MCP Document Processor

from typing import Optional import logging import re logger = logging.getLogger(__name__) class T5Summarizer: """Document summarizer using T5 model.""" def __init__(self, model_name: str, max_length: int = 150): self.model_name = model_name self.max_length = max_length self.last_confidence = 0.0 logger.info(f"Initialized mock T5Summarizer with model: {model_name}") def predict(self, text: str) -> str: """Generate a prediction (summary).""" return self.summarize(text) def summarize(self, text: str, max_length: Optional[int] = None) -> str: """Generate a summary of the text using mock implementation.""" if max_length is None: max_length = self.max_length try: # Mock implementation - extract key sentences # Split text into sentences sentences = re.split(r'(?<=[.!?])\s+', text) if not sentences: return "" # Simple extractive summarization # 1. Take the first sentence (often contains the main point) summary_sentences = [sentences[0]] # 2. Look for sentences with important keywords important_keywords = ["important", "significant", "key", "main", "critical", "essential", "conclusion", "therefore", "result", "summary", "finally"] for sentence in sentences[1:]: # Add sentences with important keywords if any(keyword in sentence.lower() for keyword in important_keywords): summary_sentences.append(sentence) # Stop if we've reached a reasonable length if len(" ".join(summary_sentences)) >= max_length: break # 3. If summary is still too short, add more sentences from the beginning if len(summary_sentences) < 3 and len(sentences) > 3: for sentence in sentences[1:4]: # Add 2nd and 3rd sentences if needed if sentence not in summary_sentences: summary_sentences.append(sentence) if len(" ".join(summary_sentences)) >= max_length: break # Combine sentences and truncate to max_length summary = " ".join(summary_sentences) if len(summary) > max_length: summary = summary[:max_length-3] + "..." # Set a fixed confidence for summarization self.last_confidence = 0.8 return summary except Exception as e: logger.error(f"Error in summarization: {str(e)}") self.last_confidence = 0.0 return "" def get_confidence(self) -> float: """Get the confidence score of the last prediction.""" return self.last_confidence