import joblib
import streamlit as st
import json
import requests
from bs4 import BeautifulSoup

# load all the models and vectorizer (global vocabulary)
# Seq_model = load_model("LSTM.h5") # Sequential
SVM_model = joblib.load("SVM_Linear_Kernel.joblib") # SVM
logistic_model = joblib.load("Logistic_Model.joblib") # Logistic
vectorizer = joblib.load("vectorizer.joblib") # global vocabulary (used for Logistic, SVC)
# tokenizer = joblib.load("tokenizer.joblib") # used for LSTM

# Decode label function
# {'business': 0, 'entertainment': 1, 'health': 2, 'politics': 3, 'sport': 4}
def decodedLabel(input_number):
    print('receive label encoded', input_number)
    categories = {
      0: 'Business',
      1: 'Entertainment',
      2: 'Health',
      3: 'Politics',
      4: 'Sport'
    }
    result = categories.get(input_number) # Ex: Health
    print('decoded result', result)
    return result

# Web Crawler function
def crawURL(url):
    # Fetch the URL content
    response = requests.get(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

# Predict for text category using Models
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
    Logistic_Predicted = logistic_model.predict(processed_text).tolist() # Logistic Model
    SVM_Predicted = SVM_model.predict(processed_text).tolist() # SVC Model
    # Seq_Predicted = Seq_model.predict(padded_sequence)
    # predicted_label_index = np.argmax(Seq_Predicted)

    # ----------- Debug Logs -----------
    logistic_debug = decodedLabel(int(Logistic_Predicted[0]))
    svc_debug = decodedLabel(int(SVM_Predicted[0]))
    print('Logistic', int(Logistic_Predicted[0]), logistic_debug)
    print('SVM', int(SVM_Predicted[0]), svc_debug)
    
    return {
            'Logistic_Predicted':decodedLabel(int(Logistic_Predicted[0])),
            'SVM_Predicted': decodedLabel(int(SVM_Predicted[0])),
            'Article_Content': text
        }

# Using Model to handle and return Category Route
def categorize(url):
    try:
        article_content = crawURL(url)
        result = process_api(article_content)
        return result
    except Exception as error:
        if hasattr(error, 'message'):
            return {"error_message": error.message}
        else:
            return {"error_message": error}

# Main App
with st.expander("See explanation"): # Explain to user why this project is only worked for CNN domain
    st.write(
        '''
            This project works best with CNN articles right now. 
            Our web crawler is like a special tool for CNN's website. 
            It can't quite understand other websites because they're built differently
        '''
    )

url = st.text_input("Paste your CNN Article's URL Here")


if url:
    result = categorize(url)
    article_content = result.get('Article_Content')
    st.text_area("Article Content", value=article_content, height=400) # render the article content as textarea element
    st.divider()  # 👈 Draws a horizontal rule
    st.json({
        "Logistic": result.get("Logistic_Predicted"),
        "SVC": result.get("SVM_Predicted")
    })
st.divider()  # 👈 Draws a horizontal rule

# Category labels and corresponding counts
categories = ["Sport", "Health", "Entertainment", "Politics", "Business"]
counts = [5638, 4547, 2658, 2461, 1362]

# Create the bar chart
st.bar_chart(data=dict(zip(categories, counts)))

# Optional: Add a chart title
st.title("Training Data Category Distribution")

# Optional: Display additional information
st.write("Here's a breakdown of the number of articles in each category:")
for category, count in zip(categories, counts):
  st.write(f"- {category}: {count}")