import joblib # Load Joblib file
import json # Load JSON file
from sklearn.feature_extraction.text import CountVectorizer # Convert text to BOW format
from tensorflow.keras.preprocessing.text import Tokenizer # tokenizing text documents into sequences of tokens (Seq Model)
from tensorflow.keras.preprocessing.sequence import pad_sequences # ensure that all sequences in a dataset have the same length (Seq Model)
from tensorflow.keras.models import load_model # load a pre-trained Keras model saved in the Hierarchical Data Format (HDF5) file format
import numpy as np # scientific computing in Python
import streamlit as st

from . import SVM_Linear_Model
from . import Logistic_Model
from . import vectorizer
from . import tokenizer

# load all the models and vectorizer (global vocabulary)
# Seq_model = load_model('./LSTM.h5') # Sequential
SVM_Linear_model = joblib.load(SVM_Linear_Model) # SVM
logistic_model = joblib.load(Logistic_Model) # Logistic
vectorizer = joblib.load(vectorizer) # global vocabulary
tokenizer = joblib.load(tokenizer)

def crawURL(url):
    print(f"Crawling page: {url}")
    # Fetch the sitemap
    response = requests.get(sitemap_url)
    # Parse the sitemap HTML
    soup = BeautifulSoup(response.content, 'html.parser')

    # Find all anchor tags that are children of span tags with class 'sitemap-link'
    urls = [span.a['href'] for span in soup.find_all('span', class_='sitemap-link') if span.a]

    # Crawl pages and extract data
    try:
        print(f"Crawling page: {url}")
        # Fetch page content
        page_response = requests.get(url)
        page_content = page_response.content

        # Parse page content with BeautifulSoup
        soup = BeautifulSoup(page_content, 'html.parser')

        # Extract data you need from the page
        author = soup.find("meta", {"name": "author"}).attrs['content'].strip()
        date_published = soup.find("meta", {"property": "article:published_time"}).attrs['content'].strip()
        article_section = soup.find("meta", {"name": "meta-section"}).attrs['content']
        url = soup.find("meta", {"property": "og:url"}).attrs['content']
        headline = soup.find("h1", {"data-editable": "headlineText"}).text.strip()
        description = soup.find("meta", {"name": "description"}).attrs['content'].strip()
        keywords = soup.find("meta", {"name": "keywords"}).attrs['content'].strip()
        text = soup.find(itemprop="articleBody")
        # Find all <p> tags with class "paragraph inline-placeholder"
        paragraphs = text.find_all('p', class_="paragraph inline-placeholder")

        # Initialize an empty list to store the text content of each paragraph
        paragraph_texts = []

        # Iterate over each <p> tag and extract its text content
        for paragraph in paragraphs:
            paragraph_texts.append(paragraph.text.strip())

        # Join the text content of all paragraphs into a single string
        full_text = ''.join(paragraph_texts)
        return full_text
        
    except Exception as e:
        print(f"Failed to crawl page: {url}, Error: {str(e)}")
        return null

def process_api(text):
    # Vectorize the text data
    processed_text = vectorizer.transform([text])
    sequence = tokenizer.texts_to_sequences([text])
    padded_sequence = pad_sequences(sequence, maxlen=1000, padding='post')
    # Get the predicted result from models
    # Seq_Predicted = Seq_model.predict(padded_sequence)
    SVM_Predicted = SVM_model.predict(processed_text).tolist()
    Logistic_Predicted = logistic_model.predict(processed_text).tolist()

    # predicted_label_index = np.argmax(Seq_Predicted)
    return {
            'Article_Content': text,
            'SVM_Predicted': int(SVM_Predicted[0]),
            'Logistic_Predicted': int(Logistic_Predicted[0])
        }

# Using Model to handle and return Category Route
def categorize(url):
    try:
        article_content = crawURL(url)
        result = process_api(article_content)
        return result
    except:
        return "No text found in the response body"

url = st.text_input("enter your CNN's URL here")
# Test
x = st.slider('Select a value')
st.write(x, 'squared is', x * x)

if url:
    result = categorize(url)
    st.json(result)