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import joblib | |
import streamlit as st | |
import json | |
import requests | |
from bs4 import BeautifulSoup | |
from datetime import date | |
# 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 | |
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 null | |
# 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) | |
# ----------- Debug Logs ----------- | |
logistic_debug = decodedLabel(int(Logistic_Predicted[0])) | |
svc_debug = decodedLabel(int(SVM_Predicted[0])) | |
print('Logistic', int(Logistic_Predicted[0]), logistic_debug) | |
print('SVM', int(SVM_Predicted[0]), svc_debug) | |
return { | |
'Logistic_Predicted':decodedLabel(int(Logistic_Predicted[0])), | |
'SVM_Predicted': decodedLabel(int(SVM_Predicted[0])), | |
'Article_Content': text | |
} | |
# Init web crawling, process article content by Model and return result as JSON | |
def categorize(url): | |
try: | |
article_content = crawURL(url) | |
result = process_api(article_content) | |
return result | |
except Exception as error: | |
if hasattr(error, 'message'): | |
return {"error_message": error.message} | |
else: | |
return {"error_message": error} | |
# Main App | |
st.title('Instant Category Classification') | |
st.write("Unsure what category a CNN article belongs to? Our clever tool can help! Paste the URL below and press Enter. We'll sort it into one of our 5 categories in a flash! ⚡️") | |
# Define category information (modify content and bullet points as needed) | |
categories = { | |
"Business": [ | |
"Analyze market trends and investment opportunities.", | |
"Gain insights into company performance and industry news.", | |
"Stay informed about economic developments and regulations." | |
], | |
"Health": [ | |
"Discover healthy recipes and exercise tips.", | |
"Learn about the latest medical research and advancements.", | |
"Find resources for managing chronic conditions and improving well-being." | |
], | |
"Sport": [ | |
"Follow your favorite sports teams and athletes.", | |
"Explore news and analysis from various sports categories.", | |
"Stay updated on upcoming games and competitions." | |
], | |
"Politics": [ | |
"Get informed about current political events and policies.", | |
"Understand different perspectives on political issues.", | |
"Engage in discussions and debates about politics." | |
], | |
"Entertainment": [ | |
"Find recommendations for movies, TV shows, and music.", | |
"Explore reviews and insights from entertainment critics.", | |
"Stay updated on celebrity news and cultural trends." | |
] | |
} | |
# Define model information (modify descriptions as needed) | |
models = { | |
"Logistic Regression": "A widely used statistical method for classification problems. It excels at identifying linear relationships between features and the target variable.", | |
"SVC (Support Vector Classifier)": "A powerful machine learning model that seeks to find a hyperplane that best separates data points of different classes. It's effective for high-dimensional data and can handle some non-linear relationships.", | |
"LSTM (Long Short-Term Memory)": "A type of recurrent neural network (RNN) particularly well-suited for sequential data like text or time series. LSTMs can effectively capture long-term dependencies within the data.", | |
"BERT (Bidirectional Encoder Representations from Transformers)": "A powerful pre-trained model based on the Transformer architecture. It excels at understanding the nuances of language and can be fine-tuned for various NLP tasks like text classification." | |
} | |
# Create expanders containing list of categories can be classified | |
with st.expander("Category List"): | |
# Title for each category | |
st.subheader("Available Categories:") | |
for category in categories.keys(): | |
st.write(f"- {category}") | |
# Content for each category (separated by a horizontal line) | |
st.write("---") | |
for category, content in categories.items(): | |
st.subheader(category) | |
for item in content: | |
st.write(f"- {item}") | |
# Create expanders containing list of models used in this project | |
with st.expander("Available Models"): | |
st.subheader("List of Models:") | |
for model_name in models.keys(): | |
st.write(f"- {model_name}") | |
st.write("---") | |
for model_name, description in models.items(): | |
st.subheader(model_name) | |
st.write(description) | |
# Explain to user why this project is only worked for CNN domain | |
with st.expander("Tips", expanded=True): | |
st.write( | |
''' | |
This project works best with CNN articles right now. | |
Our web crawler is like a special tool for CNN's website. | |
It can't quite understand other websites because they're built differently | |
''' | |
) | |
st.divider() # 👈 Draws a horizontal rule | |
st.title('Dive in! See what category your CNN story belongs to 😉.') | |
# Paste URL Input | |
url = st.text_input("Find your favorite CNN story! Paste the URL and press ENTER 🔍.", placeholder='Ex: https://edition.cnn.com/2012/01/31/health/frank-njenga-mental-health/index.html') | |
if url: | |
st.divider() # 👈 Draws a horizontal rule | |
result = categorize(url) | |
article_content = result.get('Article_Content') | |
st.title('Article Content Fetched') | |
st.text_area("", value=article_content, height=400) # render the article content as textarea element | |
st.divider() # 👈 Draws a horizontal rule | |
st.title('Predicted Results') | |
st.json({ | |
"Logistic": result.get("Logistic_Predicted"), | |
"SVC": result.get("SVM_Predicted") | |
}) | |
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.text(f"- {author}\n") # Use f-string for bullet and newline |