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import streamlit as st
import numpy as np
import pandas as pd
import json
import base64
import uuid
from pandas import DataFrame
import time
import re
def download_button(object_to_download, download_filename, button_text):
if isinstance(object_to_download, bytes):
pass
elif isinstance(object_to_download, pd.DataFrame):
object_to_download = object_to_download.to_csv(index=False)
# Try JSON encode for everything else
else:
object_to_download = json.dumps(object_to_download)
try:
# some strings <-> bytes conversions necessary here
b64 = base64.b64encode(object_to_download.encode()).decode()
except AttributeError as e:
b64 = base64.b64encode(object_to_download).decode()
button_uuid = str(uuid.uuid4()).replace("-", "")
button_id = re.sub("\d+", "", button_uuid)
custom_css = f"""
<style>
#{button_id} {{
display: inline-flex;
align-items: center;
justify-content: center;
background-color: rgb(255, 255, 255);
color: rgb(38, 39, 48);
padding: .25rem .75rem;
position: relative;
text-decoration: none;
border-radius: 4px;
border-width: 1px;
border-style: solid;
border-color: rgb(230, 234, 241);
border-image: initial;
}}
#{button_id}:hover {{
border-color: rgb(246, 51, 102);
color: rgb(246, 51, 102);
}}
#{button_id}:active {{
box-shadow: none;
background-color: rgb(246, 51, 102);
color: white;
}}
</style> """
dl_link = (
custom_css
+ f'<a download="{download_filename}" id="{button_id}" href="data:file/txt;base64,{b64}">{button_text}</a><br><br>'
)
# dl_link = f'<a download="{download_filename}" id="{button_id}" href="data:file/txt;base64,{b64}"><input type="button" kind="primary" value="{button_text}"></a><br></br>'
st.markdown(dl_link, unsafe_allow_html=True)
class c_model:
def __init__(self):
# st.write('my model')
pass
@st.cache
def load_model(self, name_or_path):
time.sleep(3)
return None
def predict(self, texts):
return np.random.randint(2), np.random.rand()
st.title('Sentiment Analysis')
# Load classification model
with st.spinner('Loading classification model...'):
from transformers import pipeline
checkpoint = "amir7d0/distilbert-base-uncased-finetuned-amazon-reviews"
checkpoint = "/home/v4vendetta/Documents/bert-fa-base-uncased-sentiment-digikala"
classifier = pipeline("text-classification", model=checkpoint)
tab1, tab2 = st.tabs(["Single Comment", "Multiple Comment"])
with tab1:
st.subheader('Single comment classification')
text_input = st.text_area(label='Paste your text below (max 256 words)',
value='Hiiiiiiiii')
MAX_WORDS = 256
res = len(re.findall(r"\w+", text_input))
if res > MAX_WORDS:
st.warning(
"⚠️ Your text contains "
+ str(res)
+ " words."
+ " Only the first 256 words will be reviewed! 😊"
)
text_input = text_input[:MAX_WORDS]
submit_button = st.button(label='Submit comment')
if submit_button:
with st.spinner('Predicting ...'):
start_time = time.time()
time.sleep(2)
preds = classifier([text_input])[0]
end_time = time.time()
p_time = round(end_time-start_time, 2)
st.success(f'Prediction finished in {p_time}s!')
st.write(f'Label: {preds["label"]}, with certainty: {preds["score"]}')
with tab2:
st.subheader('Multiple comment classification')
file_input = st.file_uploader(label='Choose a file:', type='csv')
if file_input:
try:
df = pd.read_csv(file_input)
texts = df['text'].to_list()
except:
st.write('Bad File Error...')
st.write(f"First 5 rows of {file_input.name} texts")
st.write(texts[:5])
submit_button = st.button(label='Submit file')
if submit_button:
with st.spinner('Predicting ...'):
start_time = time.time()
time.sleep(2)
preds = classifier(texts)
end_time = time.time()
p_time = round(end_time-start_time, 2)
st.success(f'Prediction finished in {p_time}s!')
for text, pred in zip(texts, preds):
pred['text'] = text
c1, c2 = st.columns([3, 1])
with c1:
st.subheader("🎈 Check & download results")
with c2:
CSVButton2 = download_button(preds, "sentiment-analysis-preds.csv", "📥 Download (.csv)")
st.header("")
df = pd.DataFrame(preds, columns=['text', 'label', 'score'])
import seaborn as sns
# Add styling
cmGreen = sns.light_palette("green", as_cmap=True)
cmRed = sns.light_palette("red", as_cmap=True)
df = df.style.background_gradient(
cmap=cmGreen,
subset=["score"],
)
st.table(df) |