import streamlit as st
import pickle
import numpy as np
import pandas as pd
from scipy.sparse import hstack
from sklearn.feature_extraction.text import CountVectorizer
import nltk
import re
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer

# Ensure NLTK data files are downloaded
nltk.download('punkt')
nltk.download('stopwords')

# Define the preprocessing function
def preprocess_text(text):
    # Remove special characters and digits
    text = re.sub(r'[^a-zA-Z\s]', '', text)
    # Convert to lowercase
    text = text.lower()
    # Tokenize the text
    tokens = nltk.word_tokenize(text)
    # Remove stopwords
    stop_words = set(stopwords.words('english'))
    tokens = [word for word in tokens if word not in stop_words]
    # Perform stemming
    stemmer = PorterStemmer()
    tokens = [stemmer.stem(word) for word in tokens]
    # Join the tokens back into a single string
    preprocessed_text = ' '.join(tokens)
    return preprocessed_text

# Load the saved model
model_filename = 'logistic_regression_model.pkl'
with open(model_filename, 'rb') as file:
    model = pickle.load(file)

# Load the fitted CountVectorizer
vectorizer_filename = 'count_vectorizer.pkl'
with open(vectorizer_filename, 'rb') as file:
    vect = pickle.load(file)

st.title("Goodreads Book Review Rating Predictor 📖")
st.image('https://s26162.pcdn.co/wp-content/uploads/2023/11/6.png')
st.write('Input a book review info and let the model predict its score!')

# Prefilled review text
prefilled_review = """This is definitely one of my favorites among the "food books" I've read! I loved the characters (Raggedy Ann and Ken Carson included)! Even though Kayla is a pessimist (which I usually dislike in a character), her personality is the perfect formula in the story.
Kayla is the school rebel, she wears unusual clothes, makes fun of the "popular" crowd in school, and she only has one friend in school---Nicole. Lately, things have been changing around her. Her mother barely talks to her and her best friend is dating her long-time crush Ben! On her disastrous 16th birthday party, she made a wish to make all her birthday wishes come true. The following day she wakes up with a pink pony in her backyard! The next day it was a room full of gumballs, and the next her Raggedy Ann doll was brought to life! She figured out that her birthday wishes are coming true and she has to find a way to stop it before her 15th birthday wish comes true---which is a kiss from Ben!
I really had fun reading it. It reminded me a lot of my childhood and my silly wishes. I might have wished for a one year supply of chocolates or a new BMX bike before, LOL! Also, there are a lot of hilarious events that occurred in the story. My favorite is definitely Ken's dancing pecs! haha! The story ended abruptly though. I wish we found out what happened to Ann, Ken, the pony and the rest of her wishes...instead of Kayla waking up the following day with them gone.
Overall it was a quick read. If you are looking for something light and fun, this is perfect for you!"""

review_text = st.text_area("Enter the review text:", value=prefilled_review, height=410)
n_votes = st.number_input("Enter the number of votes:", min_value=0, value=5)
n_comments = st.number_input("Enter the number of comments:", min_value=0, value=1)

if st.button("Predict"):
    if review_text:
        # Preprocess the text
        preprocessed_text = preprocess_text(review_text)
        # Vectorize the text
        X_text_vect = vect.transform([preprocessed_text])
        # Combine with numerical features
        X_num = np.array([[n_votes, n_comments]])
        X = hstack([X_text_vect, X_num])
        # Make prediction
        prediction = model.predict(X)
        prediction = prediction.round().astype(int).clip(min=0, max=5)
        st.header(f"Predicted Rating: {prediction[0]} ⭐")
    else:
        st.write("Please enter the review text.")