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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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| <a class="link" href="https://www.instagram.com/hidevs_community/" target="__blank">Instagram</a> | |
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| </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() |