Fix summarization issues and improve GPU handling. Update .gitignore for venv

utils/summarization.py

@@ -1,8 +1,113 @@
-from transformers import pipeline
+from transformers import pipeline, AutoTokenizer
+import torch
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 SUMMARY_MODEL = "Falconsai/text_summarization"
 
-def summarize_text(text):
-    """Summarize text using a Hugging Face pipeline."""
-    summarizer = pipeline("summarization", model=SUMMARY_MODEL)
-    return summarizer(text, max_length=150, min_length=30, do_sample=False)[0]["summary_text"]
+def chunk_text(text, max_tokens, tokenizer):
+    """
+    Splits the text into a list of chunks based on token limits.
+    
+    Args:
+        text (str): Text to chunk
+        max_tokens (int): Maximum tokens per chunk
+        tokenizer (AutoTokenizer): Tokenizer to use
+        
+    Returns:
+        list: List of text chunks
+    """
+    words = text.split()
+    chunks = []
+    current_chunk = []
+    current_length = 0
+    
+    for word in words:
+        hypothetical_length = current_length + len(tokenizer(word, return_tensors='pt')['input_ids'][0]) - 2
+        if hypothetical_length <= max_tokens:
+            current_chunk.append(word)
+            current_length = hypothetical_length
+        else:
+            chunks.append(' '.join(current_chunk))
+            current_chunk = [word]
+            current_length = len(tokenizer(word, return_tensors='pt')['input_ids'][0]) - 2
+    
+    if current_chunk:
+        chunks.append(' '.join(current_chunk))
+    
+    return chunks
+
+def summarize_text(text, use_gpu=True, memory_fraction=0.8):
+    """
+    Summarize text using a Hugging Face pipeline with chunking support.
+    
+    Args:
+        text (str): Text to summarize
+        use_gpu (bool): Whether to use GPU if available
+        memory_fraction (float): Fraction of GPU memory to use
+    
+    Returns:
+        str: Summarized text
+    """
+    # Determine device
+    device = -1  # Default to CPU
+    if use_gpu and torch.cuda.is_available():
+        device = 0  # Use first GPU
+        if torch.cuda.is_available():
+            torch.cuda.set_per_process_memory_fraction(memory_fraction)
+    
+    logger.info(f"Using device {device} for summarization")
+    
+    try:
+        # Initialize the pipeline and tokenizer
+        summarizer = pipeline("summarization", model=SUMMARY_MODEL, device=device)
+        tokenizer = AutoTokenizer.from_pretrained(SUMMARY_MODEL)
+        
+        # Check if text needs to be chunked
+        max_tokens = 512
+        tokens = tokenizer(text, return_tensors='pt')
+        num_tokens = len(tokens['input_ids'][0])
+        
+        if num_tokens > max_tokens:
+            chunks = chunk_text(text, max_tokens, tokenizer)
+            summaries = []
+            
+            for i, chunk in enumerate(chunks):
+                logger.info(f"Summarizing chunk {i+1}/{len(chunks)}")
+                summary_output = summarizer(
+                    "summarize: " + chunk,
+                    max_length=150,
+                    min_length=30,
+                    do_sample=False
+                )
+                summaries.append(summary_output[0]['summary_text'])
+            
+            # If multiple chunks, summarize the combined summaries
+            if len(summaries) > 1:
+                logger.info("Generating final summary from chunk summaries")
+                combined_text = " ".join(summaries)
+                return summarizer(
+                    "summarize: " + combined_text,
+                    max_length=150,
+                    min_length=30,
+                    do_sample=False
+                )[0]['summary_text']
+            return summaries[0]
+        else:
+            return summarizer(
+                "summarize: " + text,
+                max_length=150,
+                min_length=30,
+                do_sample=False
+            )[0]['summary_text']
+            
+    except Exception as e:
+        logger.error(f"Error during summarization: {e}")
+        # Fallback to CPU if GPU fails
+        if device != -1:
+            logger.info("Falling back to CPU")
+            return summarize_text(text, use_gpu=False, memory_fraction=memory_fraction)
+        raise