app.py

import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "cross-encoder/ms-marco-MiniLM-L-12-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()

# Function to compute relevance scores (in logits) and dynamically adjust threshold
def get_relevance_score_and_excerpt(query, paragraph1, paragraph2, paragraph3, threshold_weight):
    # Handle empty input for paragraphs
    paragraphs = [p for p in [paragraph1, paragraph2, paragraph3] if p.strip()]
    
    if not query.strip() or not paragraphs:
        return "Please provide both a query and at least one document paragraph.", ""

    ranked_paragraphs = []
    
    # Process each paragraph and calculate its logits and highlighted text
    for paragraph in paragraphs:
        # Tokenize the input
        inputs = tokenizer(query, paragraph, return_tensors="pt", truncation=True, padding=True)
        
        with torch.no_grad():
            output = model(**inputs, output_attentions=True)

        # Extract logits (no sigmoid applied)
        logit = output.logits.squeeze().item()
        base_relevance_score = logit  # Relevance score in logits

        # Dynamically adjust the attention threshold based on user weight
        dynamic_threshold = max(0.02, threshold_weight)

        # Extract attention scores (last layer)
        attention = output.attentions[-1]
        attention_scores = attention.mean(dim=1).mean(dim=0)

        query_tokens = tokenizer.tokenize(query)
        paragraph_tokens = tokenizer.tokenize(paragraph)

        query_len = len(query_tokens) + 2  # +2 for special tokens [CLS] and first [SEP]
        para_start_idx = query_len
        para_end_idx = len(inputs["input_ids"][0]) - 1

        if para_end_idx <= para_start_idx:
            continue

        para_attention_scores = attention_scores[para_start_idx:para_end_idx, para_start_idx:para_end_idx].mean(dim=0)

        if para_attention_scores.numel() == 0:
            continue

        # Get indices of relevant tokens above dynamic threshold
        relevant_indices = (para_attention_scores > dynamic_threshold).nonzero(as_tuple=True)[0].tolist()

        # Reconstruct paragraph with bolded relevant tokens using HTML tags
        highlighted_text = ""
        for idx, token in enumerate(paragraph_tokens):
            if idx in relevant_indices:
                highlighted_text += f"<b>{token}</b> "
            else:
                highlighted_text += f"{token} "

        highlighted_text = tokenizer.convert_tokens_to_string(highlighted_text.split())

        ranked_paragraphs.append({
            "logit": logit,
            "highlighted_text": highlighted_text
        })
    
    # Sort paragraphs by logit (descending)
    ranked_paragraphs.sort(key=lambda x: x["logit"], reverse=True)

    # Prepare output: Combine scores and highlighted text in a readable format
    output_html = "<table border='1' style='width:100%; border-collapse: collapse;'>"
    output_html += "<tr><th style='padding: 8px;'>Relevance Score (Logits)</th><th style='padding: 8px;'>Highlighted Paragraph</th></tr>"

    for item in ranked_paragraphs:
        output_html += f"<tr><td style='padding: 8px; text-align: center;'>{round(item['logit'], 4)}</td>"
        output_html += f"<td style='padding: 8px;'>{item['highlighted_text']}</td></tr>"

    output_html += "</table>"

    return output_html

# Define Gradio interface with a slider for threshold adjustment and multiple paragraphs input
interface = gr.Interface(
    fn=get_relevance_score_and_excerpt,
    inputs=[
        gr.Textbox(label="Query", placeholder="Enter your search query..."),
        gr.Textbox(label="Document Paragraph 1", placeholder="Enter a paragraph to match...", lines=4),
        gr.Textbox(label="Document Paragraph 2 (optional)", placeholder="Enter another paragraph...", lines=4),
        gr.Textbox(label="Document Paragraph 3 (optional)", placeholder="Enter another paragraph...", lines=4),
        gr.Slider(minimum=0.02, maximum=0.5, value=0.1, step=0.01, label="Attention Threshold")
    ],
    outputs=[
        gr.HTML(label="Ranked Paragraphs")
    ],
    title="Cross-Encoder Attention Highlighting with Reranking",
    description="Adjust the attention threshold to control token highlighting sensitivity. Multiple paragraphs can be added and reranked based on their logits.",
    allow_flagging="never",
    live=True
)

if __name__ == "__main__":
    interface.launch()