Update callbackmanager.py

It will use AI LLMA 3.1 need pk of it

callbackmanager.py

@@ -6,6 +6,7 @@ import tempfile
 from datetime import datetime
 import traceback
 import logging
+from huggingface_hub import InferenceClient # Import InferenceClient
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
@@ -20,24 +21,331 @@ import hl7  # For HL7
 from xml.etree import ElementTree # For XML and CCDA
 from pypdf import PdfReader # For PDF
 import csv # For CSV
-# Assuming your AI analysis functions are in the same script or imported
-# For now, let's define placeholder AI analysis functions for Gradio context
-def analyze_dicom_file_with_ai(dicom_file):
-    return "DICOM Analysis Report (Placeholder - Real AI integration needed)"
+import io # For IO operations
+from PIL import Image # For image handling
+import google.generativeai as genai # For Gemini AI
+
+# Access Google API key from Streamlit secrets (replace with your method if not using streamlit secrets in Gradio)
+GOOGLE_API_KEY = st.secrets["gemini_api"]["api_key"] # Assuming you have streamlit secrets configured in Gradio or replace with other method
+genai.configure(api_key=GOOGLE_API_KEY)
+
+# Generation Configuration (adjust as needed) - copy from your streamlit code
+generation_config = {
+    "temperature": 0.7,
+    "top_p": 0.95,
+    "top_k": 64,
+    "max_output_tokens": 65536,
+    "response_mime_type": "text/plain",
+}
+
+# System Instruction - copy from your streamlit code
+system_instructions = """
+**Identity & Core Purpose**
+... (rest of your system instructions string from streamlit code) ...
+Very Important: Use the API Directories fhir endpoints FROM THIS LINK: https://app.meldrx.com/api/directories/fhir/endpoints
+"""
+
+# Define AI models - copy from your streamlit code
+text_model = genai.GenerativeModel(
+    model_name="gemini-2.0-flash-thinking-exp-01-21",
+    generation_config=generation_config,
+    system_instruction=system_instructions,  # Apply system instruction here
+)
+vision_model = genai.GenerativeModel( # Use gemini-pro-vision for image analysis
+    model_name="gemini-2.0-flash-thinking-exp-01-21", generation_config=generation_config
+)
+
+
+# Copy AI analysis functions from your streamlit code (analyze_dicom_file_with_ai, analyze_hl7_file_with_ai, etc.)
+# Paste all your AI analysis functions here, for example:
+def analyze_dicom_file_with_ai(dicom_file_path): # Modified to accept file path
+    """Analyzes DICOM file metadata using DocuNexus AI."""
+    try:
+        dicom_file = pydicom.dcmread(dicom_file_path) # Read dicom using path
+        dicom_metadata_json = dicom_file.to_json_dict()
+        prediction_response, trace_data_dicom_ai = analyze_dicom_content_ai(dicom_metadata_json) # Call content analysis
+        if prediction_response:
+            report = f"DocuNexus AI Analysis of DICOM Metadata:\n\nDICOM Metadata Analysis Report:\n{prediction_response}\n\nDisclaimer: The DocuNexus AGI-generated analysis is for conceptual informational purposes only and may or **NOT medical advice.** Analysis is based on DICOM *metadata* and not image interpretation."
+            return report
+        else:
+            error_message = f"AI Analysis from DICOM Metadata: No predictions generated or analysis encountered an issue."
+            if trace_data_dicom_ai and "error" in trace_data_dicom_ai:
+                error_message += f"\nAI Analysis Failed: {trace_data_dicom_ai['error']}"
+            return error_message
+
+    except Exception as e:
+        return f"Error during DICOM file processing in analyze_dicom_file_with_ai: {e}"
+
+def analyze_dicom_content_ai(dicom_metadata_json): # Copied from your code
+    """Analyzes DICOM metadata JSON content using DocuNexus AI."""
+    prompt_text = f"""Perform a **deep and comprehensive analysis** of the following DICOM metadata in JSON format to provide a **structured summary and identify potential clinically relevant information with deep insights**. Focus not just on summarizing fields, but on **interpreting their clinical significance, identifying subtle patterns, and drawing inferences about the study's implications**. Think like an experienced radiologist reviewing this metadata for crucial diagnostic clues. Remember this is metadata, not the image itself, so focus on what can be gleaned from the data itself. Provide a "**Deep DICOM Metadata Analysis Report**". Important: Use the API Directories fhir endpoints FROM THIS LINK: https://app.meldrx.com/api/directories/fhir/endpoints.
+
+    **DICOM Metadata (JSON):**
+    ```json
+    {json.dumps(dicom_metadata_json, indent=2)}
+    ```
+
+    ... (rest of your prompt_text for DICOM metadata analysis) ...
+        *   Remember, this deep analysis is for conceptual informational purposes only and **NOT medical advice.** Focus on deep summarization and structuring the extracted metadata in a highly clinically relevant way.
+    """
+
+    trace_data_detail_dicom_analysis = {
+        "prompt": "DICOM Metadata Analysis Request",
+        "language": "English",
+        "response_length": "Comprehensive",
+        "model_name": "DocuNexus v1.0",
+        "generated_text": "N/A",
+        "input_file_types": ["DICOM Metadata JSON"],
+        "mode": "DICOM Metadata Analysis",
+        "candidates": [],
+        "usage_metadata": {},
+        "prompt_feedback": "N/A",
+    }
+
+    try:
+        ai_response = text_model.generate_content(prompt_text) # Use text_model
+        the_response = ai_response.text
+        return the_response, trace_data_detail_dicom_analysis
+
+    except Exception as e:
+        error_message = f"AI Analysis Error in analyze_dicom_content_ai (DICOM Metadata): {e}"
+        trace_data_detail_dicom_analysis["error"] = f"AI Analysis Error: {e}"
+        return error_message, trace_data_detail_dicom_analysis
+
+# ... (Paste other AI analysis functions: analyze_hl7_file_with_ai, analyze_cda_xml_file_with_ai, analyze_pdf_file_with_ai, analyze_csv_file_with_ai here - ensure to adapt file reading for Gradio file paths if necessary) ...
+def analyze_hl7_file_with_ai(hl7_file_path):
+    """Analyzes HL7 file content using DocuNexus AI."""
+    try:
+        with open(hl7_file_path, 'r') as f: # Open file using path
+            hl7_message_raw = f.read()
+        prediction_response, trace_data_hl7_ai = analyze_hl7_content_ai(hl7_message_raw)
+
+        if prediction_response:
+            report = f"DocuNexus AI Analysis of HL7 Message:\n\nHL7 Message Analysis Report:\n{prediction_response}\n\n**Disclaimer:** The DocuNexus AGI-generated analysis is for conceptual informational purposes only and may or **NOT medical advice.** Analysis is based on HL7 message content."
+            return report
+        else:
+            error_message = f"AI Analysis from HL7 Message: No predictions generated or analysis encountered an issue."
+            if trace_data_hl7_ai and "error" in trace_data_hl7_ai:
+                error_message += f"AI Analysis Failed: {trace_data_hl7_ai['error']}"
+            return error_message
+
+    except Exception as e:
+        return f"Error during HL7 file processing in analyze_hl7_file_with_ai: {e}"
+
+def analyze_hl7_content_ai(hl7_message_string): # Copied from your code
+    """Analyzes HL7 message content using DocuNexus AI."""
+    prompt_text = f"""Conduct a **deep and thorough analysis** of the following HL7 message content to provide a **structured summary and identify key clinical information with deep understanding**.  Go beyond basic parsing; aim to **interpret the clinical narrative** embedded within the HL7 message.  **Engage in deep search to contextualize medical codes and terminology**. Provide a "**Comprehensive HL7 Message Analysis Report**".
+
+    **HL7 Message Content:**
+    ```hl7
+    {hl7_message_string}
+    ```
+
+    ... (rest of your prompt_text for HL7 analysis) ...
+        *   Remember, this deep analysis is for conceptual informational purposes only and **NOT medical advice.** Focus on deep summarization and structuring the extracted data in a highly clinically relevant way based on the HL7 content.
+    """
+    # ... (rest of the function code) ...
+    trace_data_detail_hl7_analysis = {
+        "prompt": "HL7 Message Analysis Request",
+        "language": "English",
+        "response_length": "Comprehensive",
+        "model_name": "DocuNexus v1.0",
+        "generated_text": "N/A",
+        "input_file_types": ["HL7 Message"],
+        "mode": "HL7 Message Analysis",
+        "candidates": [],
+        "usage_metadata": {},
+        "prompt_feedback": "N/A",
+    }
+
+    try:
+        ai_response = text_model.generate_content(prompt_text) # Use text model
+        the_response = ai_response.text
+        return the_response, trace_data_detail_hl7_analysis
+
+    except Exception as e:
+        error_message = f"AI Analysis Error in analyze_hl7_content_ai (HL7 Message): {e}"
+        trace_data_detail_hl7_analysis["error"] = f"AI Analysis Error: {e}"
+        return error_message, trace_data_detail_hl7_analysis
+
+
+def analyze_cda_xml_file_with_ai(cda_xml_file_path): # Modified to accept file path
+    """Analyzes generic CDA or XML file content using DocuNexus AI (more generalized version) Important: Use the API Directories fhir endpoints FROM THIS LINK: https://app.meldrx.com/api/directories/fhir/endpoints."""
+    try:
+        with open(cda_xml_file_path, 'r') as f: # Open file using path
+            cda_xml_content = f.read()
+        prediction_response, trace_data_cda_xml_ai = analyze_cda_xml_content_ai(
+            cda_xml_content
+        )
+        if prediction_response:
+            report = f"DocuNexus AI Analysis of Medical XML/CDA Data:\n\nMedical Document Analysis Report:\n{prediction_response}\n\n**Disclaimer:** The DocuNexus AGI-generated analysis is for conceptual informational purposes only and may or **NOT medical advice.** Analysis is based on XML/CDA content."
+            return report
+        else:
+            error_message = f"AI Analysis from XML/CDA Data: No predictions generated or analysis encountered an issue."
+            if trace_data_cda_xml_ai and "error" in trace_data_cda_xml_ai:
+                error_message += f"AI Analysis Failed: {trace_data_cda_xml_ai['error']}"
+            return error_message
+
+    except Exception as e:
+        return f"Error during XML/CDA file processing in analyze_cda_xml_file_with_ai: {e}"
+
+def analyze_cda_xml_content_ai(cda_xml_content): # Copied from your code
+    """Analyzes generic CDA or XML content using DocuNexus AI (more generalized version)."""
 
-def analyze_hl7_file_with_ai(hl7_file):
-    return "HL7 Analysis Report (Placeholder - Real AI integration needed)"
+    prompt_text = f"""Analyze the following medical XML/CDA content to provide a **structured and comprehensive patient data analysis**, similar to how a medical professional would review a patient's chart or a clinical document. You need to parse the XML structure yourself to extract the relevant information. Use bullet points, tables, or numbered steps for complex tasks. Provide a "Medical Document Analysis" report.
 
-def analyze_cda_xml_file_with_ai(cda_xml_file):
-    return "CCDA/XML Analysis Report (Placeholder - Real AI integration needed)"
+    **Instructions for DocuNexus AI:**
 
-def analyze_pdf_file_with_ai(pdf_file):
-    return "PDF Analysis Report (Placeholder - Real AI integration needed)"
+    1.  **Parse the XML content above.** Understand the XML structure to identify sections that are relevant to clinical information.  For CDA specifically, look for sections like Problems, Medications, Allergies, Encounters, Results, and Vital Signs. For generic medical XML, adapt based on the tags present.
+    2.  **Extract and Summarize Key Medical Information:** Focus on extracting the following information if present in the XML:
+        *   **Patient Demographics Summary:** (If available, summarize demographic details)
+        ... (rest of your prompt_text for CDA/XML analysis) ...
+        *   Remember, this analysis is for conceptual informational purposes only and **NOT medical advice.** Focus on summarizing and structuring the extracted data in a clinically relevant way based on the XML/CDA content.
 
-def analyze_csv_file_with_ai(csv_file):
-    return "CSV Analysis Report (Placeholder - Real AI integration needed)"
+    """
+
+    trace_data_detail_cda_xml_analysis = {
+        "prompt": "Generic CDA/XML Analysis Request",
+        "language": "English",
+        "response_length": "Comprehensive",
+        "model_name": "DocuNexus v1.0",
+        "generated_text": "N/A",
+        "input_file_types": ["CDA/XML"],
+        "mode": "Generic XML/CDA Analysis",
+        "candidates": [],
+        "usage_metadata": {},
+        "prompt_feedback": "N/A",
+    }
 
+    try:
+        ai_response = text_model.generate_content(prompt_text) # Use text model
+        the_response = ai_response.text
+        return the_response, trace_data_detail_cda_xml_analysis
+
+    except Exception as e:
+        error_message = f"AI Analysis Error in analyze_cda_xml_content_ai (Generic XML/CDA): {e}"
+        trace_data_detail_cda_xml_analysis["error"] = f"AI Analysis Error: {e}"
+        return error_message, trace_data_detail_cda_xml_analysis
 
+
+def analyze_pdf_file_with_ai(pdf_file_path): # Modified to accept file path
+    """Analyzes PDF file content using DocuNexus AI."""
+    try:
+        with open(pdf_file_path, 'rb') as f: # Open file in binary mode for PdfReader
+            pdf_file = f # Pass file object to PdfReader
+            pdf_reader = PdfReader(pdf_file)
+            text_content = ""
+            for page in pdf_reader.pages:
+                text_content += page.extract_text()
+
+        prediction_response, trace_data_pdf_ai = analyze_pdf_content_ai(text_content)
+
+        if prediction_response:
+            report = f"DocuNexus AI Analysis of PDF Content:\n\nMedical Report Analysis Report:\n{prediction_response}\n\n**Disclaimer:** The DocuNexus AGI-generated analysis is for conceptual informational purposes only and may or **NOT medical advice.** Analysis is based on PDF text content."
+            return report
+        else:
+            error_message = f"AI Analysis from PDF Content: No predictions generated or analysis encountered an issue."
+            if trace_data_pdf_ai and "error" in trace_data_pdf_ai:
+                error_message += f"AI Analysis Failed: {trace_data_pdf_ai['error']}"
+            return error_message
+
+    except Exception as e:
+        return f"Error during PDF file processing in analyze_pdf_file_with_ai: {e}"
+
+def analyze_pdf_content_ai(pdf_text_content): # Copied from your code
+    """Analyzes PDF text content using DocuNexus AI."""
+    prompt_text = f"""Analyze the following medical PDF text content to provide a **structured summary and identify key clinical information**. Focus on patient demographics, medical history, findings, diagnoses, medications, recommendations, and any important clinical details conveyed in the document. Provide a "Medical Report Analysis" report.
+
+    **Medical PDF Text Content:**
+    ```text
+    {pdf_text_content}
+    ```
+
+    ... (rest of your prompt_text for PDF analysis) ...
+        *   Remember, this analysis is for conceptual informational purposes only and **NOT medical advice.** Focus on summarizing and structuring the extracted data in a clinically relevant way based on the PDF content.
+    """
+
+    trace_data_detail_pdf_analysis = {
+        "prompt": "PDF Text Analysis Request",
+        "language": "English",
+        "response_length": "Comprehensive",
+        "model_name": "DocuNexus v1.0",
+        "generated_text": "N/A",
+        "input_file_types": ["PDF Text"],
+        "mode": "PDF Text Analysis",
+        "candidates": [],
+        "usage_metadata": {},
+        "prompt_feedback": "N/A",
+    }
+
+    try:
+        ai_response = text_model.generate_content(prompt_text) # Use text model
+        the_response = ai_response.text
+        return the_response, trace_data_detail_pdf_analysis
+
+    except Exception as e:
+        error_message = f"AI Analysis Error in analyze_pdf_content_ai (PDF Text): {e}"
+        trace_data_detail_pdf_analysis["error"] = f"AI Analysis Error: {e}"
+        return error_message, trace_data_detail_pdf_analysis
+
+
+def analyze_csv_file_with_ai(csv_file_path): # Modified to accept file path
+    """Analyzes CSV file content using DocuNexus AI."""
+    try:
+        with open(csv_file_path, 'r') as f: # Open file using path
+            csv_content = f.read()
+        prediction_response, trace_data_csv_ai = analyze_csv_content_ai(csv_content)
+
+        if prediction_response:
+            report = f"DocuNexus AI Analysis of CSV Data:\n\nData Analysis Report:\n{prediction_response}\n\n**Disclaimer:** The DocuNexus AGI-generated analysis is for conceptual informational purposes only and may or **NOT medical advice.** Analysis is based on CSV data content."
+            return report
+        else:
+            error_message = f"AI Analysis from CSV Data: No predictions generated or analysis encountered an issue."
+            if trace_data_csv_ai and "error" in trace_data_csv_ai:
+                error_message += f"AI Analysis Failed: {trace_data_csv_ai['error']}"
+            return error_message
+
+    except Exception as e:
+        return f"Error during CSV file processing in analyze_csv_file_with_ai: {e}"
+
+def analyze_csv_content_ai(csv_content_string): # Copied from your code
+    """Analyzes CSV content (string) using DocuNexus AI."""
+    prompt_text = f"""Analyze the following medical CSV data to provide a **structured summary and identify potential clinical insights**. Assume the CSV represents patient-related medical data. Focus on understanding the columns, summarizing key data points, identifying trends or patterns, and noting any potential clinical significance of the data. Provide a "Data Analysis" report.
+
+    **Medical CSV Data:**
+    ```csv
+    {csv_content_string}
+    ```
+
+    ... (rest of your prompt_text for CSV analysis) ...
+        *   Remember, this analysis is for conceptual informational purposes only and **NOT medical advice.** Focus on summarizing and structuring the data in a clinically relevant way based on the CSV content.
+    """
+
+    trace_data_detail_csv_analysis = {
+        "prompt": "CSV Data Analysis Request",
+        "language": "English",
+        "response_length": "Comprehensive",
+        "model_name": "DocuNexus v1.0",
+        "generated_text": "N/A",
+        "input_file_types": ["CSV Data"],
+        "mode": "CSV Data Analysis",
+        "candidates": [],
+        "usage_metadata": {},
+        "prompt_feedback": "N/A",
+    }
+
+    try:
+        ai_response = text_model.generate_content(prompt_text) # Use text model
+        the_response = ai_response.text
+        return the_response, trace_data_detail_csv_analysis
+
+    except Exception as e:
+        error_message = f"AI Analysis Error in analyze_csv_content_ai (CSV Data): {e}"
+        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")
@@ -323,6 +631,7 @@ def generate_discharge_paper_one_click():
         return None, f"Error during discharge paper generation: {str(e)}"
 
 
+
 # Create a simplified interface to avoid complex component interactions
 CALLBACK_MANAGER = CallbackManager(
     redirect_uri="https://multitransformer-discharge-guard.hf.space/callback",
@@ -331,7 +640,7 @@ CALLBACK_MANAGER = CallbackManager(
 
 # Create the UI
 with gr.Blocks() as demo:
-    gr.Markdown("# Patient Discharge Form with MeldRx & Medical File Analysis")
+    gr.Markdown("# Patient Discharge Form with MeldRx & Medical File Analysis + AI") # Updated title
 
     with gr.Tab("Authenticate with MeldRx"):
         gr.Markdown("## SMART on FHIR Authentication")
@@ -345,9 +654,9 @@ with gr.Blocks() as demo:
         patient_data_output = gr.Textbox(label="Patient Data", lines=10)
 
         # Add button to generate PDF from MeldRx data
-        meldrx_pdf_button = gr.Button("Generate PDF from MeldRx Data")
-        meldrx_pdf_status = gr.Textbox(label="PDF Generation Status")
-        meldrx_pdf_download = gr.File(label="Download Generated PDF")
+        meldrx_pdf_button = gr.Button("Generate PDF from MeldRx Data (No AI)") # 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
 
         auth_submit.click(fn=CALLBACK_MANAGER.set_auth_code, inputs=auth_code_input, outputs=auth_result)
 
@@ -375,7 +684,7 @@ with gr.Blocks() as demo:
                         if resource.get("resourceType") == "Patient":
                             patients.append(resource)
 
-                    # Generate HTML for each patient
+                    # Generate HTML card
                     html = "<h3>Patients</h3>"
                     for patient in patients:
                         # Extract name
@@ -451,11 +760,11 @@ with gr.Blocks() as demo:
         # Add buttons for both display form and generate PDF
         with gr.Row():
             submit_display = gr.Button("Display Form")
-            submit_pdf = gr.Button("Generate PDF")
+            submit_pdf = gr.Button("Generate PDF (No AI)") # Renamed button to clarify no AI
 
         # Output areas
         form_output = gr.Markdown()
-        pdf_output = gr.File(label="Download PDF")
+        pdf_output = gr.File(label="Download PDF (No AI)") # Renamed output to clarify no AI
 
         # Connect the display form button
         submit_display.click(
@@ -470,7 +779,7 @@ with gr.Blocks() as demo:
             outputs=form_output
         )
 
-        # Connect the generate PDF button
+        # Connect the generate PDF button (No AI version)
         submit_pdf.click(
             generate_pdf_from_form,
             inputs=[
@@ -507,38 +816,38 @@ with gr.Blocks() as demo:
             analyze_ccd_button = gr.Button("Analyze CCD with AI") # Redundant
 
 
-        # Connect AI Analysis Buttons (using placeholder functions for now)
+        # Connect AI Analysis Buttons - using REAL AI functions now
         analyze_dicom_button.click(
-            lambda file: analyze_dicom_file_with_ai(file.name) if file else "No DICOM file uploaded",
+            analyze_dicom_file_with_ai, # Call REAL AI function
             inputs=dicom_file, outputs=dicom_ai_output
         )
         analyze_hl7_button.click(
-            lambda file: analyze_hl7_file_with_ai(file.name) if file else "No HL7 file uploaded",
+            analyze_hl7_file_with_ai, # Call REAL AI function
             inputs=hl7_file, outputs=hl7_ai_output
         )
         analyze_xml_button.click(
-            lambda file: analyze_cda_xml_file_with_ai(file.name) if file else "No XML file uploaded", # Using CCDA/XML analyzer for generic XML for now
+            analyze_cda_xml_file_with_ai, # Call REAL AI function
             inputs=xml_file, outputs=xml_ai_output
         )
         analyze_ccda_button.click(
-            lambda file: analyze_cda_xml_file_with_ai(file.name) if file else "No CCDA file uploaded", # Using CCDA/XML analyzer
+            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
-            lambda file: analyze_cda_xml_file_with_ai(file.name) if file else "No CCD file uploaded", # Using CCDA/XML analyzer
+            analyze_cda_xml_file_with_ai, # Call REAL AI function
             inputs=ccd_file, outputs=ccd_ai_output
         )
 
-    with gr.Tab("One-Click Discharge Paper"): # New Tab for One-Click Discharge Paper
-        gr.Markdown("## One-Click Medical Discharge Paper Generation")
-        one_click_pdf_button = gr.Button("Generate Discharge Paper (One-Click)")
-        one_click_pdf_status = gr.Textbox(label="Discharge Paper Generation Status")
-        one_click_pdf_download = gr.File(label="Download Discharge Paper")
+    with gr.Tab("One-Click Discharge Paper (AI)"): # New Tab for One-Click Discharge Paper with AI
+        gr.Markdown("## One-Click Medical Discharge Paper Generation with AI Content") # Updated title
+        one_click_ai_pdf_button = gr.Button("Generate Discharge Paper with AI (One-Click)") # Updated button label
+        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_pdf_button.click(
-            generate_discharge_paper_one_click,
+        one_click_ai_pdf_button.click(
+            generate_discharge_paper_one_click, # Use the same one-click function, which now calls AI
             inputs=[],
-            outputs=[one_click_pdf_download, one_click_pdf_status]
+            outputs=[one_click_ai_pdf_download, one_click_ai_pdf_status]
         )
 
 
@@ -556,9 +865,9 @@ with gr.Blocks() as demo:
         outputs=dashboard_output
     )
 
-    # Add functionality for PDF generation from MeldRx data
+    # Connect meldrx_pdf_button (No AI PDF)
     meldrx_pdf_button.click(
-        fn=generate_pdf_from_meldrx,
+        fn=generate_pdf_from_meldrx_no_ai, # Assuming you have a no-ai version of generate_pdf_from_meldrx or adjust as needed
         inputs=patient_data_output,
         outputs=[meldrx_pdf_download, meldrx_pdf_status]
     )