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import numpy as np # Import NumPy first
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import time
import os
import re
class SummariserService:
def __init__(self):
# Status tracking
self.model_loading_status = {
"is_loading": False,
"step": "",
"progress": 0
}
# Consider these alternative models
model_options = {
"general": "facebook/bart-large-cnn",
"news": "facebook/bart-large-xsum",
"long_form": "google/pegasus-large",
"literary": "t5-large"
}
# Choose the most appropriate model
model_name = model_options["literary"] # Better for literary text
# Update loading status
self.model_loading_status["is_loading"] = True
self.model_loading_status["step"] = "Initializing tokenizer"
# Ensure cache directory exists and is writable
cache_dir = os.environ.get("TRANSFORMERS_CACHE", "/tmp/huggingface_cache")
os.makedirs(cache_dir, exist_ok=True)
try:
self.tokenizer = AutoTokenizer.from_pretrained(
model_name,
cache_dir=cache_dir,
local_files_only=False
)
self.model_loading_status["step"] = "Loading model"
self.model = AutoModelForSeq2SeqLM.from_pretrained(
model_name,
cache_dir=cache_dir,
force_download=False,
local_files_only=False
)
# Move to GPU if available
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model.to(self.device)
except Exception as e:
# Fallback to a smaller model if the main one fails
print(f"Error loading model {model_name}: {str(e)}")
print("Falling back to smaller model...")
fallback_model = "sshleifer/distilbart-cnn-6-6" # Much smaller model
self.tokenizer = AutoTokenizer.from_pretrained(
fallback_model,
cache_dir=cache_dir,
local_files_only=False
)
self.model = AutoModelForSeq2SeqLM.from_pretrained(
fallback_model,
cache_dir=cache_dir,
force_download=False,
local_files_only=False
)
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model.to(self.device)
# Update model name for metadata
model_name = fallback_model
self.model_loading_status["is_loading"] = False
self.model_loading_status["progress"] = 100
# Store the actual model name used
self.model_name = model_name
# Track current processing job
self.current_job = {
"in_progress": False,
"start_time": None,
"input_word_count": 0,
"estimated_time": 0,
"stage": "",
"progress": 0
}
def clean_summary(self, summary):
"""Clean and format the summary text"""
# Remove any leading punctuation or spaces
summary = re.sub(r'^[,.\s]+', '', summary)
# Ensure the first letter is capitalized
if summary and len(summary) > 0:
summary = summary[0].upper() + summary[1:]
# Ensure proper ending punctuation
if summary and not any(summary.endswith(end) for end in ['.', '!', '?']):
last_sentence_end = max(
summary.rfind('.'),
summary.rfind('!'),
summary.rfind('?')
)
if last_sentence_end > 0:
summary = summary[:last_sentence_end + 1]
else:
summary = summary + '.'
return summary
def get_status(self):
"""Return the current status of the summarizer service"""
status = {
"model_loading": self.model_loading_status,
"device": self.device,
"current_job": self.current_job
}
# Update estimated time remaining if job in progress
if self.current_job["in_progress"] and self.current_job["start_time"]:
elapsed = time.time() - self.current_job["start_time"]
estimated = self.current_job["estimated_time"]
remaining = max(0, estimated - elapsed)
status["current_job"]["time_remaining"] = round(remaining, 1)
# Update progress based on time
if estimated > 0:
progress = min(95, (elapsed / estimated) * 100)
status["current_job"]["progress"] = round(progress, 0)
return status
def summarise(self, text, max_length=250, min_length=100, do_sample=True, temperature=1.2):
"""
Summarise the given text using the loaded model.
Args:
text (str): The text to summarise
max_length (int): Maximum length of the summary in characters
min_length (int): Minimum length of the summary in characters
do_sample (bool): Whether to use sampling for generation
temperature (float): Sampling temperature (higher = more random)
Returns:
dict: The generated summary and processing metadata
"""
# Reset and start job tracking
self.current_job = {
"in_progress": True,
"start_time": time.time(),
"input_word_count": len(text.split()),
"estimated_time": max(1, min(30, len(text.split()) / 500)), # Rough estimate
"stage": "Tokenizing input text",
"progress": 5
}
result = {
"summary": "",
"metadata": {
"input_word_count": self.current_job["input_word_count"],
"estimated_time_seconds": self.current_job["estimated_time"],
"model_used": self.model_name,
"processing_device": self.device
}
}
try:
# Tokenization step
inputs = self.tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
input_ids = inputs.input_ids.to(self.device)
# Update metadata with token info
result["metadata"]["input_token_count"] = len(input_ids[0])
result["metadata"]["truncated"] = len(input_ids[0]) == 1024
# Update job status
self.current_job["stage"] = "Generating summary"
self.current_job["progress"] = 30
# Enhanced generation parameters
summary_ids = self.model.generate(
input_ids,
max_length=max_length,
min_length=min_length,
do_sample=do_sample,
temperature=temperature,
num_beams=4,
early_stopping=True,
no_repeat_ngram_size=3,
length_penalty=2.0,
)
# Update job status
self.current_job["stage"] = "Post-processing summary"
self.current_job["progress"] = 90
summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True)
# Clean and format the summary
summary = self.clean_summary(summary)
result["summary"] = summary
result["metadata"]["output_word_count"] = len(summary.split())
result["metadata"]["compression_ratio"] = round(len(summary.split()) / self.current_job["input_word_count"] * 100, 1)
except Exception as e:
# Handle errors gracefully
print(f"Error during summarization: {str(e)}")
result["summary"] = "An error occurred during summarization. Please try again with a shorter text or different parameters."
result["error"] = str(e)
finally:
# Complete job
self.current_job["in_progress"] = False
self.current_job["stage"] = "Complete"
self.current_job["progress"] = 100
return result
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