utils/oneclick.py

# utils/oneclick.py
from typing import Tuple, Optional, Dict
from .meldrx import MeldRxAPI
from .responseparser import PatientDataExtractor
from .pdfutils import PDFGenerator
import logging
from huggingface_hub import InferenceClient

logger = logging.getLogger(__name__)

HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
    raise ValueError("HF_TOKEN environment variable not set.")
client = InferenceClient(api_key=HF_TOKEN)client = InferenceClient()  # Initialize the xAI client (adjust initialization as needed)
MODEL_NAME = "meta-llama/Llama-3.3-70B-Instruct"

def generate_ai_discharge_summary(patient_dict: Dict[str, str]) -> Optional[str]:
    """Generate a discharge summary using AI based on extracted patient data."""
    try:
        # Use the formatted summary as input
        formatted_summary = format_discharge_summary(patient_dict)
        
        logger.info("Generating AI discharge summary with patient info: %s", formatted_summary)

        messages = [
            {
                "role": "assistant",
                "content": (
                    "You are a senior medical practitioner tasked with creating discharge summaries. "
                    "Generate a complete discharge summary based on the provided patient information."
                )
            },
            {"role": "user", "content": formatted_summary}
        ]

        stream = client.chat.completions.create(
            model=MODEL_NAME,
            messages=messages,
            temperature=0.4,
            max_tokens=3584,
            top_p=0.7,
            stream=True
        )

        discharge_summary = ""
        for chunk in stream:
            content = chunk.choices[0].delta.content
            if content:
                discharge_summary += content

        logger.info("AI discharge summary generated successfully")
        return discharge_summary.strip()

    except Exception as e:
        logger.error(f"Error generating AI discharge summary: {str(e)}", exc_info=True)
        return None

def generate_discharge_paper_one_click(
    api: MeldRxAPI,
    patient_id: str = "",
    first_name: str = "",
    last_name: str = ""
) -> Tuple[Optional[str], str, Optional[str], Optional[str]]:
    """
    Generate a discharge summary PDF with one click using MeldRx API data.
    
    Returns:
        Tuple of (pdf_path, status_message, basic_summary, ai_summary)
    """
    try:
        patients_data = api.get_patients()
        if not patients_data or "entry" not in patients_data:
            return None, "Failed to fetch patient data from MeldRx API", None, None

        extractor = PatientDataExtractor(patients_data, "json")
        
        if not extractor.patients:
            return None, "No patients found in the data", None, None

        matching_patients = []
        for i in range(len(extractor.patients)):
            extractor.set_patient_by_index(i)
            patient_data = extractor.get_patient_dict()
            
            patient_data.setdefault('id', 'unknown')
            patient_data.setdefault('first_name', '')
            patient_data.setdefault('last_name', '')
            
            if (not patient_id or patient_data.get("id", "") == patient_id) and \
               (not first_name or patient_data.get("first_name", "").lower() == first_name.lower()) and \
               (not last_name or patient_data.get("last_name", "").lower() == last_name.lower()):
                matching_patients.append(patient_data)

        if not matching_patients:
            return None, "No matching patients found with the provided criteria", None, None
        
        patient_data = matching_patients[0]
        extractor.set_patient_by_index(0)
        
        # Generate both basic and AI summaries
        basic_summary = format_discharge_summary(patient_data)
        ai_summary = generate_ai_discharge_summary(patient_data)
        
        if not ai_summary:
            return None, "Failed to generate AI summary", basic_summary, None
            
        pdf_gen = PDFGenerator()
        filename = f"discharge_{patient_data.get('id', 'unknown')}_{patient_data.get('last_name', 'patient')}.pdf"
        pdf_path = pdf_gen.generate_pdf_from_text(ai_summary, filename)
        
        if pdf_path:
            return pdf_path, "Discharge summary generated successfully", basic_summary, ai_summary
        return None, "Failed to generate PDF file", basic_summary, ai_summary

    except Exception as e:
        logger.error(f"Error in one-click discharge generation: {str(e)}", exc_info=True)
        return None, f"Error generating discharge summary: {str(e)}", None, None

def format_discharge_summary(patient_data: dict) -> str:
    """Format patient data into a discharge summary text."""
    patient_data.setdefault('name_prefix', '')
    patient_data.setdefault('first_name', '')
    patient_data.setdefault('last_name', '')
    patient_data.setdefault('dob', 'Unknown')
    patient_data.setdefault('age', 'Unknown')
    patient_data.setdefault('sex', 'Unknown')
    patient_data.setdefault('id', 'Unknown')
    patient_data.setdefault('address', 'Unknown')
    patient_data.setdefault('city', 'Unknown')
    patient_data.setdefault('state', 'Unknown')
    patient_data.setdefault('zip_code', 'Unknown')
    patient_data.setdefault('phone', 'Unknown')
    patient_data.setdefault('admission_date', 'Unknown')
    patient_data.setdefault('discharge_date', 'Unknown')
    patient_data.setdefault('diagnosis', 'Unknown')
    patient_data.setdefault('medications', 'None specified')
    patient_data.setdefault('doctor_first_name', 'Unknown')
    patient_data.setdefault('doctor_last_name', 'Unknown')
    patient_data.setdefault('hospital_name', 'Unknown')
    patient_data.setdefault('doctor_address', 'Unknown')
    patient_data.setdefault('doctor_city', 'Unknown')
    patient_data.setdefault('doctor_state', 'Unknown')
    patient_data.setdefault('doctor_zip', 'Unknown')

    summary = [
        "DISCHARGE SUMMARY",
        "",
        "PATIENT INFORMATION",
        f"Name: {patient_data['name_prefix']} {patient_data['first_name']} {patient_data['last_name']}".strip(),
        f"Date of Birth: {patient_data['dob']}",
        f"Age: {patient_data['age']}",
        f"Gender: {patient_data['sex']}",
        f"Patient ID: {patient_data['id']}",
        "",
        "CONTACT INFORMATION",
        f"Address: {patient_data['address']}",
        f"City: {patient_data['city']}, {patient_data['state']} {patient_data['zip_code']}",
        f"Phone: {patient_data['phone']}",
        "",
        "ADMISSION INFORMATION",
        f"Admission Date: {patient_data['admission_date']}",
        f"Discharge Date: {patient_data['discharge_date']}",
        f"Diagnosis: {patient_data['diagnosis']}",
        "",
        "MEDICATIONS",
        f"{patient_data['medications']}",
        "",
        "PHYSICIAN INFORMATION",
        f"Physician: Dr. {patient_data['doctor_first_name']} {patient_data['doctor_last_name']}".strip(),
        f"Hospital: {patient_data['hospital_name']}",
        f"Address: {patient_data['doctor_address']}",
        f"City: {patient_data['doctor_city']}, {patient_data['doctor_state']} {patient_data['doctor_zip']}",
    ]
    
    return "\n".join(line for line in summary if line.strip() or line == "")