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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        <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()