Update patient doctor rawdata

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README.md

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+# Patient Doctor QA Raw Dataset (ZoneTwelve/patient_doctor_qa_rawdata)
+
+⚠️ **IMPORTANT**: All files in this dataset use Big5 encoding, as they originate from public e-hospital. Make sure your system is configured to handle Big5 encoded files properly.
+
+## Overview
+This dataset (`ZoneTwelve/patient_doctor_qa_rawdata`) contains patient-related Question-Answer pairs derived from Taiwan's e-Hospital system. The data is sourced from the Taiwan e-Hospital platform (https://sp1.hso.mohw.gov.tw/), providing valuable healthcare-related QA information.
+
+## Dataset Structure
+The dataset is stored in Hugging Face's blob format for efficient storage and access. It includes:
+- Question-answer pairs related to patient care
+- Medical terminology and procedures
+- Healthcare facility information
+- Patient care guidelines
+
+## Data Source
+- **Origin**: Taiwan e-Hospital Platform
+- **URL**: https://sp1.hso.mohw.gov.tw/
+- **Format**: Processed and stored in blob format
+
+## Tools and Utilities
+
+### 1. Viewer Tool (`viewer.py`)
+A Python script to preview the contents of blob files.
+
+#### Features:
+- List all files in the blob storage
+- Preview specific file contents
+- Support for both text and binary files
+- Configurable preview size
+
+#### Usage:
+```bash
+# List all files
+python viewer.py /path/to/ZoneTwelve/patient_docker_qa_data --list
+
+# Preview specific file
+python viewer.py /path/to/ZoneTwelve/patient_docker_qa_data --file 999
+
+# Preview with custom size limit
+python viewer.py /path/to/ZoneTwelve/patient_docker_qa_data --file 999 --size 2048
+```
+
+### 2. Export Tool (`export.py`)
+A Python script to extract files from the blob storage.
+
+#### Features:
+- Extract specific files from blobs
+- Maintain original file structure
+- Custom output path support
+- File listing capability
+
+#### Usage:
+```bash
+# List available files
+python export.py /path/to/ZoneTwelve/patient_docker_qa_data --list
+
+# Extract specific file
+python export.py /path/to/ZoneTwelve/patient_docker_qa_data --file 999 --output /path/to/save/999.html
+
+# Extract to default location
+python export.py /path/to/ZoneTwelve/patient_docker_qa_data --file 999
+```
+
+## Directory Structure
+```
+.
+├── blobs/
+│   ├── metadata.json
+│   ├── blob_0
+│   ├── blob_1
+│   └── ...
+├── viewer.py
+└── export.py
+```
+
+## Requirements
+- Python 3.6 or higher
+- Standard Python libraries:
+  - json
+  - argparse
+  - pathlib
+  - shutil
+
+## Installation
+1. Clone the repository
+2. Ensure Python 3.6+ is installed
+3. No additional dependencies required
+
+## Usage Examples
+
+### Viewing Files
+```bash
+# List all available files
+python viewer.py ./blobs --list
+
+# Preview a specific QA file
+python viewer.py ./blobs --file 999
+```
+
+### Extracting Files
+```bash
+# Extract a specific file to custom location
+python export.py ./blobs --file qa/patient_guidelines.txt --output ./exported/guidelines.txt
+
+# Extract maintaining original structure
+python export.py ./blobs --file qa/medical_terms.txt
+```
+
+## Data Format
+The blob storage contains file types including:
+- Raw HTML files (.html), but without extention name
+
+## License and Usage Rights
+Please refer to Taiwan e-Hospital's terms of service and data usage guidelines for specific licensing information.
+
+## Contributing
+For contributions or issues, please follow these steps:
+1. Fork the repository
+2. Create a feature branch
+3. Submit a pull request
+
+## Contact
+For questions or support regarding the dataset, please contact the maintainers or refer to the Taiwan e-Hospital platform.
+
+## Acknowledgments
+- Taiwan e-Hospital for providing the source data
+- Contributors to the data collection and processing pipeline

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export.py

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+#!/usr/bin/env python3
+"""
+Script to extract files from blobs created for Hugging Face datasets.
+Usage:
+    python blob_extract.py /path/to/blob/directory --file path/to/extract.txt --output /path/to/save
+"""
+
+import json
+import argparse
+from pathlib import Path
+import shutil
+import os
+
+class BlobExtractor:
+    def __init__(self, blob_dir: str):
+        """Initialize extractor with directory containing blobs and metadata."""
+        self.blob_dir = Path(blob_dir)
+        self.metadata_path = self.blob_dir / 'metadata.json'
+        self.metadata = self._load_metadata()
+
+    def _load_metadata(self) -> dict:
+        """Load and return the metadata JSON file."""
+        if not self.metadata_path.exists():
+            raise FileNotFoundError(f"Metadata file not found at {self.metadata_path}")
+        
+        with open(self.metadata_path, 'r') as f:
+            return json.load(f)
+
+    def list_files(self):
+        """Print all files stored in the blobs."""
+        print("\nFiles available for extraction:")
+        print("-" * 50)
+        for file_path in sorted(self.metadata.keys()):
+            info = self.metadata[file_path]
+            print(f"{file_path:<50} (size: {info['size']} bytes, blob: {info['blob']})")
+
+    def extract_file(self, file_path: str, output_path: str = None) -> None:
+        """
+        Extract a specific file from the blob.
+        
+        Args:
+            file_path: Path of the file within the blob
+            output_path: Where to save the extracted file. If None, uses original filename
+        """
+        if file_path not in self.metadata:
+            print(f"Error: File '{file_path}' not found in blobs")
+            return
+
+        info = self.metadata[file_path]
+        blob_path = self.blob_dir / info['blob']
+        
+        # Determine output path
+        if output_path is None:
+            output_path = file_path
+
+        # Create output directory if needed
+        output_path = Path(output_path)
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+            
+        print(f"\nExtracting: {file_path}")
+        print(f"From blob: {info['blob']}")
+        print(f"Size: {info['size']} bytes")
+        print(f"To: {output_path}")
+        
+        try:
+            with open(blob_path, 'rb') as src, open(output_path, 'wb') as dst:
+                # Seek to file's position in blob
+                src.seek(info['offset'])
+                # Copy exact number of bytes
+                shutil.copyfileobj(src, dst, length=info['size'])
+            
+            print(f"Successfully extracted to: {output_path}")
+            
+        except Exception as e:
+            print(f"Error extracting file: {e}")
+
+def main():
+    parser = argparse.ArgumentParser(description="Extract files from blobs")
+    parser.add_argument("blob_dir", help="Directory containing blobs and metadata.json")
+    parser.add_argument("--file", "-f", help="Specific file to extract")
+    parser.add_argument("--output", "-o", help="Output path for extracted file")
+    parser.add_argument("--list", "-l", action="store_true", help="List all files in blobs")
+    
+    args = parser.parse_args()
+    
+    try:
+        extractor = BlobExtractor(args.blob_dir)
+        
+        if args.list:
+            extractor.list_files()
+        elif args.file:
+            extractor.extract_file(args.file, args.output)
+        else:
+            extractor.list_files()  # Default to listing files if no specific action
+            
+    except Exception as e:
+        print(f"Error: {e}")
+
+if __name__ == "__main__":
+    main()

viewer.py

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+#!/usr/bin/env python3
+"""
+Simple script to preview contents of blob files created for Hugging Face datasets.
+Usage:
+    python blob_preview.py /path/to/blob/directory [--file specific/file.txt] [--list]
+"""
+
+import json
+import argparse
+from pathlib import Path
+from typing import Dict, Tuple
+
+class BlobPreview:
+    def __init__(self, blob_dir: str):
+        """Initialize preview with directory containing blobs and metadata."""
+        self.blob_dir = Path(blob_dir)
+        self.metadata_path = self.blob_dir / 'metadata.json'
+        self.metadata = self._load_metadata()
+
+    def _load_metadata(self) -> Dict:
+        """Load and return the metadata JSON file."""
+        if not self.metadata_path.exists():
+            raise FileNotFoundError(f"Metadata file not found at {self.metadata_path}")
+        
+        with open(self.metadata_path, 'r') as f:
+            return json.load(f)
+
+    def list_files(self):
+        """Print all files stored in the blobs."""
+        print("\nFiles in blobs:")
+        print("-" * 50)
+        for file_path in sorted(self.metadata.keys()):
+            info = self.metadata[file_path]
+            print(f"{file_path:<50} (size: {info['size']} bytes, blob: {info['blob']})")
+
+    def preview_file(self, file_path: str, max_size: int = 1024) -> None:
+        """Preview content of a specific file."""
+        if file_path not in self.metadata:
+            print(f"Error: File '{file_path}' not found in blobs")
+            return
+
+        info = self.metadata[file_path]
+        blob_path = self.blob_dir / info['blob']
+        
+        print(f"\nFile: {file_path}")
+        print(f"Location: {info['blob']}")
+        print(f"Size: {info['size']} bytes")
+        print(f"Offset: {info['offset']} bytes")
+        print("-" * 50)
+
+        try:
+            with open(blob_path, 'rb') as f:
+                # Seek to file's position in blob
+                f.seek(info['offset'])
+                # Read the content (up to max_size)
+                content = f.read(min(info['size'], max_size))
+
+            # Try to decode as text
+            try:
+                text = content.decode('utf-8')
+                print("Content preview (decoded as UTF-8):")
+                print(text)
+                if len(content) < info['size']:
+                    print("\n... (truncated)")
+            except UnicodeDecodeError:
+                print("Content preview (hexdump):")
+                for i in range(0, len(content), 16):
+                    chunk = content[i:i+16]
+                    hex_values = ' '.join(f'{b:02x}' for b in chunk)
+                    ascii_values = ''.join(chr(b) if 32 <= b <= 126 else '.' for b in chunk)
+                    print(f"{i:08x}  {hex_values:<48}  {ascii_values}")
+                if len(content) < info['size']:
+                    print("\n... (truncated)")
+
+        except Exception as e:
+            print(f"Error reading file: {e}")
+
+def main():
+    parser = argparse.ArgumentParser(description="Preview contents of blob files")
+    parser.add_argument("blob_dir", help="Directory containing blobs and metadata.json")
+    parser.add_argument("--file", "-f", help="Specific file to preview")
+    parser.add_argument("--list", "-l", action="store_true", help="List all files in blobs")
+    parser.add_argument("--size", "-s", type=int, default=1024,
+                       help="Maximum preview size in bytes (default: 1024)")
+    
+    args = parser.parse_args()
+    
+    try:
+        preview = BlobPreview(args.blob_dir)
+        
+        if args.list:
+            preview.list_files()
+        elif args.file:
+            preview.preview_file(args.file, args.size)
+        else:
+            preview.list_files()  # Default to listing files if no specific action
+            
+    except Exception as e:
+        print(f"Error: {e}")
+
+if __name__ == "__main__":
+    main()