import gradio as gr from utils.meldrx import MeldRxAPI import json import os import tempfile from datetime import datetime import traceback import logging from huggingface_hub import InferenceClient # Import InferenceClient from urllib.parse import urlparse, parse_qs # Import URL parsing utilities from utils.callbackmanager import CallbackManager from prompts import system_instructions # Set up logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Import PDF utilities from pdfutils import PDFGenerator, generate_discharge_summary # Import necessary libraries for new file types and AI analysis functions import pydicom # For DICOM import hl7 # For HL7 from xml.etree import ElementTree # For XML and CCDA from pypdf import PdfReader # For PDF import csv # For CSV import io # For IO operations from PIL import Image # For image handling from utils.generators import extract_auth_code_from_url, generate_pdf_from_meldrx, generate_ai_discharge_content, generate_pdf_from_meldrx_with_ai_content, extract_section, generate_discharge_paper_one_click, generate_pdf_from_form, generate_discharge_summary, generate_ai_discharge_content, analyze_dicom_file_with_ai, analyze_hl7_file_with_ai, analyze_cda_xml_file_with_ai, analyze_pdf_file_with_ai, analyze_csv_file_with_ai, generate_pdf_from_form , generate_discharge_paper_one_click , generate_ai_discharge_content , extract_section , generate_pdf_from_meldrx_with_ai_content # Initialize Inference Client - Ensure YOUR_HF_TOKEN is set in environment variables or replace with your actual token HF_TOKEN = os.getenv("HF_TOKEN") # Or replace with your actual token string if not HF_TOKEN: raise ValueError( "HF_TOKEN environment variable not set. Please set your Hugging Face API token." ) client = InferenceClient(api_key=HF_TOKEN) model_name = "meta-llama/Llama-3.3-70B-Instruct" # Specify the model to use def display_form(first_name, last_name, middle_initial, dob, age, sex, address, city, state, zip_code, doctor_first_name, doctor_last_name, doctor_middle_initial, hospital_name, doctor_address, doctor_city, doctor_state, doctor_zip, admission_date, referral_source, admission_method, discharge_date, discharge_reason, date_of_death, diagnosis, procedures, medications, preparer_name, preparer_job_title,): form = f"""
**Patient Discharge Form**
- Name: {first_name} {middle_initial} {last_name}
- Date of Birth: {dob}, Age: {age}, Sex: {sex}
- Address: {address}, {city}, {state}, {zip_code}
- Doctor: {doctor_first_name} {doctor_middle_initial} {doctor_last_name}
- Hospital/Clinic: {hospital_name}
- Doctor Address: {doctor_address}, {doctor_city}, {doctor_state}, {doctor_zip}
- Admission Date: {admission_date}, Source: {referral_source}, Method: {admission_method}
- Discharge Date: {discharge_date}, Reason: {discharge_reason}
- Date of Death: {date_of_death}
- Diagnosis: {diagnosis}
- Procedures: {procedures}
- Medications: {medications}
- Prepared By: {preparer_name}, {preparer_job_title}
""" return form # Create a simplified interface to avoid complex component interactions CALLBACK_MANAGER = CallbackManager( redirect_uri="https://multitransformer-discharge-guard.hf.space/callback", client_secret=None, ) def generate_discharge_paper_one_click(): """One-click function to fetch patient data and generate discharge paper with AI Content.""" patient_data_str = CALLBACK_MANAGER.get_patient_data() if ( patient_data_str.startswith("Not authenticated") or patient_data_str.startswith("Failed") or patient_data_str.startswith("Error") ): return None, patient_data_str # Return error message if authentication or data fetch fails try: patient_data = json.loads(patient_data_str) # --- AI Content Generation for Discharge Summary --- # This is a placeholder - Replace with actual AI call using InferenceClient and patient_data to generate content ai_generated_content = generate_ai_discharge_content( patient_data ) # Placeholder AI function if not ai_generated_content: return None, "Error: AI content generation failed." # --- PDF Generation with AI Content --- pdf_path, status_message = generate_pdf_from_meldrx_with_ai_content( patient_data, ai_generated_content ) # Function to generate PDF with AI content if pdf_path: return pdf_path, status_message else: return None, status_message # Return status message if PDF generation fails except json.JSONDecodeError: return None, "Error: Patient data is not in valid JSON format." except Exception as e: return None, f"Error during discharge paper generation: {str(e)}" # Define the cyberpunk theme - using a dark base and neon accents cyberpunk_theme = gr.themes.Monochrome( primary_hue="cyan", secondary_hue="pink", neutral_hue="slate", font=["Source Code Pro", "monospace"], # Retro monospace font font_mono=["Source Code Pro", "monospace"] ) # Create the UI with the cyberpunk theme with gr.Blocks(theme=cyberpunk_theme) as demo: # Apply the theme here gr.Markdown("

Discharge Guard Cyber

") # Cyberpunk Title with gr.Tab("Authenticate with MeldRx", elem_classes="cyberpunk-tab"): # Optional: Class for tab styling gr.Markdown("

SMART on FHIR Authentication

") # Neon Tab Header auth_url_output = gr.Textbox(label="Authorization URL", value=CALLBACK_MANAGER.get_auth_url(), interactive=False) gr.Markdown("

Copy the URL above, open it in a browser, log in, and paste the entire redirected URL from your browser's address bar below.

") # Subdued instructions with neon highlight redirected_url_input = gr.Textbox(label="Redirected URL") # New textbox for redirected URL extract_code_button = gr.Button("Extract Authorization Code", elem_classes="cyberpunk-button") # Cyberpunk button style extracted_code_output = gr.Textbox(label="Extracted Authorization Code", interactive=False) # Textbox to show extracted code auth_code_input = gr.Textbox(label="Authorization Code (from above, or paste manually if extraction fails)", interactive=True) # Updated label to be clearer auth_submit = gr.Button("Submit Code for Authentication", elem_classes="cyberpunk-button") # Cyberpunk button style auth_result = gr.HTML(label="Authentication Result") # Use HTML for styled result patient_data_button = gr.Button("Fetch Patient Data", elem_classes="cyberpunk-button") # Cyberpunk button style patient_data_output = gr.Textbox(label="Patient Data", lines=10) # Add button to generate PDF from MeldRx data (No AI) meldrx_pdf_button = gr.Button("Generate PDF from MeldRx Data (No AI)", elem_classes="cyberpunk-button") # Renamed button meldrx_pdf_status = gr.Textbox(label="PDF Generation Status (No AI)") # Renamed status meldrx_pdf_download = gr.File(label="Download Generated PDF (No AI)") # Renamed download def process_redirected_url(redirected_url): """Processes the redirected URL to extract and display the authorization code.""" auth_code, error_message = extract_auth_code_from_url(redirected_url) if auth_code: return auth_code, "Authorization code extracted!" # Neon Green Success else: return "", f"Could not extract authorization code. {error_message or ''}" # Neon Orange Error extract_code_button.click( fn=process_redirected_url, inputs=redirected_url_input, outputs=[extracted_code_output, auth_result],# Reusing auth_result for extraction status ) auth_submit.click( fn=CALLBACK_MANAGER.set_auth_code, inputs=extracted_code_output, # Using extracted code as input for authentication outputs=auth_result, ) with gr.Tab("Patient Dashboard", elem_classes="cyberpunk-tab"): # Optional: Class for tab styling gr.Markdown("

Patient Data

") # Neon Tab Header dashboard_output = gr.HTML("

Fetch patient data from the Authentication tab first.

") # Subdued placeholder text refresh_btn = gr.Button("Refresh Data", elem_classes="cyberpunk-button") # Cyberpunk button style # Simple function to update dashboard based on fetched data def update_dashboard(): try: data = CALLBACK_MANAGER.get_patient_data() if ( data.startswith("Not authenticated") or data.startswith("Failed") or data.startswith("Error") ): return f"

{data}

" # Show auth errors in orange try: # Parse the data patients_data = json.loads(data) patients = [] # Extract patients from bundle for entry in patients_data.get("entry", []): resource = entry.get("resource", {}) if resource.get("resourceType") == "Patient": patients.append(resource) # Generate HTML card html = "

Patients

" # Neon Sub-header for patient in patients: # Extract name name = patient.get("name", [{}])[0] given = " ".join(name.get("given", ["Unknown"])) family = name.get("family", "Unknown") # Extract other details gender = patient.get("gender", "unknown").capitalize() birth_date = patient.get("birthDate", "Unknown") # Generate HTML card with cyberpunk styling html += f"""

{given} {family}

Gender: {gender}

Birth Date: {birth_date}

ID: {patient.get("id", "Unknown")}

""" return html except Exception as e: return f"

Error parsing patient data: {str(e)}

" # Tomato Error except Exception as e: return f"

Error fetching patient data: {str(e)}

" # Tomato Error with gr.Tab("Discharge Form", elem_classes="cyberpunk-tab"): # Optional: Class for tab styling gr.Markdown("

Patient Details

") # Neon Tab Header with gr.Row(): first_name = gr.Textbox(label="First Name") last_name = gr.Textbox(label="Last Name") middle_initial = gr.Textbox(label="Middle Initial") with gr.Row(): dob = gr.Textbox(label="Date of Birth") age = gr.Textbox(label="Age") sex = gr.Textbox(label="Sex") address = gr.Textbox(label="Address") with gr.Row(): city = gr.Textbox(label="City") state = gr.Textbox(label="State") zip_code = gr.Textbox(label="Zip Code") gr.Markdown("

Primary Healthcare Professional Details

") # Neon Sub-header with gr.Row(): doctor_first_name = gr.Textbox(label="Doctor's First Name") doctor_last_name = gr.Textbox(label="Doctor's Last Name") doctor_middle_initial = gr.Textbox(label="Doctor's Middle Initial") hospital_name = gr.Textbox(label="Hospital/Clinic Name") doctor_address = gr.Textbox(label="Address") with gr.Row(): doctor_city = gr.Textbox(label="City") doctor_state = gr.Textbox(label="State") doctor_zip = gr.Textbox(label="Zip Code") gr.Markdown("

Admission and Discharge Details

") # Neon Sub-header with gr.Row(): admission_date = gr.Textbox(label="Date of Admission") referral_source = gr.Textbox(label="Source of Referral") admission_method = gr.Textbox(label="Method of Admission") with gr.Row(): discharge_date = gr.Textbox(label="Date of Discharge") discharge_reason = gr.Radio( ["Treated", "Transferred", "Discharge Against Advice", "Patient Died"], label="Discharge Reason", ) date_of_death = gr.Textbox(label="Date of Death (if applicable)") gr.Markdown("

Diagnosis & Procedures

") # Neon Sub-header diagnosis = gr.Textbox(label="Diagnosis") procedures = gr.Textbox(label="Operation & Procedures") gr.Markdown("

Medication Details

") # Neon Sub-header medications = gr.Textbox(label="Medication on Discharge") gr.Markdown("

Prepared By

") # Neon Sub-header with gr.Row(): preparer_name = gr.Textbox(label="Name") preparer_job_title = gr.Textbox(label="Job Title") # Add buttons for both display form and generate PDF with gr.Row(): submit_display = gr.Button("Display Form", elem_classes="cyberpunk-button") # Cyberpunk button style submit_pdf = gr.Button("Generate PDF (No AI)", elem_classes="cyberpunk-button") # Renamed button to clarify no AI and styled # Output areas form_output = gr.HTML() # Use HTML to render styled form pdf_output = gr.File(label="Download PDF (No AI)") # Renamed output to clarify no AI # Connect the display form button submit_display.click( display_form, inputs=[ first_name, last_name, middle_initial, dob, age, sex, address, city, state, zip_code, doctor_first_name, doctor_last_name, doctor_middle_initial, hospital_name, doctor_address, doctor_city, doctor_state, doctor_zip, admission_date, referral_source, admission_method, discharge_date, discharge_reason, date_of_death, diagnosis, procedures, medications, preparer_name, preparer_job_title,], outputs=form_output ) # Connect the generate PDF button (No AI version) submit_pdf.click( generate_pdf_from_form, inputs=[ first_name, last_name, middle_initial, dob, age, sex, address, city, state, zip_code, doctor_first_name, doctor_last_name, doctor_middle_initial, hospital_name, doctor_address, doctor_city, doctor_state, doctor_zip, admission_date, referral_source, admission_method, discharge_date, discharge_reason, date_of_death, diagnosis, procedures, medications, preparer_name, preparer_job_title,], outputs=pdf_output ) with gr.Tab("Medical File Analysis", elem_classes="cyberpunk-tab"): # Optional: Class for tab styling gr.Markdown("

Analyze Medical Files with Discharge Guard AI

") # Neon Tab Header with gr.Column(): dicom_file = gr.File( file_types=[".dcm"], label="Upload DICOM File (.dcm)" ) dicom_ai_output = gr.Textbox(label="DICOM Analysis Report", lines=5) analyze_dicom_button = gr.Button("Analyze DICOM with AI", elem_classes="cyberpunk-button") # Cyberpunk button style hl7_file = gr.File( file_types=[".hl7"], label="Upload HL7 File (.hl7)" ) hl7_ai_output = gr.Textbox(label="HL7 Analysis Report", lines=5) analyze_hl7_button = gr.Button("Analyze HL7 with AI", elem_classes="cyberpunk-button") # Cyberpunk button style xml_file = gr.File( file_types=[".xml"], label="Upload XML File (.xml)" ) xml_ai_output = gr.Textbox(label="XML Analysis Report", lines=5) analyze_xml_button = gr.Button("Analyze XML with AI", elem_classes="cyberpunk-button") # Cyberpunk button style ccda_file = gr.File( file_types=[".xml", ".cda", ".ccd"], label="Upload CCDA File (.xml, .cda, .ccd)" ) ccda_ai_output = gr.Textbox(label="CCDA Analysis Report", lines=5) analyze_ccda_button = gr.Button("Analyze CCDA with AI", elem_classes="cyberpunk-button") # Cyberpunk button style ccd_file = gr.File( file_types=[".ccd"], label="Upload CCD File (.ccd)", ) # Redundant, as CCDA also handles .ccd, but kept for clarity ccd_ai_output = gr.Textbox( label="CCD Analysis Report", lines=5 ) # Redundant analyze_ccd_button = gr.Button("Analyze CCD with AI", elem_classes="cyberpunk-button") # Cyberpunk button style # Redundant pdf_file = gr.File( file_types=[".pdf"], label="Upload PDF File (.pdf)" ) pdf_ai_output = gr.Textbox(label="PDF Analysis Report", lines=5) analyze_pdf_button = gr.Button("Analyze PDF with AI", elem_classes="cyberpunk-button") # Cyberpunk button style csv_file = gr.File( file_types=[".csv"], label="Upload CSV File (.csv)" ) csv_ai_output = gr.Textbox(label="CSV Analysis Report", lines=5) analyze_csv_button = gr.Button("Analyze CSV with AI", elem_classes="cyberpunk-button") # Cyberpunk button style # Connect AI Analysis Buttons - using REAL AI functions now analyze_dicom_button.click( analyze_dicom_file_with_ai, # Call REAL AI function inputs=dicom_file, outputs=dicom_ai_output ) analyze_hl7_button.click( analyze_hl7_file_with_ai, # Call REAL AI function inputs=hl7_file, outputs=hl7_ai_output ) analyze_xml_button.click( analyze_cda_xml_file_with_ai, # Call REAL AI function inputs=xml_file, outputs=xml_ai_output ) analyze_ccda_button.click( analyze_cda_xml_file_with_ai, # Call REAL AI function inputs=ccda_file, outputs=ccda_ai_output ) analyze_ccd_button.click( # Redundant button, but kept for UI if needed analyze_cda_xml_file_with_ai, # Call REAL AI function inputs=ccd_file, outputs=ccd_ai_output ) analyze_pdf_button.click( analyze_pdf_file_with_ai, inputs=pdf_file, outputs=pdf_ai_output ) analyze_csv_button.click( analyze_csv_file_with_ai, inputs=csv_file, outputs=csv_ai_output ) with gr.Tab( "One-Click Discharge Paper (AI)", elem_classes="cyberpunk-tab" ): # New Tab for One-Click Discharge Paper with AI, styled gr.Markdown("

One-Click Medical Discharge Paper Generation with AI Content

") # Neon Tab Header one_click_ai_pdf_button = gr.Button( "Generate Discharge Paper with AI (One-Click)", elem_classes="cyberpunk-button" ) # Updated button label and styled one_click_ai_pdf_status = gr.Textbox( label="Discharge Paper Generation Status (AI)" ) # Updated status label one_click_ai_pdf_download = gr.File( label="Download Discharge Paper (AI)" ) # Updated download label one_click_ai_pdf_button.click( generate_discharge_paper_one_click, # Use the one-click function that now calls AI inputs=[], outputs=[one_click_ai_pdf_download, one_click_ai_pdf_status], ) # Connect the patient data buttons patient_data_button.click( fn=CALLBACK_MANAGER.get_patient_data, inputs=None, outputs=patient_data_output ) # Connect refresh button to update dashboard refresh_btn.click( fn=update_dashboard, inputs=None, outputs=dashboard_output ) # Corrected the button click function name here to `generate_pdf_from_meldrx` (No AI PDF) meldrx_pdf_button.click( fn=generate_pdf_from_meldrx, inputs=patient_data_output, outputs=[meldrx_pdf_download, meldrx_pdf_status] ) # Connect patient data updates to dashboard patient_data_button.click( fn=update_dashboard, inputs=None, outputs=dashboard_output ) # Launch with sharing enabled for public access demo.launch(ssr_mode=False)