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import os
import hashlib
import tempfile
import streamlit as st
import logging
import time
from datetime import datetime
from pathlib import Path
from structured_ocr import StructuredOCR
from utils import generate_cache_key, timing, format_timestamp, create_descriptive_filename, extract_subject_tags
from preprocessing import apply_preprocessing_to_file
from error_handler import handle_ocr_error, check_file_size
# Configure logging
logger = logging.getLogger("ocr_processing")
logger.setLevel(logging.INFO)
@st.cache_data(ttl=24*3600, max_entries=20, show_spinner=False)
def process_file_cached(file_path, file_type, use_vision, file_size_mb, cache_key, preprocessing_options_hash=None):
"""
Cached version of OCR processing to reuse results
Args:
file_path: Path to the file to process
file_type: Type of file (pdf or image)
use_vision: Whether to use vision model
file_size_mb: File size in MB
cache_key: Cache key for the file
preprocessing_options_hash: Hash of preprocessing options
Returns:
dict: OCR result
"""
# Initialize OCR processor
processor = StructuredOCR()
# Process the file
with timing(f"OCR processing of {file_type} file"):
result = processor.process_file(
file_path,
file_type=file_type,
use_vision=use_vision,
file_size_mb=file_size_mb
)
return result
def process_file(uploaded_file, use_vision=True, preprocessing_options=None, progress_reporter=None,
pdf_dpi=150, max_pages=3, pdf_rotation=0, custom_prompt=None, perf_mode="Quality"):
"""
Process the uploaded file and return the OCR results
Args:
uploaded_file: The uploaded file to process
use_vision: Whether to use vision model
preprocessing_options: Dictionary of preprocessing options
progress_reporter: ProgressReporter instance for UI updates
pdf_dpi: DPI for PDF conversion
max_pages: Maximum number of pages to process
pdf_rotation: PDF rotation value
custom_prompt: Custom prompt for OCR
perf_mode: Performance mode (Quality or Speed)
Returns:
dict: OCR result
"""
if preprocessing_options is None:
preprocessing_options = {}
# Create a container for progress indicators if not provided
if progress_reporter is None:
from ui_components import ProgressReporter
progress_reporter = ProgressReporter(st.empty()).setup()
# Initialize temporary file paths list
temp_file_paths = []
try:
# Check if file size exceeds maximum allowed size
is_valid, file_size_mb, error_message = check_file_size(uploaded_file.getvalue())
if not is_valid:
progress_reporter.complete(success=False)
st.error(error_message)
return {
"file_name": uploaded_file.name,
"topics": ["Document"],
"languages": ["English"],
"error": error_message,
"ocr_contents": {
"error": error_message,
"partial_text": "Document could not be processed due to size limitations."
}
}
# Update progress
progress_reporter.update(10, "Initializing OCR processor...")
# Determine file type from extension
file_ext = Path(uploaded_file.name).suffix.lower()
file_type = "pdf" if file_ext == ".pdf" else "image"
file_bytes = uploaded_file.getvalue()
# For PDFs, we need to handle differently
if file_type == "pdf":
progress_reporter.update(20, "Converting PDF to images...")
# Process PDF with direct handling
progress_reporter.update(30, "Processing PDF with OCR...")
# Create a temporary file for processing
temp_path = tempfile.NamedTemporaryFile(delete=False, suffix=file_ext).name
with open(temp_path, 'wb') as f:
f.write(file_bytes)
temp_file_paths.append(temp_path)
# Generate cache key
cache_key = generate_cache_key(
file_bytes,
file_type,
use_vision,
preprocessing_options,
pdf_rotation,
custom_prompt
)
# Process with cached function if possible
try:
result = process_file_cached(temp_path, file_type, use_vision, file_size_mb, cache_key, str(preprocessing_options))
progress_reporter.update(90, "Finalizing results...")
except Exception as e:
logger.warning(f"Cached processing failed: {str(e)}. Retrying with direct processing.")
progress_reporter.update(60, f"Processing error: {str(e)}. Retrying...")
# If caching fails, process directly
processor = StructuredOCR()
# Apply performance mode settings
if perf_mode == "Speed":
# Override settings for faster processing
if pdf_dpi > 100:
pdf_dpi = 100 # Lower DPI for speed
# Process directly with optimized settings
result = processor.process_file(
file_path=temp_path,
file_type="pdf",
use_vision=use_vision,
custom_prompt=custom_prompt,
file_size_mb=file_size_mb,
pdf_rotation=pdf_rotation
)
progress_reporter.update(90, "Finalizing results...")
else:
# For image files
progress_reporter.update(20, "Preparing image for processing...")
# Apply preprocessing if needed
temp_path, preprocessing_applied = apply_preprocessing_to_file(
file_bytes,
file_ext,
preprocessing_options,
temp_file_paths
)
if preprocessing_applied:
progress_reporter.update(30, "Applied image preprocessing...")
# Generate cache key
cache_key = generate_cache_key(
open(temp_path, 'rb').read(),
file_type,
use_vision,
preprocessing_options,
0, # No rotation for images (handled in preprocessing)
custom_prompt
)
# Process the file using cached function if possible
progress_reporter.update(50, "Processing document with OCR...")
try:
result = process_file_cached(temp_path, file_type, use_vision, file_size_mb, cache_key, str(preprocessing_options))
progress_reporter.update(80, "Analyzing document structure...")
progress_reporter.update(90, "Finalizing results...")
except Exception as e:
logger.warning(f"Cached processing failed: {str(e)}. Retrying with direct processing.")
progress_reporter.update(60, f"Processing error: {str(e)}. Retrying...")
# If caching fails, process directly
processor = StructuredOCR()
# Apply performance mode settings
if perf_mode == "Speed":
# Use simpler processing for speed
pass # Any speed optimizations would be handled by the StructuredOCR class
result = processor.process_file(
file_path=temp_path,
file_type=file_type,
use_vision=use_vision,
custom_prompt=custom_prompt,
file_size_mb=file_size_mb
)
progress_reporter.update(90, "Finalizing results...")
# Add additional metadata to result
result = process_result(result, uploaded_file, preprocessing_options)
# Complete progress
progress_reporter.complete()
return result
except Exception as e:
# Handle errors
error_message = handle_ocr_error(e, progress_reporter)
# Return error result
return {
"file_name": uploaded_file.name,
"topics": ["Document"],
"languages": ["English"],
"error": error_message,
"ocr_contents": {
"error": f"Failed to process file: {error_message}",
"partial_text": "Document could not be processed due to an error."
}
}
finally:
# Clean up temporary files
for temp_path in temp_file_paths:
try:
if os.path.exists(temp_path):
os.unlink(temp_path)
logger.info(f"Removed temporary file: {temp_path}")
except Exception as e:
logger.warning(f"Failed to remove temporary file {temp_path}: {str(e)}")
def process_result(result, uploaded_file, preprocessing_options=None):
"""
Process OCR result to add metadata, tags, etc.
Args:
result: OCR result dictionary
uploaded_file: The uploaded file
preprocessing_options: Dictionary of preprocessing options
Returns:
dict: Processed OCR result
"""
# Add timestamp
result['timestamp'] = format_timestamp()
# Add processing time if not already present
if 'processing_time' not in result:
result['processing_time'] = 0.0
# Generate descriptive filename
file_ext = Path(uploaded_file.name).suffix.lower()
result['descriptive_file_name'] = create_descriptive_filename(
uploaded_file.name,
result,
file_ext,
preprocessing_options
)
# Extract raw text from OCR contents
raw_text = ""
if 'ocr_contents' in result:
if 'raw_text' in result['ocr_contents']:
raw_text = result['ocr_contents']['raw_text']
elif 'content' in result['ocr_contents']:
raw_text = result['ocr_contents']['content']
# Extract subject tags if not already present or enhance existing ones
if 'topics' not in result or not result['topics']:
result['topics'] = extract_subject_tags(result, raw_text, preprocessing_options)
return result
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