src/aibom_generator/inference_model.py

"""
Inference model implementation for metadata extraction from model cards.

This module provides a fine-tuned model for extracting structured metadata
from unstructured text in Hugging Face model cards.
"""

import json
import logging
import os
import re
import torch
from typing import Dict, List, Optional, Any, Union

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from transformers import AutoModelForSeq2SeqLM, T5Tokenizer

logger = logging.getLogger(__name__)


class ModelCardExtractor:
    """
    Fine-tuned model for extracting metadata from model card text.
    """
    
    def __init__(
        self,
        model_name: str = "distilbert-base-uncased",
        device: str = "cpu",
        max_length: int = 512,
        cache_dir: Optional[str] = None,
    ):
        """
        Initialize the model card extractor.
        
        Args:
            model_name: Name or path of the pre-trained model
            device: Device to run the model on ('cpu' or 'cuda')
            max_length: Maximum sequence length for tokenization
            cache_dir: Directory to cache models
        """
        self.model_name = model_name
        self.device = device
        self.max_length = max_length
        self.cache_dir = cache_dir
        
        # Load tokenizer and model
        self.tokenizer = None
        self.model = None
        
        # Initialize extraction pipelines
        self.section_classifier = None
        self.metadata_extractor = None
        
        # Load models
        self._load_models()
    
    def _load_models(self):
        """Load the required models for extraction."""
        try:
            # Load section classifier
            logger.info(f"Loading section classifier model: {self.model_name}")
            self.tokenizer = AutoTokenizer.from_pretrained(
                self.model_name,
                cache_dir=self.cache_dir,
            )
            self.model = AutoModelForSequenceClassification.from_pretrained(
                self.model_name,
                cache_dir=self.cache_dir,
            )
            self.model.to(self.device)
            
            # Create section classification pipeline
            self.section_classifier = pipeline(
                "text-classification",
                model=self.model,
                tokenizer=self.tokenizer,
                device=0 if self.device == "cuda" else -1,
            )
            
            # For demonstration purposes, we'll use a T5-based model for extraction
            # In a real implementation, this would be a fine-tuned model specific to the task
            logger.info("Loading metadata extraction model")
            extraction_model_name = "t5-small"  # Placeholder for fine-tuned model
            self.extraction_tokenizer = T5Tokenizer.from_pretrained(
                extraction_model_name,
                cache_dir=self.cache_dir,
            )
            self.extraction_model = AutoModelForSeq2SeqLM.from_pretrained(
                extraction_model_name,
                cache_dir=self.cache_dir,
            )
            self.extraction_model.to(self.device)
            
            logger.info("Models loaded successfully")
        except Exception as e:
            logger.error(f"Error loading models: {e}")
            raise
    
    def extract_metadata(
        self,
        text: str,
        fields: Optional[List[str]] = None,
    ) -> Dict[str, Any]:
        """
        Extract metadata from model card text.
        
        Args:
            text: The model card text
            fields: Optional list of specific fields to extract
            
        Returns:
            Extracted metadata as a dictionary
        """
        # Split text into sections
        sections = self._split_into_sections(text)
        
        # Classify sections
        classified_sections = self._classify_sections(sections)
        
        # Extract metadata from each section
        metadata = {}
        for section_type, section_text in classified_sections.items():
            if fields and section_type not in fields:
                continue
                
            extracted = self._extract_from_section(section_type, section_text)
            if extracted:
                metadata[section_type] = extracted
        
        return metadata
    
    def _split_into_sections(self, text: str) -> List[Dict[str, str]]:
        """
        Split the model card text into sections.
        
        Args:
            text: The model card text
            
        Returns:
            List of sections with title and content
        """
        # Simple section splitting based on headers
        # In a real implementation, this would be more sophisticated
        sections = []
        
        # Match markdown headers (# Header, ## Header, etc.)
        header_pattern = r"(?:^|\n)(#+)\s+(.*?)(?:\n|$)"
        
        # Find all headers
        headers = list(re.finditer(header_pattern, text))
        
        for i, match in enumerate(headers):
            header_level = len(match.group(1))
            header_text = match.group(2).strip()
            
            start = match.end()
            
            # Find the end of the section (next header or end of text)
            if i < len(headers) - 1:
                end = headers[i + 1].start()
            else:
                end = len(text)
            
            # Extract the section content
            content = text[start:end].strip()
            
            sections.append({
                "title": header_text,
                "level": header_level,
                "content": content,
            })
        
        # If no sections were found, treat the entire text as one section
        if not sections:
            sections.append({
                "title": "Main",
                "level": 1,
                "content": text.strip(),
            })
        
        return sections
    
    def _classify_sections(self, sections: List[Dict[str, str]]) -> Dict[str, str]:
        """
        Classify sections into metadata categories.
        
        Args:
            sections: List of sections with title and content
            
        Returns:
            Dictionary mapping section types to section content
        """
        classified = {}
        
        # Map common section titles to metadata fields
        title_mappings = {
            "model description": "description",
            "description": "description",
            "model details": "model_parameters",
            "model architecture": "model_parameters",
            "parameters": "model_parameters",
            "training data": "datasets",
            "dataset": "datasets",
            "datasets": "datasets",
            "training": "training_procedure",
            "evaluation": "evaluation_results",
            "results": "evaluation_results",
            "performance": "evaluation_results",
            "metrics": "evaluation_results",
            "limitations": "limitations",
            "biases": "ethical_considerations",
            "bias": "ethical_considerations",
            "ethical considerations": "ethical_considerations",
            "ethics": "ethical_considerations",
            "risks": "ethical_considerations",
            "license": "license",
            "citation": "citation",
            "references": "citation",
        }
        
        for section in sections:
            title = section["title"].lower()
            content = section["content"]
            
            # Check for direct title matches
            matched = False
            for key, value in title_mappings.items():
                if key in title:
                    if value not in classified:
                        classified[value] = content
                    else:
                        classified[value] += "\n\n" + content
                    matched = True
                    break
            
            # If no match by title, use the classifier
            if not matched and self.section_classifier and len(content.split()) > 5:
                try:
                    # This is a placeholder for actual classification
                    # In a real implementation, this would use the fine-tuned classifier
                    section_type = self._classify_text(content)
                    if section_type and section_type not in classified:
                        classified[section_type] = content
                    elif section_type:
                        classified[section_type] += "\n\n" + content
                except Exception as e:
                    logger.error(f"Error classifying section: {e}")
        
        return classified
    
    def _classify_text(self, text: str) -> Optional[str]:
        """
        Classify text into a metadata category.
        
        Args:
            text: The text to classify
            
        Returns:
            Metadata category or None if classification fails
        """
        # This is a placeholder for actual classification
        # In a real implementation, this would use the fine-tuned classifier
        
        # Simple keyword-based classification for demonstration
        keywords = {
            "description": ["is a", "this model", "based on", "pretrained"],
            "model_parameters": ["parameters", "layers", "hidden", "dimension", "architecture"],
            "datasets": ["dataset", "corpus", "trained on", "fine-tuned on"],
            "evaluation_results": ["accuracy", "f1", "precision", "recall", "performance"],
            "limitations": ["limitation", "limited", "does not", "cannot", "fails to"],
            "ethical_considerations": ["bias", "ethical", "fairness", "gender", "race"],
        }
        
        # Count keyword occurrences
        counts = {category: 0 for category in keywords}
        for category, words in keywords.items():
            for word in words:
                counts[category] += len(re.findall(r'\b' + re.escape(word) + r'\b', text.lower()))
        
        # Return the category with the most keyword matches
        if counts:
            max_category = max(counts.items(), key=lambda x: x[1])
            if max_category[1] > 0:
                return max_category[0]
        
        return None
    
    def _extract_from_section(self, section_type: str, text: str) -> Any:
        """
        Extract structured metadata from a section.
        
        Args:
            section_type: The type of section
            text: The section text
            
        Returns:
            Extracted metadata
        """
        # This is a placeholder for actual extraction
        # In a real implementation, this would use the fine-tuned extraction model
        
        if section_type == "description":
            # Simply return the text for description
            return text.strip()
        
        elif section_type == "model_parameters":
            # Extract model parameters using regex
            params = {}
            
            # Extract architecture
            arch_match = re.search(r'(?:architecture|model type|based on)[:\s]+([A-Za-z0-9\-]+)', text, re.IGNORECASE)
            if arch_match:
                params["architecture"] = arch_match.group(1).strip()
            
            # Extract parameter count
            param_match = re.search(r'(\d+(?:\.\d+)?)\s*(?:B|M|K)?\s*(?:billion|million|thousand)?\s*parameters', text, re.IGNORECASE)
            if param_match:
                params["parameter_count"] = param_match.group(1).strip()
            
            return params
        
        elif section_type == "datasets":
            # Extract dataset names
            datasets = []
            dataset_patterns = [
                r'trained on\s+(?:the\s+)?([A-Za-z0-9\-\s]+)(?:\s+dataset)?',
                r'dataset[:\s]+([A-Za-z0-9\-\s]+)',
                r'using\s+(?:the\s+)?([A-Za-z0-9\-\s]+)(?:\s+dataset)',
            ]
            
            for pattern in dataset_patterns:
                for match in re.finditer(pattern, text, re.IGNORECASE):
                    dataset = match.group(1).strip()
                    if dataset and dataset.lower() not in ["this", "these", "those"]:
                        datasets.append(dataset)
            
            return list(set(datasets))
        
        elif section_type == "evaluation_results":
            # Extract evaluation metrics
            results = {}
            
            # Extract accuracy
            acc_match = re.search(r'accuracy[:\s]+(\d+(?:\.\d+)?)\s*%?', text, re.IGNORECASE)
            if acc_match:
                results["accuracy"] = float(acc_match.group(1))
            
            # Extract F1 score
            f1_match = re.search(r'f1(?:\s*[\-_]?score)?[:\s]+(\d+(?:\.\d+)?)', text, re.IGNORECASE)
            if f1_match:
                results["f1"] = float(f1_match.group(1))
            
            # Extract precision
            prec_match = re.search(r'precision[:\s]+(\d+(?:\.\d+)?)', text, re.IGNORECASE)
            if prec_match:
                results["precision"] = float(prec_match.group(1))
            
            # Extract recall
            recall_match = re.search(r'recall[:\s]+(\d+(?:\.\d+)?)', text, re.IGNORECASE)
            if recall_match:
                results["recall"] = float(recall_match.group(1))
            
            return results
        
        elif section_type == "limitations":
            # Simply return the text for limitations
            return text.strip()
        
        elif section_type == "ethical_considerations":
            # Simply return the text for ethical considerations
            return text.strip()
        
        elif section_type == "license":
            # Extract license information
            license_match = re.search(r'(?:license|licensing)[:\s]+([A-Za-z0-9\-\s]+)', text, re.IGNORECASE)
            if license_match:
                return license_match.group(1).strip()
            return text.strip()
        
        elif section_type == "citation":
            # Simply return the text for citation
            return text.strip()
        
        # Default case
        return text.strip()


class InferenceModelServer:
    """
    Server for the inference model.
    
    This class provides a server for the inference model that can be deployed
    as a standalone service with a REST API.
    """
    
    def __init__(
        self,
        model_name: str = "distilbert-base-uncased",
        device: str = "cpu",
        cache_dir: Optional[str] = None,
    ):
        """
        Initialize the inference model server.
        
        Args:
            model_name: Name or path of the pre-trained model
            device: Device to run the model on ('cpu' or 'cuda')
            cache_dir: Directory to cache models
        """
        self.extractor = ModelCardExtractor(
            model_name=model_name,
            device=device,
            cache_dir=cache_dir,
        )
    
    def extract_metadata(
        self,
        text: str,
        structured_metadata: Optional[Dict[str, Any]] = None,
        fields: Optional[List[str]] = None,
    ) -> Dict[str, Any]:
        """
        Extract metadata from model card text.
        
        Args:
            text: The model card text
            structured_metadata: Optional structured metadata to provide context
            fields: Optional list of specific fields to extract
            
        Returns:
            Extracted metadata as a dictionary
        """
        try:
            # Extract metadata using the extractor
            metadata = self.extractor.extract_metadata(text, fields)
            
            # Enhance with structured metadata if provided
            if structured_metadata:
                # Use structured metadata for fields not extracted
                for key, value in structured_metadata.items():
                    if key not in metadata or not metadata[key]:
                        metadata[key] = value
            
            return {"metadata": metadata, "success": True}
        except Exception as e:
            logger.error(f"Error extracting metadata: {e}")
            return {"metadata": {}, "success": False, "error": str(e)}


def create_app(model_name: str = "distilbert-base-uncased", device: str = "cpu"):
    """
    Create a Flask app for the inference model server.
    
    Args:
        model_name: Name or path of the pre-trained model
        device: Device to run the model on ('cpu' or 'cuda')
        
    Returns:
        Flask app
    """
    from flask import Flask, request, jsonify
    
    app = Flask(__name__)
    server = InferenceModelServer(model_name=model_name, device=device)
    
    @app.route("/extract", methods=["POST"])
    def extract():
        data = request.json
        text = data.get("text", "")
        structured_metadata = data.get("structured_metadata", {})
        fields = data.get("fields", [])
        
        result = server.extract_metadata(text, structured_metadata, fields)
        return jsonify(result)
    
    @app.route("/health", methods=["GET"])
    def health():
        return jsonify({"status": "healthy"})
    
    return app


def main():
    """Main entry point for the inference model server."""
    import argparse
    
    parser = argparse.ArgumentParser(
        description="Start the inference model server for AIBOM metadata extraction."
    )
    
    parser.add_argument(
        "--model",
        help="Name or path of the pre-trained model",
        default="distilbert-base-uncased",
    )
    
    parser.add_argument(
        "--device",
        help="Device to run the model on ('cpu' or 'cuda')",
        choices=["cpu", "cuda"],
        default="cpu",
    )
    
    parser.add_argument(
        "--host",
        help="Host to bind the server to",
        default="0.0.0.0",
    )
    
    parser.add_argument(
        "--port",
        help="Port to bind the server to",
        type=int,
        default=5000,
    )
    
    parser.add_argument(
        "--debug",
        help="Enable debug mode",
        action="store_true",
    )
    
    args = parser.parse_args()
    
    # Create and run the app
    app = create_app(model_name=args.model, device=args.device)
    app.run(host=args.host, port=args.port, debug=args.debug)


if __name__ == "__main__":
    main()