App.py

import joblib # Load Joblib file
import json # Load JSON file
from sklearn.feature_extraction.text import CountVectorizer # Convert text to BOW format
from flask import Flask, request, jsonify # Flask Server
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

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

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,
            'LSTM':int(predicted_label_index),
            'SVM_Predicted': int(SVM_Predicted[0]),
            'Logistic_Predicted': int(Logistic_Predicted[0])
        }


# Using Model to handle and return Category Route
@app.route('/api/categorize', methods=['POST'])
def categorize():
    try:
        data = request.get_json()  # Get JSON data from the request body
        text = data['text']  # Get the value of the 'text' key
        url = data['url'] # Get the URL from request body

        article_content = crawURL(url)
        result = process_api(article_content)
        return jsonify(result), 200
    except:
        return jsonify("No text found in the response body"), 400


# Return blogs_from_CNN list
@app.route('/api/blogs', methods=['GET'])
@cross_origin()
def blog_list():
    # Specify the path to the uploaded JSON file: [GET] API Blogs
    json_file_path = 'C:/Users/LENOVO/Downloads/class/Get_Data_Minimize.json'
    # Read and parse the JSON data directly
    with open(json_file_path, 'r' ,encoding="utf8") as f:
        blogs_from_cnn = json.load(f)

    # Python's default behavior is to represent strings with single quotes when printed
    # When you print the loaded JSON data in Python,
    # you might see the representation with single quotes,
    for blog in blogs_from_cnn:
        result = process_api(blog['Article text'])
        blog.update(result)
        print(blog)
    return jsonify(blogs_from_cnn), 200

url = st.text_input("enter your CNN's URL here")

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