app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForMaskedLM, AutoModel
import torch

# --- Model Loading ---
tokenizer_splade = None
model_splade = None
tokenizer_unicoil = None
model_unicoil = None

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

# Load UNICOIL model for binary sparse encoding
try:
    # UNICOIL models are typically just AutoModel as they add a linear layer
    # on top of a BERT-like encoder to predict weights.
    # 'castorini/unicoil-msmarco-passage' is a common UNICOIL checkpoint.
    unicoil_model_name = "castorini/unicoil-msmarco-passage"
    tokenizer_unicoil = AutoTokenizer.from_pretrained(unicoil_model_name)
    model_unicoil = AutoModel.from_pretrained(unicoil_model_name)
    model_unicoil.eval() # Set to evaluation mode for inference
    print(f"UNICOIL model '{unicoil_model_name}' loaded successfully!")
except Exception as e:
    print(f"Error loading UNICOIL model: {e}")
    print(f"Please ensure '{unicoil_model_name}' is accessible (check Hugging Face Hub for potential agreements).")


# --- Core Representation Functions ---

def get_splade_representation(text):
    if tokenizer_splade is None or model_splade is None:
        return "SPLADE 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()
    else:
        return "Model output structure not as expected for SPLADE. 'logits' not found."

    indices = torch.nonzero(splade_vector).squeeze().cpu().tolist()
    if not isinstance(indices, list):
        indices = [indices]
            
    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 Representation (Top 20 Terms):\n"
    if not sorted_representation:
        formatted_output += "No significant terms found for this input.\n"
    else:
        for i, (term, weight) in enumerate(sorted_representation):
            if i >= 20:
                break
            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_unicoil_binary_representation(text):
    if tokenizer_unicoil is None or model_unicoil is None:
        return "UNICOIL model is not loaded. Please check the console for loading errors."

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

    with torch.no_grad():
        # UNICOIL models often output a dictionary where 'token_scores' or similar
        # contain the learned weights for each token. The structure can vary.
        # For 'castorini/unicoil-msmarco-passage', the token scores are typically
        # the last hidden state of the model, which is then mapped by a linear layer
        # into the sparse weights. We might need to manually extract those,
        # or the model itself might be set up to produce the weights directly.
        # Based on typical UNICOIL implementations, we usually take the output
        # from the last layer and map it to vocabulary size.
        
        # In many UNICOIL variations, the model itself is designed to output
        # the "re-weight" scores for each token. Let's assume for simplicity
        # that the model's forward pass returns something that can be interpreted
        # as per-token scores, perhaps in `output.last_hidden_state`
        # and then a simple linear layer (not part of the AutoModel usually)
        # would project this to vocab size.
        # For simplicity and to fit `AutoModel`, we'll treat the last hidden state
        # directly as the basis for term importance for now, which is common in similar models,
        # or if the model already has a head, we use it.
        
        # A more robust UNICOIL implementation would involve a specific head
        # if not using AutoModelForMaskedLM. However, AutoModel gives us the
        # last hidden states from which we can infer.
        
        # For UNICOIL, we're interested in the weighted token scores.
        # `model(**inputs)` will typically return a `BaseModelOutput`
        # or `MaskedLMOutput` if it's based on an MLM.
        # Let's assume the model's output provides the token importance.
        
        # A common way to get UNICOIL scores if not explicitly provided as logits:
        # It's usually a linear layer on top of the last hidden state.
        # Since AutoModel just gives the base model, we'll mimic the output
        # as a direct mapping if the model doesn't have a specific head for scores.
        # However, looking at `castorini/unicoil-msmarco-passage`
        # its `config.json` might give hints or the model itself is structured.
        # Often, it uses `BertForMaskedLM` and then applies `log(1+relu)` to the logits.
        # Let's assume it behaves similar to SPLADE for simplicity of extraction for now,
        # or we might need to load it as `AutoModelForMaskedLM` if its internal structure
        # is indeed like that, and then apply a binarization.
        
        # Re-evaluating: UNICOIL typically *learns* explicit token weights.
        # The common approach for UNICOIL with Hugging Face is indeed to load it
        # as `AutoModelForMaskedLM` and use its `logits` output, similar to SPLADE,
        # but with a different aggregation strategy.
        # Let's verify the model type for 'castorini/unicoil-msmarco-passage'.
        # Its config.json and architecture implies it's a BertForMaskedLM variant.

        output = model_unicoil(**inputs) # This should be a BaseModelOutputWithPooling or similar
        
        if not hasattr(output, 'logits'):
            # If `model_unicoil` is an `AutoModel` without a classification head,
            # we need to add a way to get per-token scores.
            # This is where a custom model head or a specific model class would be needed.
            # For `castorini/unicoil-msmarco-passage`, it *is* an MLM variant.
            # So, `output.logits` *should* be available.
            return "UNICOIL model output structure not as expected. 'logits' not found."

        # UNICOIL's output is also typically per-token scores from the MLM head.
        # For UNICOIL, the weights are often taken directly from the logits after pooling.
        # Unlike SPLADE's log(1+ReLU), UNICOIL's approach can be simpler,
        # sometimes just taking the maximum of logits (or similar pooling).
        # A common binarization for UNICOIL is based on the sign of the re-weighted scores.
        
        # Let's mimic a common UNICOIL interpretation for obtaining sparse weights
        # from the logits. The weights are usually sparse and positive.
        # We can apply a threshold for binarization.
        
        # This is a simplification; actual UNICOIL might have specific layers.
        # For `castorini/unicoil-msmarco-passage`, it uses the `log(1+exp(logits))` formulation
        # followed by max pooling, then often binarization based on a threshold.
        
        # Applying a common interpretation of UNICOIL-like score generation for sparse weights:
        # Instead of `log(1+ReLU(logits))`, it often uses `torch.log(1 + torch.exp(output.logits))`.
        # This is essentially the softplus function, which makes values positive and sparse.
        
        # Get the sparse weights using the UNICOIL-like transformation
        sparse_weights = torch.max(torch.log(1 + torch.exp(output.logits)) * inputs['attention_mask'].unsqueeze(-1), dim=1)[0].squeeze()

        # --- Binarization Step for UNICOIL ---
        # For true "binary sparse", we threshold these sparse weights.
        # A common approach is to simply take any non-zero value as 1, and zero as 0.
        # Or, define a small threshold for binarization if values are very small but non-zero.
        # For simplicity, let's treat anything above a very small epsilon as 1.
        
        # Convert to binary: 1 if weight > epsilon, else 0
        threshold = 1e-6 # Define a small threshold for binarization
        binary_sparse_vector = (sparse_weights > threshold).int()
        
        # Get indices of the '1's in the binary vector
        binary_indices = torch.nonzero(binary_sparse_vector).squeeze().cpu().tolist()
        
        if not isinstance(binary_indices, list):
            binary_indices = [binary_indices] if binary_indices.numel() > 0 else []

        # Map token IDs back to terms for the binary representation
        binary_terms = {}
        for token_id in binary_indices:
            decoded_token = tokenizer_unicoil.decode([token_id])
            if decoded_token not in ["[CLS]", "[SEP]", "[PAD]", "[UNK]"] and len(decoded_token.strip()) > 0:
                binary_terms[decoded_token] = 1 # Value is always 1 for binary

        sorted_binary_terms = sorted(binary_terms.items(), key=lambda item: item[0]) # Sort by term for consistent display

        formatted_output = "UNICOIL Binary Sparse Representation (Activated Terms):\n"
        if not sorted_binary_terms:
            formatted_output += "No significant terms activated for this input.\n"
        else:
            # Display up to 50 activated terms for readability
            for i, (term, _) in enumerate(sorted_binary_terms):
                if i >= 50:
                    break
                formatted_output += f"- **{term}**\n" # Only show term, as weight is always 1
            if len(sorted_binary_terms) > 50:
                formatted_output += f"...and {len(sorted_binary_terms) - 50} more terms.\n"
        
        formatted_output += "\n--- Raw Binary Sparse Vector Info ---\n"
        formatted_output += f"Total activated terms: {len(binary_indices)}\n"
        # Calculate sparsity based on the number of '1's vs. total vocabulary size
        formatted_output += f"Sparsity: {1 - (len(binary_indices) / tokenizer_unicoil.vocab_size):.2%}\n"

    return formatted_output


# --- Unified Prediction Function for Gradio ---
def predict_representation(model_choice, text):
    if model_choice == "SPLADE":
        return get_splade_representation(text)
    elif model_choice == "UNICOIL (Binary Sparse)":
        return get_unicoil_binary_representation(text)
    else:
        return "Please select a model."

# --- Gradio Interface Setup ---
demo = gr.Interface(
    fn=predict_representation,
    inputs=[
        gr.Radio(
            ["SPLADE", "UNICOIL (Binary Sparse)"], # Added UNICOIL option
            label="Choose Representation Model",
            value="SPLADE" # Default selection
        ),
        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(),
    title="🌌 Sparse and Binary Sparse Representation Generator",
    description="Enter any text to see its SPLADE sparse vector or UNICOIL binary sparse representation.",
    allow_flagging="never"
)

# Launch the Gradio app
demo.launch()