app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch
import numpy as np
from tqdm.auto import tqdm # Still useful for model loading progress if desired, but not strictly necessary for this simplified version
import os # Still useful for general purpose, but not explicitly used in this simplified version

# --- Model Loading ---
tokenizer_splade = None
model_splade = None
tokenizer_splade_lexical = None
model_splade_lexical = None
tokenizer_splade_doc = None
model_splade_doc = None

# Load SPLADE v3 model (original)
try:
    tokenizer_splade = AutoTokenizer.from_pretrained("naver/splade-cocondenser-selfdistil")
    model_splade = AutoModelForMaskedLM.from_pretrained("naver/splade-cocondenser-selfdistil")
    model_splade.eval()
    print("SPLADE-cocondenser-distil model loaded successfully!")
except Exception as e:
    print(f"Error loading SPLADE-cocondenser-distil model: {e}")
    print("Please ensure you have accepted any user access agreements on the Hugging Face Hub page for 'naver/splade-cocondenser-selfdistil'.")

# Load SPLADE v3 Lexical model
try:
    splade_lexical_model_name = "naver/splade-v3-lexical"
    tokenizer_splade_lexical = AutoTokenizer.from_pretrained(splade_lexical_model_name)
    model_splade_lexical = AutoModelForMaskedLM.from_pretrained(splade_lexical_model_name)
    model_splade_lexical.eval()
    print(f"SPLADE-v3-Lexical model '{splade_lexical_model_name}' loaded successfully!")
except Exception as e:
    print(f"Error loading SPLADE-v3-Lexical model: {e}")
    print(f"Please ensure '{splade_lexical_model_name}' is accessible (check Hugging Face Hub for potential agreements).")

# Load SPLADE v3 Doc model
try:
    splade_doc_model_name = "naver/splade-v3-doc"
    tokenizer_splade_doc = AutoTokenizer.from_pretrained(splade_doc_model_name)
    model_splade_doc = AutoModelForMaskedLM.from_pretrained(splade_doc_model_name)
    model_splade_doc.eval()
    print(f"SPLADE-v3-Doc model '{splade_doc_model_name}' loaded successfully!")
except Exception as e:
    print(f"Error loading SPLADE-v3-Doc model: {e}")
    print(f"Please ensure '{splade_doc_model_name}' is accessible (check Hugging Face Hub for potential agreements).")


# --- Helper function for lexical mask (now handles batches, but used for single input here) ---
def create_lexical_bow_mask(input_ids_batch, vocab_size, tokenizer):
    """
    Creates a batch of lexical BOW masks.
    input_ids_batch: torch.Tensor of shape (batch_size, sequence_length)
    vocab_size: int, size of the tokenizer vocabulary
    tokenizer: the tokenizer object
    Returns: torch.Tensor of shape (batch_size, vocab_size)
    """
    batch_size = input_ids_batch.shape[0]
    bow_masks = torch.zeros(batch_size, vocab_size, device=input_ids_batch.device)

    for i in range(batch_size):
        input_ids = input_ids_batch[i] # Get input_ids for the current item in the batch
        meaningful_token_ids = []
        for token_id in input_ids.tolist():
            if token_id not in [
                tokenizer.pad_token_id,
                tokenizer.cls_token_id,
                tokenizer.sep_token_id,
                tokenizer.mask_token_id,
                tokenizer.unk_token_id
            ]:
                meaningful_token_ids.append(token_id)
                
        if meaningful_token_ids:
            # Apply mask to the current row in the batch
            bow_masks[i, list(set(meaningful_token_ids))] = 1
            
    return bow_masks


# --- Core Representation Functions (Return Formatted Strings - for Explorer Tab) ---
# These functions take single text input for the Explorer tab
def get_splade_cocondenser_representation(text):
    if tokenizer_splade is None or model_splade is None:
        return "SPLADE-cocondenser-distil model is not loaded. Please check the console for loading errors."

    inputs = tokenizer_splade(text, return_tensors="pt", padding=True, truncation=True)
    inputs = {k: v.to(model_splade.device) for k, v in inputs.items()}

    with torch.no_grad():
        output = model_splade(**inputs)

    if hasattr(output, 'logits'):
        splade_vector = torch.max(
            torch.log(1 + torch.relu(output.logits)) * inputs['attention_mask'].unsqueeze(-1),
            dim=1
        )[0].squeeze() # Squeeze is fine here as it's a single input
    else:
        return "Model output structure not as expected for SPLADE-cocondenser-distil. 'logits' not found."

    indices = torch.nonzero(splade_vector).squeeze().cpu().tolist()
    if not isinstance(indices, list):
        indices = [indices] if indices else []

    values = splade_vector[indices].cpu().tolist()
    token_weights = dict(zip(indices, values))

    meaningful_tokens = {}
    for token_id, weight in token_weights.items():
        decoded_token = tokenizer_splade.decode([token_id])
        if decoded_token not in ["[CLS]", "[SEP]", "[PAD]", "[UNK]"] and len(decoded_token.strip()) > 0:
            meaningful_tokens[decoded_token] = weight

    sorted_representation = sorted(meaningful_tokens.items(), key=lambda item: item[1], reverse=True)

    formatted_output = "SPLADE-cocondenser-distil Representation (Weighting and Expansion):\n"
    if not sorted_representation:
        formatted_output += "No significant terms found for this input.\n"
    else:
        for term, weight in sorted_representation:
            formatted_output += f"- **{term}**: {weight:.4f}\n"

    formatted_output += "\n--- Raw SPLADE Vector Info ---\n"
    formatted_output += f"Total non-zero terms in vector: {len(indices)}\n"
    formatted_output += f"Sparsity: {1 - (len(indices) / tokenizer_splade.vocab_size):.2%}\n"

    return formatted_output


def get_splade_lexical_representation(text):
    if tokenizer_splade_lexical is None or model_splade_lexical is None:
        return "SPLADE-v3-Lexical model is not loaded. Please check the console for loading errors."

    inputs = tokenizer_splade_lexical(text, return_tensors="pt", padding=True, truncation=True)
    inputs = {k: v.to(model_splade_lexical.device) for k, v in inputs.items()}

    with torch.no_grad():
        output = model_splade_lexical(**inputs)

    if hasattr(output, 'logits'):
        splade_vector = torch.max(
            torch.log(1 + torch.relu(output.logits)) * inputs['attention_mask'].unsqueeze(-1),
            dim=1
        )[0].squeeze() # Squeeze is fine here
    else:
        return "Model output structure not as expected for SPLADE-v3-Lexical. 'logits' not found."

    # Always apply lexical mask for this model's specific behavior
    vocab_size = tokenizer_splade_lexical.vocab_size
    # Call with unsqueezed input_ids for single sample processing
    bow_mask = create_lexical_bow_mask(
        inputs['input_ids'], vocab_size, tokenizer_splade_lexical
    ).squeeze() # Squeeze back for single output
    splade_vector = splade_vector * bow_mask

    indices = torch.nonzero(splade_vector).squeeze().cpu().tolist()
    if not isinstance(indices, list):
        indices = [indices] if indices else []

    values = splade_vector[indices].cpu().tolist()
    token_weights = dict(zip(indices, values))

    meaningful_tokens = {}
    for token_id, weight in token_weights.items():
        decoded_token = tokenizer_splade_lexical.decode([token_id])
        if decoded_token not in ["[CLS]", "[SEP]", "[PAD]", "[UNK]"] and len(decoded_token.strip()) > 0:
            meaningful_tokens[decoded_token] = weight

    sorted_representation = sorted(meaningful_tokens.items(), key=lambda item: item[1], reverse=True)

    formatted_output = "SPLADE-v3-Lexical Representation (Weighting):\n"
    if not sorted_representation:
        formatted_output += "No significant terms found for this input.\n"
    else:
        for term, weight in sorted_representation:
            formatted_output += f"- **{term}**: {weight:.4f}\n"

    formatted_output += "\n--- Raw SPLADE Vector Info ---\n"
    formatted_output += f"Total non-zero terms in vector: {len(indices)}\n"
    formatted_output += f"Sparsity: {1 - (len(indices) / tokenizer_splade_lexical.vocab_size):.2%}\n"

    return formatted_output


def get_splade_doc_representation(text):
    if tokenizer_splade_doc is None or model_splade_doc is None:
        return "SPLADE-v3-Doc model is not loaded. Please check the console for loading errors."

    inputs = tokenizer_splade_doc(text, return_tensors="pt", padding=True, truncation=True)
    inputs = {k: v.to(model_splade_doc.device) for k, v in inputs.items()}

    with torch.no_grad():
        output = model_splade_doc(**inputs)

    if not hasattr(output, "logits"):
        return "Model output structure not as expected. 'logits' not found."

    vocab_size = tokenizer_splade_doc.vocab_size
    # Call with unsqueezed input_ids for single sample processing
    binary_splade_vector = create_lexical_bow_mask(
        inputs['input_ids'], vocab_size, tokenizer_splade_doc
    ).squeeze() # Squeeze back for single output
    
    indices = torch.nonzero(binary_splade_vector).squeeze().cpu().tolist()
    if not isinstance(indices, list):
        indices = [indices] if indices else []
        
    values = [1.0] * len(indices) # All values are 1 for binary representation
    token_weights = dict(zip(indices, values))

    meaningful_tokens = {}
    for token_id, weight in token_weights.items():
        decoded_token = tokenizer_splade_doc.decode([token_id])
        if decoded_token not in ["[CLS]", "[SEP]", "[PAD]", "[UNK]"] and len(decoded_token.strip()) > 0:
            meaningful_tokens[decoded_token] = weight

    sorted_representation = sorted(meaningful_tokens.items(), key=lambda item: item[0]) # Sort alphabetically for clarity

    formatted_output = "SPLADE-v3-Doc Representation (Binary):\n"
    if not sorted_representation:
        formatted_output += "No significant terms found for this input.\n"
    else:
        for i, (term, _) in enumerate(sorted_representation):
            if i >= 50: # Limit display for very long lists
                formatted_output += f"...and {len(sorted_representation) - 50} more terms.\n"
                break
            formatted_output += f"- **{term}**\n"

    formatted_output += "\n--- Raw Binary Sparse Vector Info ---\n"
    formatted_output += f"Total activated terms: {len(indices)}\n"
    formatted_output += f"Sparsity: {1 - (len(indices) / tokenizer_splade_doc.vocab_size):.2%}\n"

    return formatted_output


# --- Unified Prediction Function for the Explorer Tab ---
def predict_representation_explorer(model_choice, text):
    if model_choice == "SPLADE-cocondenser-distil (weighting and expansion)":
        return get_splade_cocondenser_representation(text)
    elif model_choice == "SPLADE-v3-Lexical (weighting)":
        return get_splade_lexical_representation(text)
    elif model_choice == "SPLADE-v3-Doc (binary)":
        return get_splade_doc_representation(text)
    else:
        return "Please select a model."

# --- Core Representation Functions (Return RAW TENSORS - for Dot Product Tab) ---
# These functions remain unchanged from the previous iteration, as they return the raw tensors.
def get_splade_cocondenser_vector(text):
    if tokenizer_splade is None or model_splade is None:
        return None

    inputs = tokenizer_splade(text, return_tensors="pt", padding=True, truncation=True)
    inputs = {k: v.to(model_splade.device) for k, v in inputs.items()}

    with torch.no_grad():
        output = model_splade(**inputs)

    if hasattr(output, 'logits'):
        splade_vector = torch.max(
            torch.log(1 + torch.relu(output.logits)) * inputs['attention_mask'].unsqueeze(-1),
            dim=1
        )[0].squeeze()
        return splade_vector
    return None

def get_splade_lexical_vector(text):
    if tokenizer_splade_lexical is None or model_splade_lexical is None:
        return None

    inputs = tokenizer_splade_lexical(text, return_tensors="pt", padding=True, truncation=True)
    inputs = {k: v.to(model_splade_lexical.device) for k, v in inputs.items()}

    with torch.no_grad():
        output = model_splade_lexical(**inputs)

    if hasattr(output, 'logits'):
        splade_vector = torch.max(
            torch.log(1 + torch.relu(output.logits)) * inputs['attention_mask'].unsqueeze(-1),
            dim=1
        )[0].squeeze()
        
        vocab_size = tokenizer_splade_lexical.vocab_size
        bow_mask = create_lexical_bow_mask(
            inputs['input_ids'], vocab_size, tokenizer_splade_lexical
        ).squeeze()
        
        splade_vector = splade_vector * bow_mask
        return splade_vector
    return None

def get_splade_doc_vector(text):
    if tokenizer_splade_doc is None or model_splade_doc is None:
        return None

    inputs = tokenizer_splade_doc(text, return_tensors="pt", padding=True, truncation=True)
    inputs = {k: v.to(model_splade_doc.device) for k, v in inputs.items()}

    with torch.no_grad():
        output = model_splade_doc(**inputs)

    if hasattr(output, "logits"):
        vocab_size = tokenizer_splade_doc.vocab_size
        binary_splade_vector = create_lexical_bow_mask(
            inputs['input_ids'], vocab_size, tokenizer_splade_doc
        ).squeeze()
        return binary_splade_vector
    return None


# --- Function to get formatted representation from a raw vector and tokenizer ---
# This function remains unchanged as it's a generic formatter for any sparse vector.
def format_sparse_vector_output(splade_vector, tokenizer, is_binary=False):
    if splade_vector is None:
        return "Failed to generate vector."

    indices = torch.nonzero(splade_vector).squeeze().cpu().tolist()
    if not isinstance(indices, list):
        indices = [indices] if indices else []

    if is_binary:
        values = [1.0] * len(indices)
    else:
        values = splade_vector[indices].cpu().tolist()
    
    token_weights = dict(zip(indices, values))

    meaningful_tokens = {}
    for token_id, weight in token_weights.items():
        decoded_token = tokenizer.decode([token_id])
        if decoded_token not in ["[CLS]", "[SEP]", "[PAD]", "[UNK]"] and len(decoded_token.strip()) > 0:
            meaningful_tokens[decoded_token] = weight

    if is_binary:
        sorted_representation = sorted(meaningful_tokens.items(), key=lambda item: item[0]) # Sort alphabetically for binary
    else:
        sorted_representation = sorted(meaningful_tokens.items(), key=lambda item: item[1], reverse=True)

    formatted_output = ""
    if not sorted_representation:
        formatted_output += "No significant terms found.\n"
    else:
        for i, (term, weight) in enumerate(sorted_representation):
            if i >= 50 and is_binary: # Limit display for very long binary lists
                formatted_output += f"...and {len(sorted_representation) - 50} more terms.\n"
                break
            if is_binary:
                formatted_output += f"- **{term}**\n"
            else:
                formatted_output += f"- **{term}**: {weight:.4f}\n"

    formatted_output += f"\nTotal non-zero terms: {len(indices)}\n"
    formatted_output += f"Sparsity: {1 - (len(indices) / tokenizer.vocab_size):.2%}\n"
    
    return formatted_output


# --- NEW/MODIFIED: Helper to get the correct vector function, tokenizer, and binary flag ---
def get_model_assets(model_choice_str):
    if model_choice_str == "SPLADE-cocondenser-distil (weighting and expansion)":
        return get_splade_cocondenser_vector, tokenizer_splade, False, "SPLADE-cocondenser-distil (Weighting and Expansion)"
    elif model_choice_str == "SPLADE-v3-Lexical (weighting)":
        return get_splade_lexical_vector, tokenizer_splade_lexical, False, "SPLADE-v3-Lexical (Weighting)"
    elif model_choice_str == "SPLADE-v3-Doc (binary)":
        return get_splade_doc_vector, tokenizer_splade_doc, True, "SPLADE-v3-Doc (Binary)"
    else:
        return None, None, False, "Unknown Model"

# --- MODIFIED: Dot Product Calculation Function for the new tab ---
def calculate_dot_product_and_representations_independent(query_model_choice, doc_model_choice, query_text, doc_text):
    query_vector_fn, query_tokenizer, query_is_binary, query_model_name_display = get_model_assets(query_model_choice)
    doc_vector_fn, doc_tokenizer, doc_is_binary, doc_model_name_display = get_model_assets(doc_model_choice)

    if query_vector_fn is None or doc_vector_fn is None:
        return "Please select valid models for both query and document encoding.", "", ""

    query_vector = query_vector_fn(query_text)
    doc_vector = doc_vector_fn(doc_text)

    if query_vector is None or doc_vector is None:
        return "Failed to generate one or both vectors. Please check model loading and input text.", "", ""

    # Calculate dot product
    # Ensure both vectors are on CPU before dot product to avoid device mismatch issues
    # and to ensure .item() works reliably for conversion to float.
    dot_product = float(torch.dot(query_vector.cpu(), doc_vector.cpu()).item())

    # Format representations
    query_rep_str = f"Query Representation ({query_model_name_display}):\n"
    query_rep_str += format_sparse_vector_output(query_vector, query_tokenizer, query_is_binary)
    
    doc_rep_str = f"Document Representation ({doc_model_name_display}):\n"
    doc_rep_str += format_sparse_vector_output(doc_vector, doc_tokenizer, doc_is_binary)

    # Combine output
    full_output = f"### Dot Product Score: {dot_product:.6f}\n\n"
    full_output += "---\n\n"
    full_output += f"{query_rep_str}\n\n---\n\n{doc_rep_str}"
    
    return full_output


# --- Gradio Interface Setup with Tabs ---
with gr.Blocks(title="SPLADE Demos") as demo:
    gr.Markdown("# 🌌 Sparse Encoder Playground") # Updated title
    gr.Markdown("Explore different SPLADE models and their sparse representation types, and calculate similarity between query and document representations.") # Updated description

    with gr.Tabs():
        with gr.TabItem("Sparse Representation"):
            gr.Markdown("### Produce a Sparse Representation of of an Input Text")
            gr.Interface(
                fn=predict_representation_explorer,
                inputs=[
                    gr.Radio(
                        [
                            "MLM encoder (SPLADE-cocondenser-distil)",
                            "MLP encoder (SPLADE-v3-lexical)",
                            "Binary Encoder"
                        ],
                        label="Choose Sparse Encoder",
                        value="SPLADE-cocondenser-distil (weighting and expansion)"
                    ),
                    gr.Textbox(
                        lines=5,
                        label="Enter your query or document text here:",
                        placeholder="e.g., Why is Padua the nicest city in Italy?"
                    )
                ],
                outputs=gr.Markdown(),
                allow_flagging="never",
                # live=True # Setting live=True might be slow for complex models on every keystroke
            )
        
        with gr.TabItem("Compare Encoders"): # NEW TAB
            gr.Markdown("### Calculate Dot Product Similarity between Query and Document")
            gr.Markdown("Select **independent** SPLADE models to encode your query and document, then see their sparse representations and their similarity score.")
            
            # Define the common model choices for cleaner code
            model_choices = [
                "SPLADE-cocondenser-distil (weighting and expansion)",
                "SPLADE-v3-Lexical (weighting)",
                "SPLADE-v3-Doc (binary)"
            ]

            gr.Interface(
                fn=calculate_dot_product_and_representations_independent, # MODIFIED FUNCTION NAME
                inputs=[
                    gr.Radio(
                        model_choices,
                        label="Choose Query Encoding Model",
                        value="SPLADE-cocondenser-distil (weighting and expansion)" # Default value
                    ),
                    gr.Radio(
                        model_choices,
                        label="Choose Document Encoding Model",
                        value="SPLADE-cocondenser-distil (weighting and expansion)" # Default value
                    ),
                    gr.Textbox(
                        lines=3,
                        label="Enter Query Text:",
                        placeholder="e.g., famous dishes of Padua"
                    ),
                    gr.Textbox(
                        lines=5,
                        label="Enter Document Text:",
                        placeholder="e.g., Padua's cuisine is as famous as its legendary University."
                    )
                ],
                outputs=gr.Markdown(),
                allow_flagging="never"
            )

demo.launch()