Update app.py

app.py

@@ -6,10 +6,10 @@ import torch
 model_name = "cross-encoder/ms-marco-MiniLM-L-12-v2"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
-model.eval()  # Set the model to evaluation mode
+model.eval()
 
 # Function to compute relevance score and dynamically adjust threshold
-def get_relevance_score_and_excerpt(query, paragraph):
+def get_relevance_score_and_excerpt(query, paragraph, threshold_weight):
     if not query.strip() or not paragraph.strip():
         return "Please provide both a query and a document paragraph.", ""
 
@@ -17,32 +17,29 @@ def get_relevance_score_and_excerpt(query, paragraph):
     inputs = tokenizer(query, paragraph, return_tensors="pt", truncation=True, padding=True)
     
     with torch.no_grad():
-        output = model(**inputs, output_attentions=True)  # Get attention scores
+        output = model(**inputs, output_attentions=True)
 
     # Extract logits and calculate base relevance score
     logit = output.logits.squeeze().item()
     base_relevance_score = torch.sigmoid(torch.tensor(logit)).item()
 
-    # Dynamically adjust the attention threshold based on relevance score
-    dynamic_threshold = max(0.02, base_relevance_score * 0.1)  # Example formula
+    # Dynamically adjust the attention threshold based on user weight (no relevance score influence)
+    dynamic_threshold = max(0.02, threshold_weight)
 
     # Extract attention scores (last layer)
-    attention = output.attentions[-1]  # Shape: (batch_size, num_heads, seq_len, seq_len)
-    attention_scores = attention.mean(dim=1).mean(dim=0)  # Average over heads and batch
+    attention = output.attentions[-1]
+    attention_scores = attention.mean(dim=1).mean(dim=0)
 
-    # Tokenize query and paragraph separately
     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  # Ignore final [SEP] token
+    para_end_idx = len(inputs["input_ids"][0]) - 1
 
-    # Handle potential indexing issues
     if para_end_idx <= para_start_idx:
         return round(base_relevance_score, 4), "No relevant tokens extracted."
 
-    # Extract paragraph attention scores and apply dynamic threshold
     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:
@@ -51,39 +48,32 @@ def get_relevance_score_and_excerpt(query, paragraph):
     # Get indices of relevant tokens above dynamic threshold
     relevant_indices = (para_attention_scores > dynamic_threshold).nonzero(as_tuple=True)[0].tolist()
 
-    # Compute attention-weighted relevance score
-    if relevant_indices:
-        relevant_attention_values = para_attention_scores[relevant_indices]
-        attention_weighted_score = relevant_attention_values.mean().item() * base_relevance_score
-    else:
-        attention_weighted_score = base_relevance_score  # No relevant tokens found
-
-    # Reconstruct paragraph with bolded relevant tokens
+    # 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"**{token}** "
+            highlighted_text += f"<b>{token}</b> "
         else:
             highlighted_text += f"{token} "
 
-    # Convert tokens back to readable format
     highlighted_text = tokenizer.convert_tokens_to_string(highlighted_text.split())
 
-    return round(attention_weighted_score, 4), highlighted_text
+    return round(base_relevance_score, 4), highlighted_text
 
-# Define Gradio interface
+# Define Gradio interface with a slider for threshold adjustment
 interface = gr.Interface(
     fn=get_relevance_score_and_excerpt,
     inputs=[
         gr.Textbox(label="Query", placeholder="Enter your search query..."),
-        gr.Textbox(label="Document Paragraph", placeholder="Enter a paragraph to match...")
+        gr.Textbox(label="Document Paragraph", placeholder="Enter a paragraph to match..."),
+        gr.Slider(minimum=0.02, maximum=0.5, value=0.1, step=0.01, label="Attention Threshold")
     ],
     outputs=[
-        gr.Textbox(label="Attention-Weighted Relevance Score"),
+        gr.Textbox(label="Relevance Score"),
         gr.HTML(label="Highlighted Document Paragraph")
     ],
-    title="Cross-Encoder with Dynamic Attention Threshold",
-    description="Enter a query and document paragraph to get a relevance score with relevant tokens in bold.",
+    title="Cross-Encoder Attention Highlighting",
+    description="Adjust the attention threshold to control token highlighting sensitivity.",
     allow_flagging="never",
     live=True
 )