st.warning('Wrong credentials')
state.degiro = None
st.stop()
if state.degiro and state.products is None:
placeholder.empty()
process.get_data(state)
if state.products is not None:
show_page(state)
state.sync()
def create_cross_validation_form(ui_params):
col1, col2, col3 = st.columns(3)
with col1:
st_cv_initial_days = st.number_input("Initial days",
value=730,
min_value=1,
step=1)
with col2:
st_cv_period_days = st.number_input("Period days",
value=180,
min_value=1,
step=1)
with col3:
st_cv_horizon_days = st.number_input("Horizon days",
value=365,
min_value=1,
step=1)
st_validation_metric = st.selectbox("Validation Metric",
options=VALIDATION_METRICS,
index=3)
show_cross_validation = st.form_submit_button(label='Cross-Validate')
# show_cross_validation = st.checkbox("Show Cross-Validation", value=False)
st.caption("This can take some time.")
dic_return = DotDict(initial_days=st_cv_initial_days,
period_days=st_cv_period_days,
horizon_days=st_cv_horizon_days,
validation_metric=st_validation_metric,
cross_validation=show_cross_validation)
return dic_return
def form():
with st.form("my_form"):
st.write("Envie os seguintes dados abaixo")
with col1:
st.write("""
## João da silva
(aposentado/funcionario publico)""")
with col2:
date = st.date_input('Data de nascimento', datetime.date.today())
st.write("## Foto")
uploaded_file_foto = st.file_uploader("Choose a file", key=1)
st.write("## Audio")
uploaded_file_audio = st.file_uploader("Choose a file", key=2)
st.write("## Video")
uploaded_file_video = st.file_uploader("Choose a file", key=3)
# Every form must have a submit button.
submitted = st.form_submit_button("Submit")
if submitted:
st.write("data", date)
st.write(date)
st.balloons()
## enviar para api de autenticação
## enciar para s3 e rodar sagemaker para validação
## usar ec2 para hosting
st.write(" ")
def predict_page(self):
st.write('## Simulator')
st.write("""Predict the outcome or probability of survival by customizing the input data of individuals""")
st.write('')
model_list = os.listdir('../output')
avail_algo = [re.search('(?<=model_).+(?=\.pkl)', m).group(0) for m in model_list]
with st.form(key="predict_form"):
# New Data
col1, col2, col3 = st.columns(3)
pclass = col1.radio("Passage Class", (1, 2, 3), format_func=lambda x: CLASS_NAME[x])
sex = col2.selectbox("Sex", ('male', 'female'), format_func=lambda x: x.title())
age = col3.number_input("Age", step = 5)
new_data = pd.DataFrame({
'Pclass': [pclass],
'Sex': [sex],
'Age': [age]
})
# Prediction Options
col4, col5 = st.columns([2, 1])
algorithm = col4.selectbox('Algorithm', avail_algo, format_func=lambda x: ALGO_NAME[x])
probs = col5.radio('Predict Probability?', (True, False), format_func=lambda x: 'Yes' if x else 'No')
predict = st.form_submit_button(label = 'Predict')
if predict:
pred = self.predict(new_data, algorithm, probs)
if probs:
velocimeter_chart(pred[0])
else:
outcome = 'SURVIVED' if pred == 1 else 'DIED'
st.write(f"""
The model predicted that this individual would have
### {outcome}
in the Titanic tragic accident
""")
def main():
st.title("Titanic: Machine Learning from Disaster")
df = load_data()
target = "Survived"
features = [c for c in df.columns.values if c != target]
with st.beta_expander("About Titanic"):
c1, c2 = st.beta_columns(2)
description = read_markdown_file("pages/titanic.md")
c1.markdown(f"{description}", unsafe_allow_html=True)
c2.image("images/titanic.jpg")
st.header("Data preview")
st.caption(f"Shape of dataset : {df.shape[0]} rows, {df.shape[1]} columns")
st.dataframe(df.describe())
cols_to_style = st.multiselect("Choose columns to apply BG gradient",
features)
st.dataframe(
df.style.background_gradient(subset=cols_to_style, cmap="BuGn"))
st.markdown("---")
st.header("Plot distribution")
col = st.selectbox("Choose a column to display", features)
with_target = st.checkbox("Separate per target ?")
chart = (alt.Chart(df).mark_bar().encode(
alt.X(f"{col}:Q", bin=alt.Bin(maxbins=10)),
alt.Y("count()"),
tooltip=[col, "count()"],
).interactive())
if with_target:
chart = chart.encode(color=f"{target}:N")
st.altair_chart(chart, use_container_width=True)
st.markdown("---")
st.header("Correlation")
fig, ax = plt.subplots()
sns.heatmap(df.corr(), ax=ax)
st.pyplot(fig)
st.markdown("---")
st.header("Classification")
with st.form(key="classify"):
c1, c2 = st.beta_columns(2)
n_estimators = c1.number_input("Choose number of trees:", 1, 1000, 100)
max_depth = c2.number_input("Max depth:", 1, 100, 5)
button_pressed = st.form_submit_button(label="Train model")
if button_pressed:
with st.spinner("Training..."):
clf, confusion_matrix, importance_plot, force_plot = train_rf(
df, n_estimators, max_depth)
st.balloons()
st.pyplot(confusion_matrix)
st.pyplot(importance_plot)
st_shap(force_plot, 400)
def login(state):
with st.sidebar.form(key='my_form'):
user = st.text_input('Utilizador', '')
password = st.text_input('Password', '', type='password')
submit_button = st.form_submit_button(label='Entrar')
if submit_button:
if user == st.secrets['user'] and password == st.secrets['password']:
state.login = True
else:
st.sidebar.warning('Utilizador / Password errados')
st.stop()
def web_visualisation():
submitted = False
while not submitted:
with st.form("my_form"):
st.title("Are you likely to have heart disease?")
age = st.slider("How old are you?", 0, 100)
sex = st.radio("What is your gender?", ["Male", "Female"])
cp = st.radio("What is your chest pain type", [0, 1, 2, 3])
trtbps = st.slider("What is your resting blood pressure (Hg)?", 0,
200)
chol = st.slider("What is your cholestrol level (mg /dl)?", 0, 700)
fbs = st.radio("What is your fasting blood sugar (mg) ?", [0, 1])
restecg = st.slider(
"What is resting electrocardiographic results?", 0, 2)
thalachh = st.slider("What is your maximmum heart rate?", 0, 200)
exng = st.radio("Does your exercise induce anigma?", ["No", "Yes"])
oldpeak = st.slider(
"What is ST depression induced by exercise relative to rest?",
0.00,
7.00,
step=0.05)
slp = st.slider(
"What is the slope of the peak exercise ST segment?", 0, 2)
caa = st.slider(
"What is number of major vessels (0-3) colored by flourosopy?",
0, 3)
thall = st.radio(
"3 = normal; 6 = fixed defect; 7 = reversable defect",
[3, 6, 7])
submitted = st.form_submit_button("Submit")
if sex == 'Female':
sex = 0
sex = 1
if thall == 3:
thall = 1
elif thall == 6:
thall = 2
else:
thall = 3
if exng == "No":
exng = 0
else:
exng = 1
heart_param = np.array([
age, sex, cp, trtbps, chol, fbs, restecg, thalachh, exng, oldpeak,
slp, caa, thall
])
submitted = True
return heart_param
def login(state):
with st.sidebar.form(key='login'):
user = st.text_input('Utilizador', '')
password = st.text_input('Password', '', type='password')
submit_button = st.form_submit_button(label='Entrar')
if submit_button:
if not state.user_credentials.loc[
(state.user_credentials.user == user) &
(state.user_credentials.password == password)].empty:
state.user = user
else:
st.sidebar.warning('Utilizador / Password errados')
def _create_sidebar(self) -> None:
state = self._state
devdf = gui_get_df('device', columns=['namespace'])
if devdf.empty:
st.error('Unable to retrieve any namespace info')
st.stop()
namespaces = [''] + sorted(devdf.namespace.unique().tolist())
if self._state.namespace:
nsidx = namespaces.index(self._state.namespace)
else:
nsidx = 0
url = '&'.join([
f'{get_base_url()}?page=Help&session={get_session_id()}',
'help=yes',
'help_on=Path',
])
display_help_icon(url)
with st.sidebar:
with st.form(key='trace'):
state.namespace = st.selectbox('Namespace',
namespaces,
key='path_namespace',
index=nsidx)
state.source = st.text_input('Source IP',
key='path_source',
value=state.source)
state.dest = st.text_input('Dest IP',
value=state.dest,
key='path_dest')
state.vrf = st.text_input('VRF',
value=state.vrf,
key='path_vrf')
state.start_time = st.text_input('Start Time',
value=state.start_time,
key='path_start_time')
state.end_time = st.text_input('End Time',
value=state.end_time,
key='path_end_time')
_ = st.form_submit_button('Trace', on_click=self._sync_state)
state.show_ifnames = st.checkbox('Show in/out interface names',
value=state.show_ifnames,
key='path_show_ifnames',
on_change=self._sync_state)
_ = st.button('Source <-> Dest',
key='path_swap',
on_click=self._sync_state)
def main():
st.title("Emotion Classifier App")
menu = ["Home", "Monitor", "About"]
choice = st.sidebar.selectbox("Menu", menu)
if choice == "Home":
st.subheader("Home-Emotion In Text")
with st.form(key='emotion_clf_form'):
raw_text = st.text_area("Type Here")
submit_text = st.form_submit_button(label='Submit')
if submit_text:
col1, col2 = st.beta_columns(2)
# Apply Fxn Here
prediction = predict_emotions(raw_text)
probability = get_prediction_proba(raw_text)
#add_prediction_details(raw_text,prediction,np.max(probability))
with col1:
st.success("Original Text")
st.write(raw_text)
st.success("Prediction")
emoji_icon = emotions_emoji_dict[prediction]
st.write("{}:{}".format(prediction, emoji_icon))
st.write("Confidence:{}".format(np.max(probability)))
with col2:
st.success("Prediction Probability")
# st.write(probability)
proba_df = pd.DataFrame(probability, columns=pipe_lr.classes_)
# st.write(proba_df.T)
proba_df_clean = proba_df.T.reset_index()
proba_df_clean.columns = ["emotions", "probability"]
fig = alt.Chart(proba_df_clean).mark_bar().encode(
x='emotions', y='probability', color='emotions')
st.altair_chart(fig, use_container_width=True)
elif choice == "Monitor":
st.subheader("Monitor App")
else:
st.subheader("About")
def delete(st_obj):
with st_obj:
with st.form(key='delete_review'):
st.subheader('Delete review from database')
st.markdown('Copy and paste the ID of the review to be deleted')
update_id = st.text_input('Enter review ID', key='delete')
update_table = st.selectbox('Enter table to be deleted from',['movie-reviews', 'short-film-reviews'])
st.info('This action is non-reversible. Once deleted, review cannot be recovered.')
submit_delete = st.form_submit_button('Confirm deletion of selected review')
if submit_delete:
Database.deleteReview(update_table, update_id)
def editIG(st_obj):
with st_obj:
with st.form(key='update_ig'):
st.subheader('Update Instagram Status')
st.markdown('Copy and paste the ID of the review that you want its IG status to be updated')
update_id = st.text_input('Enter review ID')
update_val = st.selectbox('Uploaded on Instagram?', [True, False])
update_table = st.selectbox('Enter table to be updated',['movie-reviews', 'short-film-reviews'])
submit_ig = st.form_submit_button('Confirm instagram update status')
if submit_ig:
Database.updateIG(update_table, update_id, update_val)
def main():
st.title("Emotion Classifier")
menu = ['Home', 'Monitor', 'About']
choice = st.sidebar.selectbox("Menu", menu)
if choice == "Home":
st.subheader("Home - Emotion in Text")
with st.form(key='emotion_clf_form'):
raw_text = st.text_area("Enter Your Text Here")
submit_text = st.form_submit_button(label='Submit')
if submit_text:
col1, col2 = st.beta_columns(2)
# Apply functions here
prediction = predict_emotions(raw_text)
probability = get_prediction_proba(raw_text)
with col1:
st.success("Original Text")
st.write(raw_text)
st.success("Prediction")
st.write(emotion_emoji_dict[prediction])
st.write(f"Confidence Level: {np.max(probability):.3%}")
with col2:
st.success("Prediction Probability")
# st.write(probability)
df_proba = pd.DataFrame(probability, columns=pipe_lr.classes_)
# Print out a probability table
df_proba_trans = df_proba.T
df_proba_trans.columns = ["Probability"]
st.write(df_proba_trans)
# Transpose it so it is a vertical column
df_proba_clean = df_proba.T.reset_index()
df_proba_clean.columns = ["Emotion", "Probability"]
fig = alt.Chart(df_proba_clean).mark_bar().encode(x="Emotion", y="Probability", color='Emotion')
st.altair_chart(fig, use_container_width=True)
elif choice == "Monitor":
st.subheader("Monitor App")
else:
st.subheader("About")
def login(auth):
with st.beta_expander('Sign in and Logout panel'):
with st.form('login_panel'):
email = st.text_input('Enter email')
password = st.text_input('Enter password')
submit_login = st.form_submit_button('Login to the dashboard')
if submit_login:
login = auth.sign_in_with_email_and_password(email, password)
if (login):
Page.state = True
st.success('Signed in! Logging you in')
if st.button('Log out from current session'):
Page.state = False
def model_page(self):
st.write('## Model Fitting')
st.write("""#### Train the developed machine learning algorithm""")
with st.form(key="model_form"):
algo_options = ['All'] + list(self.algos.keys())
chosen_algo = st.selectbox('Choose algorithm', algo_options, format_func=lambda x: x if x == 'All' else ALGO_NAME[x])
if chosen_algo == 'All':
model_algo = self.algos
else:
model_algo = {key: value for key, value in self.algos.items() if key == chosen_algo}
st.write('Execute model?')
fit = st.form_submit_button('Fit')
if fit:
with st.spinner("Running algorithm ..."):
df_model = self.preprocess()
self.fit_model(df_model, model_algo)
st.write('### Success! Models fitted!')
def main():
if config.ISLAND_URL == None:
st.error("未设置小岛链接")
return
db = InitDB()
st.header("敏感词检测")
with st.form("敏感词检测"):
limit_time = st.date_input("时间限制", help="只会对时间在此之后的帖子进行敏感词检测")
enable_ban_word_match = st.form_submit_button("开始检测")
if enable_ban_word_match:
flag = st.warning("正在获取数据，请稍后......")
GetData(config.ISLAND_URL, db, "all_post_data", limit_time)
flag = st.success("获取数据成功！")
BanWordMatch(db)
def page(kiara: Kiara):
with st.form(key="onboard_corpus"):
st.markdown("## Onboard text corpus")
st.markdown("Select sample data from one publication folder located in kiara_modules.playground/examples/newspaper_corpora/CI_newspaper_subcorpora")
st.markdown("Once you're ready, go to the next step via select box on the left")
alias = st.text_input("Corpus name")
uploaded_files = st.file_uploader("Add files", type = 'txt', accept_multiple_files=True)
alias_valid = check_alias_valid(kiara, alias)
onboard_button = st.form_submit_button(label="Onboard")
if onboard_button and alias_valid and uploaded_files:
onboard_file_bundle(kiara=kiara, uploaded_files=uploaded_files, aliases=[alias])
st.markdown(f"Onboarded corpus: {alias}")
else:
if onboard_button:
if not alias_valid:
st.markdown("Corpus name not valid or already exists. Not onboarding any files...")
else:
st.markdown("Corpus name valid, but no files selected.")
available_tables = []
for item in kiara.data_store.aliases:
md = kiara.data_store.get_metadata_for_id(item)
if md.value_type == "table":
column_names = md.metadata["table"]["metadata_item"]["column_names"]
if "content" in column_names and "file_name" in column_names:
available_tables.append(item)
st.markdown("## Select text corpus")
st.markdown("Select the corpus you want to work.")
selected = st.selectbox("Corpus alias", available_tables)
if selected:
table_value = kiara.data_store.load_value(selected)
table: pa.Table = table_value.get_value_data()
st.dataframe(table.to_pandas())
st.session_state.selected_corpus_name = selected
def page_third():
st.title("Retrieve Reports")
with st.form(key='retrieve'):
topic = st.text_input(label='Enter Topic')
username = st.text_input("Enter username")
password = st.text_input("Enter password", type="password")
submitted3 = st.form_submit_button('Retrieve')
if submitted3:
usrPass = username + ":" + password
b64Val = base64.b64encode(usrPass.encode()).decode()
headers = {"Authorization": "Basic %s" % b64Val}
res = requests.get(f"http://web:8000/summaries/getReports?topic=" +
str(topic),
headers=headers)
processed_reports = res.json()
for reportId in processed_reports.keys():
report_name = processed_reports[reportId]
st.markdown(get_report_download_link(reportId, report_name),
unsafe_allow_html=True)
def forms_demo():
st.image(
"https://emojipedia-us.s3.dualstack.us-west-1.amazonaws.com/thumbs/240/apple/271/crystal-ball_1f52e.png",
width=100,
)
st.write("""
# Try out `st.form`!
We built a super-secret algorithm to recommend a Streamlit app or component based on your personality. The problem: It takes a long time to run. That's why we use a form to bundle all the input widgets 👇
""")
st.write("")
with st.form(key="recommender"):
st.write("Click **Submit** to get your recommendation!")
st.selectbox(
"Your favorite streamlit call",
["st.form", "st.balloons 🎈 ", "st.form_submit_button", "st.write"],
)
st.text_input("Your favorite thing to build streamlit apps for")
st.slider("How excited you are about forms", 0, 11, 10)
submitted = st.form_submit_button()
st.write("")
if submitted:
with st.spinner("🤓 Crunching numbers..."):
time.sleep(2)
use_component = random.choice([True, False])
if use_component:
desc = "a component"
name, author, url = random.choice(components)
else:
desc = "an app"
name, author, url = random.choice(apps)
st.success(
f"☘️ The algorithm recommended {desc} to you: **[{name}]({url}) by {author}** (find more cool apps & components in [our gallery](https://streamlit.io/gallery)!)"
)
st.info(
"💡 With `st.form`, this app (and our complex algorithm!) only reruns when you hit the submit button, not at each widget interaction. [Check out the blog post to learn how it works!](https://blog.streamlit.io/introducing-submit-button-and-forms/)"
)
def email():
with st.form(key='my_form'):
st.header("Want to be notified of new features?")
col1, col2 = st.beta_columns([5, 1])
regex = '^(\w|\.|\_|\-)+[@](\w|\_|\-|\.)+[.]\w{2,3}$'
with col1:
text_input = st.text_input(
'Subscribe to get the latest news and updates.',
'Email Address')
with col2:
st.header('')
submit_button = st.form_submit_button(label="Subscribe")
if submit_button and re.search(regex, text_input):
st.info("You've been subscribed successfully!")
st.balloons()
sendmail(text_input)
elif submit_button:
st.error("Invalid Email")
def create_account(dbx, state):
with st.sidebar.form(key='create_account'):
user = st.text_input('Novo Utilizador', '')
password = st.text_input('Password', '', type='password')
submit_button = st.form_submit_button(label='Criar Conta')
if submit_button:
if not password:
st.sidebar.warning('Por favor escolha uma password')
st.stop()
if any(state.user_credentials.user == user):
st.sidebar.warning('Utilizador já registado')
st.stop()
else:
st.sidebar.warning('Criando Conta...')
state.user_credentials = state.user_credentials.append(
{'user': user, 'password': password},
ignore_index=True).sort_values(by='user', axis=0)
db.upload_dataframe(
dbx, APP, state.user_credentials, CREDS)
state.user = user
def get_sp_data(form_key, data_model, st_asset, submit_label = 'Submit',
do_cache = False, **kwargs):
''' Create a form in ST using streamlit_pydantic and
return the resulting data object in dictionary form
'''
if do_cache:
cache_key = f'{form_key}_cache'
with st_asset.form(key = form_key):
st.markdown(f'#### {form_key} params')
input_data = sp.pydantic_input(key = form_key, model = data_model, **kwargs)
submitted = st.form_submit_button(label = submit_label if submit_label else 'Submit')
if submitted:
data = input_data
st.session_state[cache_key] = data
else:
data = st.session_state[cache_key] if cache_key in st.session_state else None
else:
with st_asset:
data = sp.pydantic_form(key = form_key, model = data_model,
submit_label = submit_label, **kwargs)
return data
def api_poc():
st.subheader("POC for connecting catalog and API")
with st.form(key="catalog_search"):
query = st.text_input(label="Search the catalog", value="data")
submit_button = st.form_submit_button(label="Submit catalog search")
catalog_data = get_catalog_results(query)
st.subheader("Results from the catalog are:")
st.write(catalog_data)
st.subheader("Results from the API")
api_keyword_data = requests.get(
f"http://localhost:8000/keyword?q={query}").json()
api_keyword_data_df = pd.DataFrame(api_keyword_data)
st.write(api_keyword_data_df)
st.subheader("Results from title similiarity")
title_query = " ".join(catalog_data["title"])
api_similarity_data = requests.get(
f"http://localhost:8000/titlesimilarity?q={title_query}").json()
st.write(api_similarity_data)
