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| 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_Linear_model = joblib.load("SVM_Linear_Kernel") # 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 | |
| # 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() | |
| SVM_Predicted = SVM_model.predict(processed_text).tolist() | |
| # Seq_Predicted = Seq_model.predict(padded_sequence) | |
| # predicted_label_index = np.argmax(Seq_Predicted) | |
| return { | |
| 'SVM_Predicted': int(SVM_Predicted[0]), | |
| 'Logistic_Predicted': int(Logistic_Predicted[0]) | |
| 'Article_Content': text, | |
| } | |
| # Using Model to handle and return Category Route | |
| def categorize(url): | |
| print("outside try", url) | |
| try: | |
| print("inside try", url) | |
| article_content = crawURL(url) | |
| print("inside try article_content", article_content) | |
| result = process_api(article_content) | |
| print("inside try result", result) | |
| return result | |
| except Exception as error: | |
| if hasattr(error, 'message'): | |
| return {"error_message": error.message} | |
| else: | |
| return {"error_message": error} | |
| # Main App | |
| url = st.text_input("enter your CNN's URL here") | |
| if url: | |
| result = categorize(url) | |
| st.json(result) |