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import streamlit as st
import numpy as np
import pickle
import streamlit.components.v1 as components
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
import pandas as pd
import nltk
import re
nltk.download('stopwords')
stemmer = nltk.SnowballStemmer("english")
from nltk.corpus import stopwords
import string
stopword=set(stopwords.words('english'))
# Separate target and feature column in X and y variable
df = pd.read_csv('stress.csv')
# X will be the features
def clean(text):
text = str(text).lower()
text = re.sub('\[.*?\]', '', text)
text = re.sub('https?://\S+|www\.\S+', '', text)
text = re.sub('<.*?>+', '', text)
text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
text = re.sub('\n', '', text)
text = re.sub('\w*\d\w*', '', text)
text = [word for word in text.split(' ') if word not in stopword]
text=" ".join(text)
text = [stemmer.stem(word) for word in text.split(' ')]
text=" ".join(text)
return text
df["text"] = df["text"].apply(clean)
X = np.array(df["text"])
# y will be the target variable
y = np.array(df["label"])
df["text"] = df["text"].apply(clean)
cv = CountVectorizer()
# Load the pickled model
X = cv.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.33,
random_state=42)
# Function for model prediction
def model_prediction(features):
features = cv.transform([features]).toarray()
pickled_model = pickle.load(open('Stress_Detection_BernoulliNB.pkl', 'rb'))
Message = str(list(pickled_model.predict(features)))
return Message
def app_design():
# Add input fields for High, Open, and Low values
image = '36.png' # Load image
st.image(image, use_column_width=True)
st.subheader("Enter the following values:")
text= st.text_input("Enter your message")
# Create a feature list from the user inputs
features = text # add features according to notebook
# Make a prediction when the user clicks the "Predict" button
if st.button('Predict Stress'):
predicted_value = model_prediction(features)
if predicted_value == "['Stress']":
st.success("Your message contains Stress")
elif predicted_value == "['No Stress']":
st.success("Your message doesnot contains Stress")
def about_hidevs():
components.html("""
<div>
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<p class="subtitle">💡 Join us now, and turbocharge your career!</p>
<p class="subtitle"><a class="link" href="https://hidevscommunity.wixsite.com/hidevs" target="__blank">Website</a>
<a class="link" href="https://www.youtube.com/@HidevsCommunity1307/" target="__blank">YouTube</a>
<a class="link" href="https://www.instagram.com/hidevs_community/" target="__blank">Instagram</a>
<a class="link" href="https://medium.com/@hidevscommunity" target="__blank">Medium</a>
<a class="link" href="https://www.linkedin.com/company/hidevs-community/" target="__blank">LinkedIn</a>
<a class="link" href="https://github.com/hidevscommunity" target="__blank">GitHub</a></p>
</div>
""",
height=600)
def main():
# Set the app title and add your website name and logo
st.set_page_config(
page_title="Stress Detection",
page_icon=":chart_with_upwards_trend:",
)
st.title("Welcome to our Stress Detection App!")
app_design()
st.header("About HiDevs Community")
about_hidevs()
if __name__ == '__main__':
main() |