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#from . import SVM_Linear_Model
#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.joblib") # Logistic
vectorizer = joblib.load("vectorizer.joblib") # 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) |