import os
import pandas as pd
import numpy as np
import streamlit as st
import re
import pickle
import nltk
import string
import contractions

from nltk.corpus import stopwords

nltk.download("stopwords")
sw_list = stopwords.words('english')

def remove_tags(text):
    return re.sub(re.compile('<.*?>'), '', text)

def lwr(text):
    return text.lower()

def stopword(text):
    return " ".join([word for word in text.split() if word not in sw_list])

def remove_punctuation(text):
    return text.translate(str.maketrans('', '', string.punctuation))

def remove_contractions(text):
    return contractions.fix(text)

def dec_vector(doc):
    with open("Sentimental_Analysis_WV.pkl", 'rb') as file:  
        model = pickle.load(file)
    doc = [word for word in doc.split() if word in model.wv.index_to_key]
    return np.mean(model.wv[doc], axis=0)

def xvalue(text):
    X = []
    X.append(dec_vector(text))
    return X

def preprocessed(text):
    text = remove_tags(text)
    text = lwr(text)
    text = stopword(text)
    text = remove_punctuation(text)
    text = remove_contractions(text)
    X = xvalue(text)
    X = np.array(X)
    return X

def clear_text():
    st.session_state["text"] = ""

def main():
    st.set_page_config(page_title="Sentiment Analysis AI", page_icon=":smiley:", layout="wide")
    
    with open("Sentimental_Analysis_Word2Vec.pkl", 'rb') as file1:  
        rf = pickle.load(file1)
    
    st.title('Sentiment Analysis AI')
    st.markdown("""
        <style>
        .main {
            background-color: #0a3d57;
            padding: 20px;
            border-radius: 10px;
        }
        .stButton > button {
            background-color: #4CAF50;
            color: white;
            padding: 10px 24px;
            border: none;
            border-radius: 4px;
            cursor: pointer;
        }
        .stButton > button:hover {
            background-color: #45a049;
        }
        .stTextInput > div > input {
            padding: 10px;
            border: 2px solid #ccc;
            border-radius: 4px;
        }
        </style>
        """, unsafe_allow_html=True)

    st.markdown("<div class='main'>", unsafe_allow_html=True)
    
    st.header('Welcome to Sentiment Analysis AI')
    st.subheader('Check if reviews or any text have a positive or negative sentiment instantly!')

    text = st.text_input(
        "Enter some text 👇", key="text", help="Type in any text to analyze its sentiment.")
    
    col1, col2 = st.columns(2)
    
    with col1:
        if st.button('Classify'):
            if text:
                z = preprocessed(text)
                if rf.predict(z)[0] == 1:
                    st.success("Positive")
                else:
                    st.success("Negative")
            else:
                st.warning("Please enter some text to analyze.")
    
    with col2:
        st.button("Clear", on_click=clear_text)
    
    st.markdown("</div>", unsafe_allow_html=True)

if __name__ == '__main__':
    main()