Refactor PdfDocumentProcessor to enhance PDF content processing

- Updated read_content method to return raw bytes instead of extracted text.
- Modified process_content method to handle bytes and generate multiple output files including markdown, JSON, and processed PDFs.
- Implemented directory setup for image storage and output management.
- Integrated PymuDocDataset for PDF classification and processing based on OCR capabilities.

src/models/processors/pdf_processor.py

@@ -1,20 +1,69 @@
+import os
 import PyPDF2
 from models.document_processor import DocumentProcessor
+from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
+from magic_pdf.data.dataset import PymuDocDataset
+from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
+from magic_pdf.config.enums import SupportedPdfParseMethod
 
 class PdfDocumentProcessor(DocumentProcessor):
     def __init__(self, input_path: str, output_path: str):
         self.input_path = input_path
         self.output_path = output_path
+        self.output_dir = os.path.dirname(output_path)
+        self.name_without_suff = os.path.splitext(os.path.basename(input_path))[0]
+        
+        # Setup output directories
+        self.local_image_dir = os.path.join(self.output_dir, "images")
+        self.image_dir = os.path.basename(self.local_image_dir)
+        os.makedirs(self.local_image_dir, exist_ok=True)
 
-    def read_content(self) -> str:
+    def read_content(self) -> bytes:
         with open(self.input_path, 'rb') as file:
-            pdf_reader = PyPDF2.PdfReader(file)
+            return file.read()
-            return ' '.join([page.extract_text() for page in pdf_reader.pages])
 
-    def process_content(self, content: str) -> str:
+    def process_content(self, content: bytes) -> dict:
-        # Implementation for processing PDF content
+        # Initialize writers
-        return content
+        image_writer = FileBasedDataWriter(self.local_image_dir)
+        md_writer = FileBasedDataWriter(self.output_dir)
 
-    def save_content(self, content: str) -> None:
+        # Create Dataset Instance
-        # Implementation for saving as PDF
+        ds = PymuDocDataset(content)
-        pass
+        
+        # Process based on PDF type
+        if ds.classify() == SupportedPdfParseMethod.OCR:
+            infer_result = ds.apply(doc_analyze, ocr=True)
+            pipe_result = infer_result.pipe_ocr_mode(image_writer)
+        else:
+            infer_result = ds.apply(doc_analyze, ocr=False)
+            pipe_result = infer_result.pipe_txt_mode(image_writer)
+
+        # Generate all outputs
+        infer_result.draw_model(os.path.join(self.output_dir, f"{self.name_without_suff}_model.pdf"))
+        model_inference_result = infer_result.get_infer_res()
+        
+        pipe_result.draw_layout(os.path.join(self.output_dir, f"{self.name_without_suff}_layout.pdf"))
+        pipe_result.draw_span(os.path.join(self.output_dir, f"{self.name_without_suff}_spans.pdf"))
+        
+        md_content = pipe_result.get_markdown(self.image_dir)
+        pipe_result.dump_md(md_writer, f"{self.name_without_suff}.md", self.image_dir)
+        
+        content_list = pipe_result.get_content_list(self.image_dir)
+        pipe_result.dump_content_list(md_writer, f"{self.name_without_suff}_content_list.json", self.image_dir)
+        
+        middle_json = pipe_result.get_middle_json()
+        pipe_result.dump_middle_json(md_writer, f'{self.name_without_suff}_middle.json')
+
+        return md_content
+
+        return {
+            'markdown': md_content,
+            'content_list': content_list,
+            'middle_json': middle_json,
+            'model_inference': model_inference_result
+        }
+
+    def save_content(self, content: dict) -> None:
+        # Content is already saved during processing
+        with open(self.output_path, 'w', encoding='utf-8') as file:
+            file.write(content)