Update app.py

app.py

@@ -15,110 +15,6 @@ 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")
-
 # Test
 x = st.slider('Select a value')
 st.write(x, 'squared is', x * x)