Update summarizer.py

summarizer.py

@@ -1,154 +1,155 @@
-import logging
-import nltk
-import torch
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-# Configure logger to print to console
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
-
-# Download necessary NLTK data (if not already downloaded)
-nltk.download('punkt')
-
-class Summarizer:
-    def __init__(self, model_path, tokenizer_path):
-        """
-        Initialize the summarizer with a fine-tuned model and tokenizer.
-        Both model and tokenizer are loaded from the same directory.
-        """
-        logger.info(f"Initializing Summarizer with model_path: {model_path} and tokenizer_path: {tokenizer_path}")
-        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
-        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
-        self.tokenizer_path = tokenizer_path
-        
-        # Set device to GPU if available
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.model.to(self.device)
-        logger.info(f"Model loaded on device: {self.device}")
-    
-    def model_summarize(self, text_chunk, 
-                        max_length=200, 
-                        min_length=30,
-                        num_beams=4, 
-                        temperature=0.7, 
-                        top_k=50, 
-                        top_p=0.95):
-        """
-        Summarizes a text chunk using the fine-tuned model.
-        The prompt instructs the model to include explicit noun references.
-        """
-        logger.info(f"Summarizing text chunk of {len(text_chunk.split())} words.")
-        
-        # Re-load tokenizer from the given path (as in original code)
-        self.tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_path)
-        input_text = "summarize : " + text_chunk
-        inputs = self.tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
-        inputs = {k: v.to(self.device) for k, v in inputs.items()}
-        
-        with torch.no_grad():
-            output_ids = self.model.generate(
-                **inputs,
-                max_length=max_length,
-                num_beams=num_beams,
-                temperature=temperature,
-                top_k=top_k,
-                top_p=top_p,
-                do_sample=True,
-                early_stopping=True
-            )
-        
-        summary = self.tokenizer.decode(output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
-        logger.info("Summary generated.")
-        return summary
-
-    def split_into_sentences(self, text):
-        """
-        Splits the text into sentences using NLTK.
-        """
-        sentences = nltk.sent_tokenize(text)
-        logger.info(f"Text split into {len(sentences)} sentences.")
-        return sentences
-    
-    def chunk_sentences(self, sentences):
-        """
-        Groups sentences into chunks.
-        Each chunk contains as many sentences as possible while keeping its total word count below 300.
-        Only chunks with at least 50 words are kept; chunks with fewer words are discarded.
-        """
-        logger.info("Starting sentence chunking.")
-        chunks = []
-        current_chunk = []
-        current_word_count = 0
-
-        for sentence in sentences:
-            sentence_word_count = len(sentence.split())
-            
-            # If adding this sentence keeps the chunk under 300 words, add it.
-            if current_word_count + sentence_word_count <= 300:
-                current_chunk.append(sentence)
-                current_word_count += sentence_word_count
-            else:
-                # If current chunk meets the minimum word requirement, add it to the chunks list.
-                if current_word_count >= 50:
-                    chunks.append(" ".join(current_chunk))
-                    logger.info(f"Created a chunk with {current_word_count} words.")
-                # Start a new chunk with the current sentence.
-                current_chunk = [sentence]
-                current_word_count = sentence_word_count
-
-        # After the loop, add the last chunk if it meets the minimum requirement.
-        if current_word_count >= 75:
-            chunks.append(" ".join(current_chunk))
-            logger.info(f"Final chunk created with {current_word_count} words.")
-        
-        logger.info(f"Total chunks created: {len(chunks)}")
-        return chunks
-    
-    def recursive_summarize(self, text, threshold=50):
-        """
-        Recursively summarizes the text until its word count is below the threshold.
-        If the combined summary consists of a single sentence (even if its length is above the threshold),
-        the recursion stops.
-        """
-        logger.info(f"Recursive summarization called on text with {len(text.split())} words.")
-        if len(text.split()) <= threshold:
-            logger.info("Text is below the threshold; returning original text.")
-            return text
-
-        sentences = self.split_into_sentences(text)
-        if not sentences:
-            logger.warning("No sentences found; returning original text.")
-            return text  # Edge case if sentence splitting fails
-
-        chunks = self.chunk_sentences(sentences)
-        logger.info("Generating summaries for each chunk.")
-        summaries = [self.model_summarize(chunk) for chunk in chunks]
-        combined_summary = " ".join(summaries)
-        logger.info(f"Combined summary length: {len(combined_summary.split())} words.")
-
-        # Check if the combined summary is a single sentence; if so, stop recursion.
-        summary_sentences = self.split_into_sentences(combined_summary)
-        if len(summary_sentences) == 1:
-            logger.info("Combined summary consists of a single sentence; returning summary without further recursion.")
-            return combined_summary
-
-        if len(combined_summary.split()) > threshold:
-            logger.info("Combined summary exceeds threshold; recursing further.")
-            return self.recursive_summarize(combined_summary, threshold)
-        else:
-            logger.info("Combined summary meets threshold; summarization complete.")
-            return combined_summary
-
-    def iterative_summarization(self, text, threshold=50):
-        """
-        Alias for recursive_summarize to maintain compatibility with fetch_top_news.py.
-        """
-        logger.info("Starting iterative summarization.")
-        return self.recursive_summarize(text, threshold)
-
-# if __name__ == "__main__":
-#     # Example test block to verify functionality.
-#     text = """Your test text here."""
-#     summarizer = Summarizer("beta./model", "beta./model")
-#     final_summary = summarizer.iterative_summarization(text, threshold=50)
-#     print(final_summary)
+import logging
+import nltk
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Configure logger to print to console
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Download necessary NLTK data (if not already downloaded)
+nltk.download('punkt')
+
+class Summarizer:
+    def __init__(self, model_path, tokenizer_path):
+        """
+        Initialize the summarizer with a fine-tuned model and tokenizer.
+        Both model and tokenizer are loaded from the same directory.
+        """
+        logger.info(f"Initializing Summarizer with model_path: {model_path} and tokenizer_path: {tokenizer_path}")
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
+        self.tokenizer_path = tokenizer_path
+        
+        # Set device to GPU if available
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.model.to(self.device)
+        logger.info(f"Model loaded on device: {self.device}")
+    
+    def model_summarize(self, text_chunk, 
+                        max_length=200, 
+                        min_length=30,
+                        num_beams=4, 
+                        temperature=0.7, 
+                        top_k=50, 
+                        top_p=0.95):
+        """
+        Summarizes a text chunk using the fine-tuned model.
+        The prompt instructs the model to include explicit noun references.
+        """
+        logger.info(f"Summarizing text chunk of {len(text_chunk.split())} words.")
+        
+        # Re-load tokenizer from the given path (as in original code)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_path)
+        input_text = "summarize : " + text_chunk
+        inputs = self.tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
+        inputs = {k: v.to(self.device) for k, v in inputs.items()}
+        
+        with torch.no_grad():
+            output_ids = self.model.generate(
+                **inputs,
+                max_length=max_length,
+                min_length=30
+                num_beams=num_beams,
+                temperature=temperature,
+                top_k=top_k,
+                top_p=top_p,
+                do_sample=True,
+                early_stopping=True
+            )
+        
+        summary = self.tokenizer.decode(output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
+        logger.info("Summary generated.")
+        return summary
+
+    def split_into_sentences(self, text):
+        """
+        Splits the text into sentences using NLTK.
+        """
+        sentences = nltk.sent_tokenize(text)
+        logger.info(f"Text split into {len(sentences)} sentences.")
+        return sentences
+    
+    def chunk_sentences(self, sentences):
+        """
+        Groups sentences into chunks.
+        Each chunk contains as many sentences as possible while keeping its total word count below 300.
+        Only chunks with at least 50 words are kept; chunks with fewer words are discarded.
+        """
+        logger.info("Starting sentence chunking.")
+        chunks = []
+        current_chunk = []
+        current_word_count = 0
+
+        for sentence in sentences:
+            sentence_word_count = len(sentence.split())
+            
+            # If adding this sentence keeps the chunk under 300 words, add it.
+            if current_word_count + sentence_word_count <= 300:
+                current_chunk.append(sentence)
+                current_word_count += sentence_word_count
+            else:
+                # If current chunk meets the minimum word requirement, add it to the chunks list.
+                if current_word_count >= 75:
+                    chunks.append(" ".join(current_chunk))
+                    logger.info(f"Created a chunk with {current_word_count} words.")
+                # Start a new chunk with the current sentence.
+                current_chunk = [sentence]
+                current_word_count = sentence_word_count
+
+        # After the loop, add the last chunk if it meets the minimum requirement.
+        if current_word_count >= 75:
+            chunks.append(" ".join(current_chunk))
+            logger.info(f"Final chunk created with {current_word_count} words.")
+        
+        logger.info(f"Total chunks created: {len(chunks)}")
+        return chunks
+    
+    def recursive_summarize(self, text, threshold=75):
+        """
+        Recursively summarizes the text until its word count is below the threshold.
+        If the combined summary consists of a single sentence (even if its length is above the threshold),
+        the recursion stops.
+        """
+        logger.info(f"Recursive summarization called on text with {len(text.split())} words.")
+        if len(text.split()) <= threshold:
+            logger.info("Text is below the threshold; returning original text.")
+            return text
+
+        sentences = self.split_into_sentences(text)
+        if not sentences:
+            logger.warning("No sentences found; returning original text.")
+            return text  # Edge case if sentence splitting fails
+
+        chunks = self.chunk_sentences(sentences)
+        logger.info("Generating summaries for each chunk.")
+        summaries = [self.model_summarize(chunk) for chunk in chunks]
+        combined_summary = " ".join(summaries)
+        logger.info(f"Combined summary length: {len(combined_summary.split())} words.")
+
+        # Check if the combined summary is a single sentence; if so, stop recursion.
+        summary_sentences = self.split_into_sentences(combined_summary)
+        if len(summary_sentences) == 1:
+            logger.info("Combined summary consists of a single sentence; returning summary without further recursion.")
+            return combined_summary
+
+        if len(combined_summary.split()) > threshold:
+            logger.info("Combined summary exceeds threshold; recursing further.")
+            return self.recursive_summarize(combined_summary, threshold)
+        else:
+            logger.info("Combined summary meets threshold; summarization complete.")
+            return combined_summary
+
+    def iterative_summarization(self, text, threshold=75):
+        """
+        Alias for recursive_summarize to maintain compatibility with fetch_top_news.py.
+        """
+        logger.info("Starting iterative summarization.")
+        return self.recursive_summarize(text, threshold)
+
+# if __name__ == "__main__":
+#     # Example test block to verify functionality.
+#     text = """Your test text here."""
+#     summarizer = Summarizer("beta./model", "beta./model")
+#     final_summary = summarizer.iterative_summarization(text, threshold=50)
+#     print(final_summary)