import joblib
import streamlit as st
import json
import requests
from bs4 import BeautifulSoup
from datetime import date
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np

# 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
svm_model = joblib.load('svm_model.joblib')

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 None

# Predict for text category by 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)
    
    # ----------- Proba -----------
    Logistic_Predicted_proba = logistic_model.predict_proba(processed_text)
    svm_new_probs = SVM_model.decision_function(processed_text)
    svm_probs = svm_model.predict_proba(svm_new_probs)
    predicted_label_index = np.argmax(Seq_Predicted)
    
    # ----------- Debug Logs -----------
    logistic_debug = decodedLabel(int(Logistic_Predicted[0]))
    svc_debug = decodedLabel(int(SVM_Predicted[0]))
    # predicted_label_index = np.argmax(Seq_Predicted)
    #print('Logistic', int(Logistic_Predicted[0]), logistic_debug)
    #print('SVM', int(SVM_Predicted[0]), svc_debug)
    
    return {
            'predicted_label_logistic': decodedLabel(int(Logistic_Predicted[0])),
            'probability_logistic': f"{int(float(np.max(Logistic_Predicted_proba))*10000//100)}%",
        
            'predicted_label_svm': decodedLabel(int(SVM_Predicted[0])),
            'probability_svm': f"{int(float(np.max(svm_probs))*10000//100)}%",
        
            'predicted_label_lstm': int(predicted_label_index),
            'probability_lstm': f"{int(float(np.max(Seq_Predicted))*10000//100)}%",
        
            'Article_Content': text
        }

# Init web crawling, process article content by Model and return result as JSON
lstm")
        },
    })
    
st.divider()  # 👈 Draws a horizontal rule

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

# 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}")

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

st.divider()  # 👈 Draws a horizontal rule

# ------------ Copyright Section ------------
# Get the current year
current_year = date.today().year
# Format the copyright statement with dynamic year
copyright_text = f"Copyright © {current_year}"
st.title(copyright_text)
author_names = ["Trần Thanh Phước (Mentor)", "Lương Ngọc Phương (Member)", "Trịnh Cẩm Minh (Member)"]
st.write("Meet the minds behind the work!")
for author in author_names:
    if (author == "Trịnh Cẩm Minh (Member)"): st.markdown("- [Trịnh Cẩm Minh (Member)](https://minhct.netlify.app/)")
    else: st.markdown(f"- {author}\n")  # Use f-string for bullet and newline