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import logging
MAX_INPUT_CHARS = 6000 # Limit text length
def chunk_text(text, max_len=700):
words = text.split()
chunks = []
current = []
for word in words:
current.append(word)
if len(" ".join(current)) >= max_len:
chunks.append(" ".join(current))
current = []
if current:
chunks.append(" ".join(current))
return chunks[:3] # Limit to first 3 chunks
def get_summary(text):
logging.info("=== AutoTLDR Incoming Request ===")
logging.info(f"Raw input (first 500 chars):\n{text[:500]}")
logging.info(f"Total input length: {len(text)} characters")
if len(text) > MAX_INPUT_CHARS:
logging.warning("Input too long! Aborting.")
raise ValueError("Text too long to summarize. Please try a shorter page.")
from transformers import pipeline
summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-samsum")
summaries = []
chunks = chunk_text(text)
for i, chunk in enumerate(chunks):
logging.info(f"Summarizing chunk {i+1}/{len(chunks)} (length: {len(chunk)})")
try:
result = summarizer(chunk, max_length=120, min_length=20, do_sample=False)
summaries.append(result[0]['summary_text'])
logging.info(f"Chunk {i+1} summary: {result[0]['summary_text']}")
except Exception as e:
logging.exception(f"Error summarizing chunk {i+1}")
summaries.append("...")
final_summary = "\n".join(summaries)
logging.info("=== Final Summary Output ===")
logging.info(final_summary)
return final_summary
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