145 lines
No EOL
5.3 KiB
Python
145 lines
No EOL
5.3 KiB
Python
from pdf2image import convert_from_path
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import os
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import json
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from pathlib import Path
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import tempfile
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import base64
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import anthropic
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from typing import List, Dict
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import time
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class PDFProcessor:
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def __init__(self, input_dir: str, output_dir: str, api_key: str):
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self.input_dir = Path(input_dir)
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self.output_dir = Path(output_dir)
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self.output_dir.mkdir(parents=True, exist_ok=True)
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self.temp_dir = Path(tempfile.mkdtemp())
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self.client = anthropic.Client(api_key=api_key)
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def encode_image(self, image_path: str) -> str:
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"""Convert image to base64 for API"""
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with open(image_path, 'rb') as image_file:
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return base64.b64encode(image_file.read()).decode('utf-8')
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def analyze_image(self, image_path: str) -> Dict:
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"""Analyze a single image using Claude Vision API"""
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try:
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with open(image_path, 'rb') as img:
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message = self.client.messages.create(
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model="claude-3-opus-20240229",
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max_tokens=1000,
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messages=[{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": """Analyze this magazine cover and extract the following metadata:
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1. Magazine Title
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2. Issue Date/Publication Date
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3. Publisher
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4. Issue Number
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Format your response as JSON with these exact keys:
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{
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"title": string,
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"date": string,
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"publisher": string,
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"issue_number": string,
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"confidence": "high|medium|low"
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}
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If any field cannot be determined, use null. Set confidence based on how clear the information is."""
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},
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{
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": "image/jpeg",
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"data": self.encode_image(image_path)
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}
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}
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]
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}]
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)
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# Parse the JSON response
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response_text = message.content[0].text
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metadata = json.loads(response_text)
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return metadata
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except Exception as e:
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print(f"Error analyzing image {image_path}: {str(e)}")
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return {
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"title": None,
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"date": None,
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"publisher": None,
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"issue_number": None,
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"confidence": "error"
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}
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def process_pdfs(self) -> List[Dict]:
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"""Process all PDFs in the input directory"""
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pdf_files = list(self.input_dir.glob('*.pdf'))
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results = []
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for pdf_path in pdf_files:
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try:
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result = self.process_single_pdf(pdf_path)
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results.append(result)
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# Small delay to respect API rate limits
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time.sleep(1)
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except Exception as e:
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print(f"Error processing {pdf_path}: {str(e)}")
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results.append({
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'pdf_path': str(pdf_path),
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'status': 'error',
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'error': str(e)
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})
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# Save results to JSON
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with open(self.output_dir / 'processing_results.json', 'w', encoding='utf-8') as f:
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json.dump(results, f, indent=4, ensure_ascii=False)
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return results
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def process_single_pdf(self, pdf_path: Path) -> Dict:
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"""Process a single PDF file"""
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print(f"Processing: {pdf_path}")
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# Convert first page to image
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images = convert_from_path(pdf_path, first_page=1, last_page=1)
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if not images:
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raise Exception("Could not extract first page")
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# Save first page image
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first_page = images[0]
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image_path = self.temp_dir / f"{pdf_path.stem}_page1.jpg"
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first_page.save(str(image_path), 'JPEG')
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# Analyze the image
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metadata = self.analyze_image(str(image_path))
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return {
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'pdf_path': str(pdf_path),
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'image_path': str(image_path),
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'metadata': metadata,
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'status': 'completed'
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}
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def main():
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# Get API key from environment variable
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api_key = os.getenv('ANTHROPIC_API_KEY')
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if not api_key:
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raise ValueError("ANTHROPIC_API_KEY environment variable not set")
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input_dir = "path/to/pdfs"
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output_dir = "path/to/output"
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processor = PDFProcessor(input_dir, output_dir, api_key)
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results = processor.process_pdfs()
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print(f"\nProcessed {len(results)} PDF files")
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print(f"Results saved to: {processor.output_dir}/processing_results.json")
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if __name__ == "__main__":
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main() |