app.py

import streamlit as st
import torch
import torch.nn.functional as F
from torch.nn.functional import softmax
from transformers import AlbertTokenizerFast, AutoModelForTokenClassification
import pandas as pd
import trafilatura

# Set Streamlit configuration
st.set_page_config(layout="wide", page_title="LinkBERT")

# Load model and tokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AlbertTokenizerFast.from_pretrained("dejanseo/LinkBERT-mini")
model = AutoModelForTokenClassification.from_pretrained("dejanseo/LinkBERT-mini").to(device)
model.eval()

# Functions

def tokenize_with_indices(text: str):
    encoded = tokenizer.encode_plus(text, return_offsets_mapping=True, add_special_tokens=True)
    return encoded['input_ids'], encoded['offset_mapping']

def fetch_and_extract_content(url: str):
    downloaded = trafilatura.fetch_url(url)
    if downloaded:
        content = trafilatura.extract(downloaded, include_comments=False, include_tables=False)
        return content
    return None

def process_text(inputs: str, confidence_threshold: float):
    max_chunk_length = 512 - 2
    words = inputs.split()
    chunk_texts = []
    current_chunk = []
    current_length = 0
    for word in words:
        if len(tokenizer.tokenize(word)) + current_length > max_chunk_length:
            chunk_texts.append(" ".join(current_chunk))
            current_chunk = [word]
            current_length = len(tokenizer.tokenize(word))
        else:
            current_chunk.append(word)
            current_length += len(tokenizer.tokenize(word))
    chunk_texts.append(" ".join(current_chunk))

    df_data = {
        'Word': [],
        'Prediction': [],
        'Confidence': [],
        'Start': [],
        'End': []
    }
    reconstructed_text = ""
    original_position_offset = 0

    for chunk in chunk_texts:
        input_ids, token_offsets = tokenize_with_indices(chunk)
        predictions = []

        input_ids_tensor = torch.tensor(input_ids).unsqueeze(0).to(device)
        with torch.no_grad():
            outputs = model(input_ids_tensor)
            logits = outputs.logits
            predictions = torch.argmax(logits, dim=-1).squeeze().tolist()
            softmax_scores = F.softmax(logits, dim=-1).squeeze().tolist()

        word_info = {}

        for idx, (start, end) in enumerate(token_offsets):
            if idx == 0 or idx == len(token_offsets) - 1:
                continue

            word_start = start
            while word_start > 0 and chunk[word_start-1] != ' ':
                word_start -= 1

            if word_start not in word_info:
                word_info[word_start] = {'prediction': 0, 'confidence': 0.0, 'subtokens': []}

            confidence_percentage = softmax_scores[idx][predictions[idx]] * 100

            if predictions[idx] == 1 and confidence_percentage >= confidence_threshold:
                word_info[word_start]['prediction'] = 1

            word_info[word_start]['confidence'] = max(word_info[word_start]['confidence'], confidence_percentage)
            word_info[word_start]['subtokens'].append((start, end, chunk[start:end]))

        last_end = 0
        for word_start in sorted(word_info.keys()):
            word_data = word_info[word_start]
            for subtoken_start, subtoken_end, subtoken_text in word_data['subtokens']:
                escaped_subtoken_text = subtoken_text.replace('$', '\\$')  # Perform replacement outside f-string
                if last_end < subtoken_start:
                    reconstructed_text += chunk[last_end:subtoken_start]
                if word_data['prediction'] == 1:
                    reconstructed_text += f"<span style='background-color: rgba(0, 255, 0); display: inline;'>{escaped_subtoken_text}</span>"
                else:
                    reconstructed_text += escaped_subtoken_text
                last_end = subtoken_end

                df_data['Word'].append(escaped_subtoken_text)
                df_data['Prediction'].append(word_data['prediction'])
                df_data['Confidence'].append(word_info[word_start]['confidence'])
                df_data['Start'].append(subtoken_start + original_position_offset)
                df_data['End'].append(subtoken_end + original_position_offset)

            original_position_offset += len(chunk) + 1

        reconstructed_text += chunk[last_end:].replace('$', '\\$')

    df_tokens = pd.DataFrame(df_data)
    return reconstructed_text, df_tokens

# Streamlit Interface

st.title('LinkBERT')
st.markdown("""
LinkBERT is a model developed by [Dejan Marketing](https://dejanmarketing.com/) designed to predict natural link placement within web content. You can either enter plain text or the URL for automated plain text extraction. To reduce the number of link predictions increase the threshold slider value.
""")

confidence_threshold = st.slider('Confidence Threshold', 50, 100, 50)

tab1, tab2 = st.tabs(["Text Input", "URL Input"])

with tab1:
    user_input = st.text_area("Enter text to process:")
    if st.button('Process Text'):
        highlighted_text, df_tokens = process_text(user_input, confidence_threshold)
        st.markdown(highlighted_text, unsafe_allow_html=True)
        st.dataframe(df_tokens)

with tab2:
    url_input = st.text_input("Enter URL to process:")
    if st.button('Fetch and Process'):
        content = fetch_and_extract_content(url_input)
        if content:
            highlighted_text, df_tokens = process_text(content, confidence_threshold)
            st.markdown(highlighted_text, unsafe_allow_html=True)
            st.dataframe(df_tokens)
        else:
            st.error("Could not fetch content from the URL. Please check the URL and try again.")

# Additional information at the end
st.divider()
st.markdown("""

## Applications of LinkBERT

LinkBERT's applications are vast and diverse, tailored to enhance both the efficiency and quality of web content creation and analysis:

- **Anchor Text Suggestion:** Acts as a mechanism during internal link optimization, suggesting potential anchor texts to web authors.
- **Evaluation of Existing Links:** Assesses the naturalness of link placements within existing content, aiding in the refinement of web pages.
- **Link Placement Guide:** Offers guidance to link builders by suggesting optimal placement for links within content.
- **Anchor Text Idea Generator:** Provides creative anchor text suggestions to enrich content and improve SEO strategies.
- **Spam and Inorganic SEO Detection:** Helps identify unnatural link patterns, contributing to the detection of spam and inorganic SEO tactics.

## Training and Performance

LinkBERT was fine-tuned on a dataset of organic web content and editorial links.

[Watch the video](https://www.youtube.com/watch?v=A0ZulyVqjZo)
            
# Engage Our Team
Interested in using this in an automated pipeline for bulk link prediction?

Please [book an appointment](https://dejanmarketing.com/conference/) to discuss your needs.
""")