refactor app

README.md

@@ -5,7 +5,7 @@ colorFrom: gray
 colorTo: pink
 sdk: gradio
 sdk_version: 5.20.0
-app_file: callbackmanager.py
+app_file: app.py
 pinned: true
 license: mit
 short_description: Provides guardrails and discharge summaries with compliance

app.py

@@ -0,0 +1,418 @@
+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"""
+    <div style='color:#00FFFF; font-family: monospace;'>
+    **Patient Discharge Form** <br>
+    - Name: {first_name} {middle_initial} {last_name} <br>
+    - Date of Birth: {dob}, Age: {age}, Sex: {sex} <br>
+    - Address: {address}, {city}, {state}, {zip_code} <br>
+    - Doctor: {doctor_first_name} {doctor_middle_initial} {doctor_last_name} <br>
+    - Hospital/Clinic: {hospital_name} <br>
+    - Doctor Address: {doctor_address}, {doctor_city}, {doctor_state}, {doctor_zip} <br>
+    - Admission Date: {admission_date}, Source: {referral_source}, Method: {admission_method} <br>
+    - Discharge Date: {discharge_date}, Reason: {discharge_reason} <br>
+    - Date of Death: {date_of_death} <br>
+    - Diagnosis: {diagnosis} <br>
+    - Procedures: {procedures} <br>
+    - Medications: {medications} <br>
+    - Prepared By: {preparer_name}, {preparer_job_title}
+    </div>
+    """
+    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("<h1 style='color:#00FFFF; text-shadow: 0 0 5px #00FFFF;'>Discharge Guard <span style='color:#FF00FF; text-shadow: 0 0 5px #FF00FF;'>Cyber</span></h1>") # Cyberpunk Title
+
+    with gr.Tab("Authenticate with MeldRx", elem_classes="cyberpunk-tab"): # Optional: Class for tab styling
+        gr.Markdown("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>SMART on FHIR Authentication</h2>") # Neon Tab Header
+        auth_url_output = gr.Textbox(label="Authorization URL", value=CALLBACK_MANAGER.get_auth_url(), interactive=False)
+        gr.Markdown("<p style='color:#A9A9A9;'>Copy the URL above, open it in a browser, log in, and paste the <span style='color:#00FFFF;'>entire redirected URL</span> from your browser's address bar below.</p>") # 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, "<span style='color:#00FF7F;'>Authorization code extracted!</span>" # Neon Green Success
+            else:
+                return "", f"<span style='color:#FF4500;'>Could not extract authorization code.</span> {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("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Patient Data</h2>") # Neon Tab Header
+        dashboard_output = gr.HTML("<p style='color:#A9A9A9;'>Fetch patient data from the Authentication tab first.</p>") # 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("<span style='color:#FF8C00;'>Not authenticated")
+                    or data.startswith("<span style='color:#DC143C;'>Failed")
+                    or data.startswith("<span style='color:#FF6347;'>Error")
+                ):
+                    return f"<p style='color:#FF8C00;'>{data}</p>" # 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 = "<h3 style='color:#00FFFF; text-shadow: 0 0 2px #00FFFF;'>Patients</h3>" # 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"""
+                        <div style="border: 1px solid #00FFFF; padding: 10px; margin: 10px 0; border-radius: 5px; background-color: #222; box-shadow: 0 0 5px #00FFFF;">
+                            <h4 style='color:#00FFFF;'>{given} {family}</h4>
+                            <p style='color:#A9A9A9;'><strong>Gender:</strong> <span style='color:#00FFFF;'>{gender}</span></p>
+                            <p style='color:#A9A9A9;'><strong>Birth Date:</strong> <span style='color:#00FFFF;'>{birth_date}</span></p>
+                            <p style='color:#A9A9A9;'><strong>ID:</strong> <span style='color:#00FFFF;'>{patient.get("id", "Unknown")}</span></p>
+                        </div>
+                        """
+
+                    return html
+                except Exception as e:
+                    return f"<p style='color:#FF6347;'>Error parsing patient data: {str(e)}</p>" # Tomato Error
+            except Exception as e:
+                return f"<p style='color:#FF6347;'>Error fetching patient data: {str(e)}</p>" # Tomato Error
+
+
+    with gr.Tab("Discharge Form", elem_classes="cyberpunk-tab"): # Optional: Class for tab styling
+        gr.Markdown("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Patient Details</h2>") # 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("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Primary Healthcare Professional Details</h2>") # 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("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Admission and Discharge Details</h2>") # 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("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Diagnosis & Procedures</h2>") # Neon Sub-header
+        diagnosis = gr.Textbox(label="Diagnosis")
+        procedures = gr.Textbox(label="Operation & Procedures")
+        gr.Markdown("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Medication Details</h2>") # Neon Sub-header
+        medications = gr.Textbox(label="Medication on Discharge")
+        gr.Markdown("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Prepared By</h2>") # 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("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Analyze Medical Files with Discharge Guard AI</h2>") # 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("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>One-Click Medical Discharge Paper Generation with AI Content</h2>") # 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)

meldrxtester.py → old/meldrxtester.py

@@ -1,7 +1,7 @@
 import requests
 import json
 from typing import Optional, Dict, Any
-from meldrx import MeldRxAPI
+from utils.meldrx import MeldRxAPI
 
 # Testing class
 class MeldRxAPITest:

meldrxtester2.py → old/meldrxtester2.py

@@ -1,7 +1,7 @@
 import requests
 import json
 from typing import Optional, Dict, Any
-from meldrx import MeldRxAPI  # Assuming meldrx.py contains the updated MeldRxAPI with SMART on FHIR
+from utils.meldrx import MeldRxAPI  # Assuming meldrx.py contains the updated MeldRxAPI with SMART on FHIR
 
 class MeldRxAPITest:
     """A class to test the functionality of the MeldRxAPI class with SMART on FHIR and Gradio callback."""

test_meldrx.py → old/test_meldrx.py

@@ -3,7 +3,7 @@ from unittest.mock import patch, Mock
 import json
 from io import StringIO
 from contextlib import redirect_stdout
-from meldrx import MeldRxAPI  # Import the class from meldrx.py
+from utils.meldrx import MeldRxAPI  # Import the class from meldrx.py
 
 class TestMeldRxAPI(unittest.TestCase):
     def setUp(self):

prompts.py

@@ -0,0 +1,31 @@
+
+system_instructions = """
+**Discharge Guard - Medical Data Analysis Assistant**
+**Core Role:** I am Discharge Guard, an advanced AI designed for deep medical data analysis and informational insights. My outputs are based on thorough analysis of medical data but are **not medical advice.**
+**Important Guidelines:**
+1.  **Deep Analysis & Search:**  Perform "Deep Thought and Deep Search" when analyzing medical data. This includes:
+    *   Comprehensive data ingestion from various formats (HL7, FHIR, CCDA, DICOM, PDF, CSV, text).
+    *   Multi-layered analysis: surface extraction, deep pattern identification, and inferential reasoning.
+    *   Contextual understanding of medical data.
+    *   Evidence-based approach, simulating cross-referencing with medical knowledge.
+    *   Structured output with clear explanations.
+2.  **Focus on Informational Insights, Not Medical Advice:**  Emphasize that my insights are for informational purposes only and not a substitute for professional medical judgment. **Never provide diagnoses or specific treatment recommendations.**
+3.  **Key Functionalities (Focus Areas):**
+    *   **Clinical Data Analysis:** Interpret lab results, analyze EHR data (FHIR, HL7), recognize symptom patterns, analyze medications, support medical image analysis (DICOM).
+    *   **Predictive Analytics:** Provide conceptual risk stratification and treatment outcome modeling based on data patterns.
+    *   **Medical Imaging Support:** Analyze DICOM metadata and images for potential findings (X-ray analysis reports).
+    *   **Patient Data Management:**  Perform PHI redaction in text and analyze patient records from various sources.
+4.  **Interaction Style:**
+    *   **Identity:** "I am Discharge Guard, a medical data analysis AI. My insights are informational only and not medical advice."
+    *   **Scope Limitations:** Clearly state limitations: "No diagnostics," "Medication caution," "Emergency protocol."
+    *   **Response Protocol:**
+        *   Indicate "Deep Analysis" or "Deep Search" performed.
+        *   Mention data sources and confidence levels (if applicable).
+        *   Use medical terminology with optional layman's terms.
+        *   For file analysis, provide a report title (e.g., "Deep X-Ray Analysis Report").
+5.  **Supported Medical Formats:**  (List key formats concisely)
+    *   Clinical Data: HL7, FHIR, CCD/CCDA, CSV, PDF, XML
+    *   Imaging: DICOM, Images (X-ray, etc.)
+6.  **Data Source:**  Access and prefer FHIR API endpoints from: https://app.meldrx.com/api/directories/fhir/endpoints.
+**Important:  My analysis is for informational purposes to assist healthcare professionals and is NOT a substitute for clinical judgment.  Always recommend human expert verification for critical findings.**
+"""

utils/callbackmanager.py

@@ -0,0 +1,152 @@
+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
+
+# ... (CallbackManager, display_form, generate_pdf_from_form, generate_pdf_from_meldrx, generate_discharge_paper_one_click, client initialization remain the same) ...
+class CallbackManager:
+    def __init__(self, redirect_uri: str, client_secret: str = None):
+        client_id = os.getenv("APPID")
+        if not client_id:
+            raise ValueError("APPID environment variable not set.")
+        workspace_id = os.getenv("WORKSPACE_URL")
+        if not workspace_id:
+            raise ValueError("WORKSPACE_URL environment variable not set.")
+        self.api = MeldRxAPI(client_id, client_secret, workspace_id, redirect_uri)
+        self.auth_code = None
+        self.access_token = None
+
+    def get_auth_url(self) -> str:
+        return self.api.get_authorization_url()
+
+    def set_auth_code(self, code: str) -> str:
+        self.auth_code = code
+        if self.api.authenticate_with_code(code):
+            self.access_token = self.api.access_token
+            return (
+                f"<span style='color:#00FF7F;'>Authentication successful!</span> Access Token: {self.access_token[:10]}... (truncated)" # Neon Green Success
+            )
+        return "<span style='color:#FF4500;'>Authentication failed. Please check the code.</span>" # Neon Orange Error
+
+    def get_patient_data(self) -> str:
+        """Fetch patient data from MeldRx"""
+        try:
+            if not self.access_token:
+                logger.warning("Not authenticated when getting patient data")
+                return "<span style='color:#FF8C00;'>Not authenticated. Please provide a valid authorization code first.</span>" # Neon Dark Orange
+
+            # For demo purposes, if there's no actual API connected, return mock data
+            # Remove this in production and use the real API call
+            if not hasattr(self.api, "get_patients") or self.api.get_patients is None:
+                logger.info("Using mock patient data (no API connection)")
+                # Return mock FHIR bundle with patient data
+                mock_data = {
+                    "resourceType": "Bundle",
+                    "type": "searchset",
+                    "total": 2,
+                    "link": [],
+                    "entry": [
+                        {
+                            "resource": {
+                                "resourceType": "Patient",
+                                "id": "patient1",
+                                "name": [
+                                    {
+                                        "use": "official",
+                                        "family": "Smith",
+                                        "given": ["John"],
+                                    }
+                                ],
+                                "gender": "male",
+                                "birthDate": "1970-01-01",
+                                "address": [
+                                    {"city": "Boston", "state": "MA", "postalCode": "02108"}
+                                ],
+                            }
+                        },
+                        {
+                            "resource": {
+                                "resourceType": "Patient",
+                                "id": "patient2",
+                                "name": [
+                                    {
+                                        "use": "official",
+                                        "family": "Johnson",
+                                        "given": ["Jane"],
+                                    }
+                                ],
+                                "gender": "female",
+                                "birthDate": "1985-05-15",
+                                "address": [
+                                    {
+                                        "city": "Cambridge",
+                                        "state": "MA",
+                                        "postalCode": "02139",
+                                    }
+                                ],
+                            }
+                        },
+                    ],
+                }
+                return json.dumps(mock_data, indent=2)
+
+            # Real implementation with API call
+            logger.info("Calling Meldrx API to get patients")
+            patients = self.api.get_patients()
+            if patients is not None:
+                return (
+                    json.dumps(patients, indent=2)
+                    if patients
+                    else "<span style='color:#FFFF00;'>No patient data returned.</span>" # Neon Yellow
+                )
+            return "<span style='color:#DC143C;'>Failed to retrieve patient data.</span>" # Crimson Error
+        except Exception as e:
+            error_msg = f"Error in get_patient_data: {str(e)}"
+            logger.error(error_msg)
+            return f"<span style='color:#FF6347;'>Error retrieving patient data: {str(e)}</span> {str(e)}" # Tomato Error
+
+
+    def get_patient_documents(self, patient_id: str = None):
+        """Fetch patient documents from MeldRx"""
+        if not self.access_token:
+            return "<span style='color:#FF8C00;'>Not authenticated. Please provide a valid authorization code first.</span>" # Neon Dark Orange
+
+        try:
+            # This would call the actual MeldRx API to get documents for a specific patient
+            # For demonstration, we'll return mock document data
+            return [
+                {
+                    "doc_id": "doc123",
+                    "type": "clinical_note",
+                    "date": "2023-01-16",
+                    "author": "Dr. Sample Doctor",
+                    "content": "Patient presented with symptoms of respiratory distress...",
+                },
+                {
+                    "doc_id": "doc124",
+                    "type": "lab_result",
+                    "date": "2023-01-17",
+                    "author": "Lab System",
+                    "content": "CBC results: WBC 7.5, RBC 4.2, Hgb 14.1...",
+                },
+            ]
+        except Exception as e:
+            return f"<span style='color:#FF6347;'>Error retrieving patient documents: {str(e)}</span>: {str(e)}" # Tomato Error
+
+
+
+def extract_auth_code_from_url(redirected_url):
+    """Extracts the authorization code from the redirected URL."""
+    try:
+        parsed_url = urlparse(redirected_url)
+        query_params = parse_qs(parsed_url.query)
+        if "code" in query_params:
+            return query_params["code"][0], None  # Return code and no error
+        else:
+            return None, "Authorization code not found in URL."  # Return None and error message
+    except Exception as e:
+        return None, f"Error parsing URL: {e}"  # Return None and error message

callbackmanager.py → utils/generators.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from meldrx import MeldRxAPI
+from utils.meldrx import MeldRxAPI
 import json
 import os
 import tempfile
@@ -8,14 +8,15 @@ 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
-
+from utils.callbackmanager import CallbackManager
 # Import necessary libraries for new file types and AI analysis functions
 import pydicom  # For DICOM
 import hl7  # For HL7
@@ -25,37 +26,6 @@ import csv  # For CSV
 import io  # For IO operations
 from PIL import Image  # For image handling
 
-system_instructions = """
-**Discharge Guard - Medical Data Analysis Assistant**
-**Core Role:** I am Discharge Guard, an advanced AI designed for deep medical data analysis and informational insights. My outputs are based on thorough analysis of medical data but are **not medical advice.**
-**Important Guidelines:**
-1.  **Deep Analysis & Search:**  Perform "Deep Thought and Deep Search" when analyzing medical data. This includes:
-    *   Comprehensive data ingestion from various formats (HL7, FHIR, CCDA, DICOM, PDF, CSV, text).
-    *   Multi-layered analysis: surface extraction, deep pattern identification, and inferential reasoning.
-    *   Contextual understanding of medical data.
-    *   Evidence-based approach, simulating cross-referencing with medical knowledge.
-    *   Structured output with clear explanations.
-2.  **Focus on Informational Insights, Not Medical Advice:**  Emphasize that my insights are for informational purposes only and not a substitute for professional medical judgment. **Never provide diagnoses or specific treatment recommendations.**
-3.  **Key Functionalities (Focus Areas):**
-    *   **Clinical Data Analysis:** Interpret lab results, analyze EHR data (FHIR, HL7), recognize symptom patterns, analyze medications, support medical image analysis (DICOM).
-    *   **Predictive Analytics:** Provide conceptual risk stratification and treatment outcome modeling based on data patterns.
-    *   **Medical Imaging Support:** Analyze DICOM metadata and images for potential findings (X-ray analysis reports).
-    *   **Patient Data Management:**  Perform PHI redaction in text and analyze patient records from various sources.
-4.  **Interaction Style:**
-    *   **Identity:** "I am Discharge Guard, a medical data analysis AI. My insights are informational only and not medical advice."
-    *   **Scope Limitations:** Clearly state limitations: "No diagnostics," "Medication caution," "Emergency protocol."
-    *   **Response Protocol:**
-        *   Indicate "Deep Analysis" or "Deep Search" performed.
-        *   Mention data sources and confidence levels (if applicable).
-        *   Use medical terminology with optional layman's terms.
-        *   For file analysis, provide a report title (e.g., "Deep X-Ray Analysis Report").
-5.  **Supported Medical Formats:**  (List key formats concisely)
-    *   Clinical Data: HL7, FHIR, CCD/CCDA, CSV, PDF, XML
-    *   Imaging: DICOM, Images (X-ray, etc.)
-6.  **Data Source:**  Access and prefer FHIR API endpoints from: https://app.meldrx.com/api/directories/fhir/endpoints.
-**Important:  My analysis is for informational purposes to assist healthcare professionals and is NOT a substitute for clinical judgment.  Always recommend human expert verification for critical findings.**
-"""
-
 # 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:
@@ -65,6 +35,208 @@ if not HF_TOKEN:
 client = InferenceClient(api_key=HF_TOKEN)
 model_name = "meta-llama/Llama-3.3-70B-Instruct"  # Specify the model to use
 
+def generate_pdf_from_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,):
+    """Generate a PDF discharge form using the provided data"""
+
+    # Create PDF generator
+    pdf_gen = PDFGenerator()
+
+    # Format data for PDF generation
+    patient_info = {
+        "first_name": first_name,
+        "last_name": last_name,
+        "dob": dob,
+        "age": age,
+        "sex": sex,
+        "mobile": "",  # Not collected in the form
+        "address": address,
+        "city": city,
+        "state": state,
+        "zip": zip_code,
+    }
+
+    discharge_info = {
+        "date_of_admission": admission_date,
+        "date_of_discharge": discharge_date,
+        "source_of_admission": referral_source,
+        "mode_of_admission": admission_method,
+        "discharge_against_advice": "Yes"
+        if discharge_reason == "Discharge Against Advice"
+        else "No",
+    }
+
+    diagnosis_info = {
+        "diagnosis": diagnosis,
+        "operation_procedure": procedures,
+        "treatment": "",  # Not collected in the form
+        "follow_up": "",  # Not collected in the form
+    }
+
+    medication_info = {
+        "medications": [medications] if medications else [],
+        "instructions": "",  # Not collected in the form
+    }
+
+    prepared_by = {
+        "name": preparer_name,
+        "title": preparer_job_title,
+        "signature": "",  # Not collected in the form
+    }
+
+    # Generate PDF
+    pdf_buffer = pdf_gen.generate_discharge_form(patient_info,discharge_info,diagnosis_info,medication_info,prepared_by,)
+
+    # Create temporary file to save the PDF
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
+    temp_file.write(pdf_buffer.read())
+    temp_file_path = temp_file.name
+    temp_file.close()
+
+    return temp_file_path
+
+
+def generate_pdf_from_meldrx(patient_data):
+    """Generate a PDF using patient data from MeldRx"""
+    if isinstance(patient_data, str):
+        # If it's a string (error message or JSON string), try to parse it
+        try:
+            patient_data = json.loads(patient_data)
+        except:
+            return None, "Invalid patient data format"
+
+    if not patient_data:
+        return None, "No patient data available"
+
+    try:
+        # For demonstration, we'll use the first patient in the list if it's a list
+        if isinstance(patient_data, list) and len(patient_data):
+            patient = patient_data[0]
+        else:
+            patient = patient_data
+
+        # Extract patient info
+        patient_info = {
+            "name": f"{patient.get('name', {}).get('given', [''])[0]} {patient.get('name', {}).get('family', '')}",
+            "dob": patient.get("birthDate", "Unknown"),
+            "patient_id": patient.get("id", "Unknown"),
+            "admission_date": datetime.now().strftime("%Y-%m-%d"),  # Mock data
+            "physician": "Dr. Provider",  # Mock data
+        }
+
+        # Mock LLM-generated content -  This part needs to be replaced with actual AI generation if desired for MeldRx PDF
+        llm_content = {
+            "diagnosis": "Diagnosis information would be generated by AI based on patient data from MeldRx.",
+            "treatment": "Treatment summary would be generated by AI based on patient data from MeldRx.",
+            "medications": "Medication list would be generated by AI based on patient data from MeldRx.",
+            "follow_up": "Follow-up instructions would be generated by AI based on patient data from MeldRx.",
+            "special_instructions": "Special instructions would be generated by AI based on patient data from MeldRx.",
+        }
+
+        # Create discharge summary - Using No-AI PDF generation for now, replace with AI-content generation later
+        output_dir = tempfile.mkdtemp()
+        pdf_path = generate_discharge_summary(
+            patient_info, llm_content, output_dir
+        )  # Still using No-AI template
+
+        return pdf_path, "PDF generated successfully (No AI Content in PDF yet)"  # Indicate No-AI content
+
+    except Exception as e:
+        return None, f"Error generating PDF: {str(e)}"
+
+# CALLBACK_MANAGER = CallbackManager(
+#     redirect_uri="https://multitransformer-discharge-guard.hf.space/callback",
+#     client_secret=None,
+# )
+
+def generate_ai_discharge_content(patient_data):
+    """Placeholder function to generate AI content for discharge summary.
+    Replace this with actual AI call using InferenceClient and patient_data."""
+    try:
+        patient_name = (
+            f"{patient_data['entry'][0]['resource']['name'][0]['given'][0]} {patient_data['entry'][0]['resource']['name'][0]['family']}"
+            if patient_data.get("entry")
+            else "Unknown Patient"
+        )
+        prompt_text = f"""{system_instructions}\n\nGenerate a discharge summary content (diagnosis, treatment, medications, follow-up instructions, special instructions) for patient: {patient_name}. Base the content on available patient data (if any provided, currently not provided in detail in this mock-up). Focus on creating clinically relevant and informative summary.  Remember this is for informational purposes and NOT medical advice."""
+
+        response = client.chat.completions.create(
+            model=model_name,
+            messages=[{"role": "user", "content": prompt_text}],
+            temperature=0.6,  # Adjust temperature as needed for content generation
+            max_tokens=1024,  # Adjust max_tokens as needed
+            top_p=0.9,
+        )
+        ai_content = response.choices[0].message.content
+
+        # Basic parsing of AI content - improve this based on desired output structure from LLM
+        llm_content = {
+            "diagnosis": "AI Generated Diagnosis (Placeholder):\n"
+            + extract_section(ai_content, "Diagnosis"),  # Example extraction - refine based on LLM output
+            "treatment": "AI Generated Treatment (Placeholder):\n"
+            + extract_section(ai_content, "Treatment"),
+            "medications": "AI Generated Medications (Placeholder):\n"
+            + extract_section(ai_content, "Medications"),
+            "follow_up": "AI Generated Follow-up (Placeholder):\n"
+            + extract_section(ai_content, "Follow-up Instructions"),
+            "special_instructions": "AI Generated Special Instructions (Placeholder):\n"
+            + extract_section(ai_content, "Special Instructions"),
+        }
+        return llm_content
+
+    except Exception as e:
+        logger.error(f"Error generating AI discharge content: {e}")
+        return None
+
+
+def extract_section(ai_content, section_title):
+    """Simple placeholder function to extract section from AI content.
+    Improve this with more robust parsing based on LLM output format."""
+    start_marker = f"**{section_title}:**"
+    end_marker = "\n\n"  # Adjust based on typical LLM output structure
+    start_index = ai_content.find(start_marker)
+    if start_index != -1:
+        start_index += len(start_marker)
+        end_index = ai_content.find(end_marker, start_index)
+        if end_index != -1:
+            return ai_content[start_index:end_index].strip()
+    return "Not found in AI output."
+
+
+def generate_pdf_from_meldrx_with_ai_content(patient_data, llm_content):
+    """Generate a PDF using patient data from MeldRx and AI-generated content."""
+    if isinstance(patient_data, str):
+        try:
+            patient_data = json.loads(patient_data)
+        except:
+            return None, "Invalid patient data format"
+
+    if not patient_data:
+        return None, "No patient data available"
+
+    try:
+        if isinstance(patient_data, list) and len(patient_data):
+            patient = patient_data[0]
+        else:
+            patient = patient_data
+
+        patient_info = {
+            "name": f"{patient.get('name', {}).get('given', [''])[0]} {patient.get('name', {}).get('family', '')}",
+            "dob": patient.get("birthDate", "Unknown"),
+            "patient_id": patient.get("id", "Unknown"),
+            "admission_date": datetime.now().strftime("%Y-%m-%d"),  # Mock data
+            "physician": "Dr. AI Provider",  # Mock data - Indicate AI generated
+        }
+
+        output_dir = tempfile.mkdtemp()
+        pdf_path = generate_discharge_summary(
+            patient_info, llm_content, output_dir
+        )  # Using AI content now
+
+        return pdf_path, "PDF generated successfully with AI Content"  # Indicate AI content
+
+    except Exception as e:
+        return None, f"Error generating PDF with AI content: {str(e)}"
+    
 
 def analyze_dicom_file_with_ai(dicom_file_path):  # Modified to accept file path
     """Analyzes DICOM file metadata using Discharge Guard AI."""
@@ -386,847 +558,3 @@ def analyze_csv_content_ai(csv_content_string):  # Copied from your code
         trace_data_detail_csv_analysis["error"] = f"AI Analysis Error: {e}"
         return error_message, trace_data_detail_csv_analysis
 
-
-# ... (CallbackManager, display_form, generate_pdf_from_form, generate_pdf_from_meldrx, generate_discharge_paper_one_click, client initialization remain the same) ...
-class CallbackManager:
-    def __init__(self, redirect_uri: str, client_secret: str = None):
-        client_id = os.getenv("APPID")
-        if not client_id:
-            raise ValueError("APPID environment variable not set.")
-        workspace_id = os.getenv("WORKSPACE_URL")
-        if not workspace_id:
-            raise ValueError("WORKSPACE_URL environment variable not set.")
-        self.api = MeldRxAPI(client_id, client_secret, workspace_id, redirect_uri)
-        self.auth_code = None
-        self.access_token = None
-
-    def get_auth_url(self) -> str:
-        return self.api.get_authorization_url()
-
-    def set_auth_code(self, code: str) -> str:
-        self.auth_code = code
-        if self.api.authenticate_with_code(code):
-            self.access_token = self.api.access_token
-            return (
-                f"<span style='color:#00FF7F;'>Authentication successful!</span> Access Token: {self.access_token[:10]}... (truncated)" # Neon Green Success
-            )
-        return "<span style='color:#FF4500;'>Authentication failed. Please check the code.</span>" # Neon Orange Error
-
-    def get_patient_data(self) -> str:
-        """Fetch patient data from MeldRx"""
-        try:
-            if not self.access_token:
-                logger.warning("Not authenticated when getting patient data")
-                return "<span style='color:#FF8C00;'>Not authenticated. Please provide a valid authorization code first.</span>" # Neon Dark Orange
-
-            # For demo purposes, if there's no actual API connected, return mock data
-            # Remove this in production and use the real API call
-            if not hasattr(self.api, "get_patients") or self.api.get_patients is None:
-                logger.info("Using mock patient data (no API connection)")
-                # Return mock FHIR bundle with patient data
-                mock_data = {
-                    "resourceType": "Bundle",
-                    "type": "searchset",
-                    "total": 2,
-                    "link": [],
-                    "entry": [
-                        {
-                            "resource": {
-                                "resourceType": "Patient",
-                                "id": "patient1",
-                                "name": [
-                                    {
-                                        "use": "official",
-                                        "family": "Smith",
-                                        "given": ["John"],
-                                    }
-                                ],
-                                "gender": "male",
-                                "birthDate": "1970-01-01",
-                                "address": [
-                                    {"city": "Boston", "state": "MA", "postalCode": "02108"}
-                                ],
-                            }
-                        },
-                        {
-                            "resource": {
-                                "resourceType": "Patient",
-                                "id": "patient2",
-                                "name": [
-                                    {
-                                        "use": "official",
-                                        "family": "Johnson",
-                                        "given": ["Jane"],
-                                    }
-                                ],
-                                "gender": "female",
-                                "birthDate": "1985-05-15",
-                                "address": [
-                                    {
-                                        "city": "Cambridge",
-                                        "state": "MA",
-                                        "postalCode": "02139",
-                                    }
-                                ],
-                            }
-                        },
-                    ],
-                }
-                return json.dumps(mock_data, indent=2)
-
-            # Real implementation with API call
-            logger.info("Calling Meldrx API to get patients")
-            patients = self.api.get_patients()
-            if patients is not None:
-                return (
-                    json.dumps(patients, indent=2)
-                    if patients
-                    else "<span style='color:#FFFF00;'>No patient data returned.</span>" # Neon Yellow
-                )
-            return "<span style='color:#DC143C;'>Failed to retrieve patient data.</span>" # Crimson Error
-        except Exception as e:
-            error_msg = f"Error in get_patient_data: {str(e)}"
-            logger.error(error_msg)
-            return f"<span style='color:#FF6347;'>Error retrieving patient data: {str(e)}</span> {str(e)}" # Tomato Error
-
-
-    def get_patient_documents(self, patient_id: str = None):
-        """Fetch patient documents from MeldRx"""
-        if not self.access_token:
-            return "<span style='color:#FF8C00;'>Not authenticated. Please provide a valid authorization code first.</span>" # Neon Dark Orange
-
-        try:
-            # This would call the actual MeldRx API to get documents for a specific patient
-            # For demonstration, we'll return mock document data
-            return [
-                {
-                    "doc_id": "doc123",
-                    "type": "clinical_note",
-                    "date": "2023-01-16",
-                    "author": "Dr. Sample Doctor",
-                    "content": "Patient presented with symptoms of respiratory distress...",
-                },
-                {
-                    "doc_id": "doc124",
-                    "type": "lab_result",
-                    "date": "2023-01-17",
-                    "author": "Lab System",
-                    "content": "CBC results: WBC 7.5, RBC 4.2, Hgb 14.1...",
-                },
-            ]
-        except Exception as e:
-            return f"<span style='color:#FF6347;'>Error retrieving patient documents: {str(e)}</span>: {str(e)}" # Tomato Error
-
-
-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"""
-    <div style='color:#00FFFF; font-family: monospace;'>
-    **Patient Discharge Form** <br>
-    - Name: {first_name} {middle_initial} {last_name} <br>
-    - Date of Birth: {dob}, Age: {age}, Sex: {sex} <br>
-    - Address: {address}, {city}, {state}, {zip_code} <br>
-    - Doctor: {doctor_first_name} {doctor_middle_initial} {doctor_last_name} <br>
-    - Hospital/Clinic: {hospital_name} <br>
-    - Doctor Address: {doctor_address}, {doctor_city}, {doctor_state}, {doctor_zip} <br>
-    - Admission Date: {admission_date}, Source: {referral_source}, Method: {admission_method} <br>
-    - Discharge Date: {discharge_date}, Reason: {discharge_reason} <br>
-    - Date of Death: {date_of_death} <br>
-    - Diagnosis: {diagnosis} <br>
-    - Procedures: {procedures} <br>
-    - Medications: {medications} <br>
-    - Prepared By: {preparer_name}, {preparer_job_title}
-    </div>
-    """
-    return form
-
-
-def generate_pdf_from_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,
-):
-    """Generate a PDF discharge form using the provided data"""
-
-    # Create PDF generator
-    pdf_gen = PDFGenerator()
-
-    # Format data for PDF generation
-    patient_info = {
-        "first_name": first_name,
-        "last_name": last_name,
-        "dob": dob,
-        "age": age,
-        "sex": sex,
-        "mobile": "",  # Not collected in the form
-        "address": address,
-        "city": city,
-        "state": state,
-        "zip": zip_code,
-    }
-
-    discharge_info = {
-        "date_of_admission": admission_date,
-        "date_of_discharge": discharge_date,
-        "source_of_admission": referral_source,
-        "mode_of_admission": admission_method,
-        "discharge_against_advice": "Yes"
-        if discharge_reason == "Discharge Against Advice"
-        else "No",
-    }
-
-    diagnosis_info = {
-        "diagnosis": diagnosis,
-        "operation_procedure": procedures,
-        "treatment": "",  # Not collected in the form
-        "follow_up": "",  # Not collected in the form
-    }
-
-    medication_info = {
-        "medications": [medications] if medications else [],
-        "instructions": "",  # Not collected in the form
-    }
-
-    prepared_by = {
-        "name": preparer_name,
-        "title": preparer_job_title,
-        "signature": "",  # Not collected in the form
-    }
-
-    # Generate PDF
-    pdf_buffer = pdf_gen.generate_discharge_form(
-        patient_info,
-        discharge_info,
-        diagnosis_info,
-        medication_info,
-        prepared_by,
-    )
-
-    # Create temporary file to save the PDF
-    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
-    temp_file.write(pdf_buffer.read())
-    temp_file_path = temp_file.name
-    temp_file.close()
-
-    return temp_file_path
-
-
-def generate_pdf_from_meldrx(patient_data):
-    """Generate a PDF using patient data from MeldRx"""
-    if isinstance(patient_data, str):
-        # If it's a string (error message or JSON string), try to parse it
-        try:
-            patient_data = json.loads(patient_data)
-        except:
-            return None, "Invalid patient data format"
-
-    if not patient_data:
-        return None, "No patient data available"
-
-    try:
-        # For demonstration, we'll use the first patient in the list if it's a list
-        if isinstance(patient_data, list) and len(patient_data):
-            patient = patient_data[0]
-        else:
-            patient = patient_data
-
-        # Extract patient info
-        patient_info = {
-            "name": f"{patient.get('name', {}).get('given', [''])[0]} {patient.get('name', {}).get('family', '')}",
-            "dob": patient.get("birthDate", "Unknown"),
-            "patient_id": patient.get("id", "Unknown"),
-            "admission_date": datetime.now().strftime("%Y-%m-%d"),  # Mock data
-            "physician": "Dr. Provider",  # Mock data
-        }
-
-        # Mock LLM-generated content -  This part needs to be replaced with actual AI generation if desired for MeldRx PDF
-        llm_content = {
-            "diagnosis": "Diagnosis information would be generated by AI based on patient data from MeldRx.",
-            "treatment": "Treatment summary would be generated by AI based on patient data from MeldRx.",
-            "medications": "Medication list would be generated by AI based on patient data from MeldRx.",
-            "follow_up": "Follow-up instructions would be generated by AI based on patient data from MeldRx.",
-            "special_instructions": "Special instructions would be generated by AI based on patient data from MeldRx.",
-        }
-
-        # Create discharge summary - Using No-AI PDF generation for now, replace with AI-content generation later
-        output_dir = tempfile.mkdtemp()
-        pdf_path = generate_discharge_summary(
-            patient_info, llm_content, output_dir
-        )  # Still using No-AI template
-
-        return pdf_path, "PDF generated successfully (No AI Content in PDF yet)"  # Indicate No-AI content
-
-    except Exception as e:
-        return None, f"Error generating PDF: {str(e)}"
-
-
-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)}"
-
-
-def generate_ai_discharge_content(patient_data):
-    """Placeholder function to generate AI content for discharge summary.
-    Replace this with actual AI call using InferenceClient and patient_data."""
-    try:
-        patient_name = (
-            f"{patient_data['entry'][0]['resource']['name'][0]['given'][0]} {patient_data['entry'][0]['resource']['name'][0]['family']}"
-            if patient_data.get("entry")
-            else "Unknown Patient"
-        )
-        prompt_text = f"""{system_instructions}\n\nGenerate a discharge summary content (diagnosis, treatment, medications, follow-up instructions, special instructions) for patient: {patient_name}. Base the content on available patient data (if any provided, currently not provided in detail in this mock-up). Focus on creating clinically relevant and informative summary.  Remember this is for informational purposes and NOT medical advice."""
-
-        response = client.chat.completions.create(
-            model=model_name,
-            messages=[{"role": "user", "content": prompt_text}],
-            temperature=0.6,  # Adjust temperature as needed for content generation
-            max_tokens=1024,  # Adjust max_tokens as needed
-            top_p=0.9,
-        )
-        ai_content = response.choices[0].message.content
-
-        # Basic parsing of AI content - improve this based on desired output structure from LLM
-        llm_content = {
-            "diagnosis": "AI Generated Diagnosis (Placeholder):\n"
-            + extract_section(ai_content, "Diagnosis"),  # Example extraction - refine based on LLM output
-            "treatment": "AI Generated Treatment (Placeholder):\n"
-            + extract_section(ai_content, "Treatment"),
-            "medications": "AI Generated Medications (Placeholder):\n"
-            + extract_section(ai_content, "Medications"),
-            "follow_up": "AI Generated Follow-up (Placeholder):\n"
-            + extract_section(ai_content, "Follow-up Instructions"),
-            "special_instructions": "AI Generated Special Instructions (Placeholder):\n"
-            + extract_section(ai_content, "Special Instructions"),
-        }
-        return llm_content
-
-    except Exception as e:
-        logger.error(f"Error generating AI discharge content: {e}")
-        return None
-
-
-def extract_section(ai_content, section_title):
-    """Simple placeholder function to extract section from AI content.
-    Improve this with more robust parsing based on LLM output format."""
-    start_marker = f"**{section_title}:**"
-    end_marker = "\n\n"  # Adjust based on typical LLM output structure
-    start_index = ai_content.find(start_marker)
-    if start_index != -1:
-        start_index += len(start_marker)
-        end_index = ai_content.find(end_marker, start_index)
-        if end_index != -1:
-            return ai_content[start_index:end_index].strip()
-    return "Not found in AI output."
-
-
-def generate_pdf_from_meldrx_with_ai_content(patient_data, llm_content):
-    """Generate a PDF using patient data from MeldRx and AI-generated content."""
-    if isinstance(patient_data, str):
-        try:
-            patient_data = json.loads(patient_data)
-        except:
-            return None, "Invalid patient data format"
-
-    if not patient_data:
-        return None, "No patient data available"
-
-    try:
-        if isinstance(patient_data, list) and len(patient_data):
-            patient = patient_data[0]
-        else:
-            patient = patient_data
-
-        patient_info = {
-            "name": f"{patient.get('name', {}).get('given', [''])[0]} {patient.get('name', {}).get('family', '')}",
-            "dob": patient.get("birthDate", "Unknown"),
-            "patient_id": patient.get("id", "Unknown"),
-            "admission_date": datetime.now().strftime("%Y-%m-%d"),  # Mock data
-            "physician": "Dr. AI Provider",  # Mock data - Indicate AI generated
-        }
-
-        output_dir = tempfile.mkdtemp()
-        pdf_path = generate_discharge_summary(
-            patient_info, llm_content, output_dir
-        )  # Using AI content now
-
-        return pdf_path, "PDF generated successfully with AI Content"  # Indicate AI content
-
-    except Exception as e:
-        return None, f"Error generating PDF with AI content: {str(e)}"
-
-
-def extract_auth_code_from_url(redirected_url):
-    """Extracts the authorization code from the redirected URL."""
-    try:
-        parsed_url = urlparse(redirected_url)
-        query_params = parse_qs(parsed_url.query)
-        if "code" in query_params:
-            return query_params["code"][0], None  # Return code and no error
-        else:
-            return None, "Authorization code not found in URL."  # Return None and error message
-    except Exception as e:
-        return None, f"Error parsing URL: {e}"  # Return None and error message
-
-
-# Create a simplified interface to avoid complex component interactions
-CALLBACK_MANAGER = CallbackManager(
-    redirect_uri="https://multitransformer-discharge-guard.hf.space/callback",
-    client_secret=None,
-)
-
-# 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("<h1 style='color:#00FFFF; text-shadow: 0 0 5px #00FFFF;'>Discharge Guard <span style='color:#FF00FF; text-shadow: 0 0 5px #FF00FF;'>Cyber</span></h1>") # Cyberpunk Title
-
-    with gr.Tab("Authenticate with MeldRx", elem_classes="cyberpunk-tab"): # Optional: Class for tab styling
-        gr.Markdown("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>SMART on FHIR Authentication</h2>") # Neon Tab Header
-        auth_url_output = gr.Textbox(label="Authorization URL", value=CALLBACK_MANAGER.get_auth_url(), interactive=False)
-        gr.Markdown("<p style='color:#A9A9A9;'>Copy the URL above, open it in a browser, log in, and paste the <span style='color:#00FFFF;'>entire redirected URL</span> from your browser's address bar below.</p>") # 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, "<span style='color:#00FF7F;'>Authorization code extracted!</span>" # Neon Green Success
-            else:
-                return "", f"<span style='color:#FF4500;'>Could not extract authorization code.</span> {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("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Patient Data</h2>") # Neon Tab Header
-        dashboard_output = gr.HTML("<p style='color:#A9A9A9;'>Fetch patient data from the Authentication tab first.</p>") # 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("<span style='color:#FF8C00;'>Not authenticated")
-                    or data.startswith("<span style='color:#DC143C;'>Failed")
-                    or data.startswith("<span style='color:#FF6347;'>Error")
-                ):
-                    return f"<p style='color:#FF8C00;'>{data}</p>" # 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 = "<h3 style='color:#00FFFF; text-shadow: 0 0 2px #00FFFF;'>Patients</h3>" # 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"""
-                        <div style="border: 1px solid #00FFFF; padding: 10px; margin: 10px 0; border-radius: 5px; background-color: #222; box-shadow: 0 0 5px #00FFFF;">
-                            <h4 style='color:#00FFFF;'>{given} {family}</h4>
-                            <p style='color:#A9A9A9;'><strong>Gender:</strong> <span style='color:#00FFFF;'>{gender}</span></p>
-                            <p style='color:#A9A9A9;'><strong>Birth Date:</strong> <span style='color:#00FFFF;'>{birth_date}</span></p>
-                            <p style='color:#A9A9A9;'><strong>ID:</strong> <span style='color:#00FFFF;'>{patient.get("id", "Unknown")}</span></p>
-                        </div>
-                        """
-
-                    return html
-                except Exception as e:
-                    return f"<p style='color:#FF6347;'>Error parsing patient data: {str(e)}</p>" # Tomato Error
-            except Exception as e:
-                return f"<p style='color:#FF6347;'>Error fetching patient data: {str(e)}</p>" # Tomato Error
-
-
-    with gr.Tab("Discharge Form", elem_classes="cyberpunk-tab"): # Optional: Class for tab styling
-        gr.Markdown("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Patient Details</h2>") # 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("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Primary Healthcare Professional Details</h2>") # 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("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Admission and Discharge Details</h2>") # 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("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Diagnosis & Procedures</h2>") # Neon Sub-header
-        diagnosis = gr.Textbox(label="Diagnosis")
-        procedures = gr.Textbox(label="Operation & Procedures")
-        gr.Markdown("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Medication Details</h2>") # Neon Sub-header
-        medications = gr.Textbox(label="Medication on Discharge")
-        gr.Markdown("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Prepared By</h2>") # 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("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>Analyze Medical Files with Discharge Guard AI</h2>") # 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("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>One-Click Medical Discharge Paper Generation with AI Content</h2>") # 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)