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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 |