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	File size: 2,642 Bytes
			
			| 5eef1a8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | import re
import hashlib
import io
import json
from datetime import datetime
from typing import Dict, List, Tuple
from bson import ObjectId
import logging
from config import logger
def clean_text_response(text: str) -> str:
    text = re.sub(r'\n\s*\n', '\n\n', text)
    text = re.sub(r'[ ]+', ' ', text)
    return text.replace("**", "").replace("__", "").strip()
def extract_section(text: str, heading: str) -> str:
    try:
        pattern = rf"{re.escape(heading)}:\s*\n(.*?)(?=\n[A-Z][^\n]*:|\Z)"
        match = re.search(pattern, text, re.DOTALL | re.IGNORECASE)
        return match.group(1).strip() if match else ""
    except Exception as e:
        logger.error(f"Section extraction failed for heading '{heading}': {e}")
        return ""
def structure_medical_response(text: str) -> Dict:
    def extract_improved(text: str, heading: str) -> str:
        patterns = [
            rf"{re.escape(heading)}:\s*\n(.*?)(?=\n\s*\n|\Z)",
            rf"\*\*{re.escape(heading)}\*\*:\s*\n(.*?)(?=\n\s*\n|\Z)",
            rf"{re.escape(heading)}[\s\-]+(.*?)(?=\n\s*\n|\Z)",
            rf"\n{re.escape(heading)}\s*\n(.*?)(?=\n\s*\n|\Z)"
        ]
        for pattern in patterns:
            match = re.search(pattern, text, re.DOTALL | re.IGNORECASE)
            if match:
                content = match.group(1).strip()
                content = re.sub(r'^\s*[\-\*]\s*', '', content, flags=re.MULTILINE)
                return content
        return ""
    
    text = text.replace('**', '').replace('__', '')
    return {
        "summary": extract_improved(text, "Summary of Patient's Medical History") or 
                  extract_improved(text, "Summarize the patient's medical history"),
        "risks": extract_improved(text, "Identify Risks or Red Flags") or 
                extract_improved(text, "Risks or Red Flags"),
        "missed_issues": extract_improved(text, "Missed Diagnoses or Treatments") or 
                       extract_improved(text, "What the doctor might have missed"),
        "recommendations": extract_improved(text, "Suggest Next Clinical Steps") or 
                         extract_improved(text, "Suggested Clinical Actions")
    }
def serialize_patient(patient: dict) -> dict:
    patient_copy = patient.copy()
    if "_id" in patient_copy:
        patient_copy["_id"] = str(patient_copy["_id"])
    return patient_copy
def compute_patient_data_hash(data: dict) -> str:
    serialized = json.dumps(data, sort_keys=True)
    return hashlib.sha256(serialized.encode()).hexdigest()
def compute_file_content_hash(file_content: bytes) -> str:
    return hashlib.sha256(file_content).hexdigest() | 
