Update callbackmanager.py

callbackmanager.py

@@ -23,49 +23,56 @@ from pypdf import PdfReader # For PDF
 import csv # For CSV
 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
-"""
+**Discharge Guard - Medical Data Analysis Assistant**
 
-# 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
-)
+**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.**
+"""
 
-# 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."""
+    """Analyzes DICOM file metadata using Discharge Guard 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."
+            report = f"Discharge Guard AI Analysis of DICOM Metadata:\n\nDICOM Metadata Analysis Report:\n{prediction_response}\n\nDisclaimer: The Discharge Guard -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."
@@ -77,7 +84,7 @@ def analyze_dicom_file_with_ai(dicom_file_path): # Modified to accept file path
         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."""
+    """Analyzes DICOM metadata JSON content using Discharge Guard 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):**
@@ -93,7 +100,7 @@ def analyze_dicom_content_ai(dicom_metadata_json): # Copied from your code
         "prompt": "DICOM Metadata Analysis Request",
         "language": "English",
         "response_length": "Comprehensive",
-        "model_name": "DocuNexus v1.0",
+        "model_name": "Discharge Guard v1.0",
         "generated_text": "N/A",
         "input_file_types": ["DICOM Metadata JSON"],
         "mode": "DICOM Metadata Analysis",
@@ -114,14 +121,14 @@ def analyze_dicom_content_ai(dicom_metadata_json): # Copied from your code
 
 # ... (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."""
+    """Analyzes HL7 file content using Discharge Guard 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."
+            report = f"Discharge Guard AI Analysis of HL7 Message:\n\nHL7 Message Analysis Report:\n{prediction_response}\n\n**Disclaimer:** The Discharge Guard 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."
@@ -133,7 +140,7 @@ def analyze_hl7_file_with_ai(hl7_file_path):
         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."""
+    """Analyzes HL7 message content using Discharge Guard 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:**
@@ -149,7 +156,7 @@ def analyze_hl7_content_ai(hl7_message_string): # Copied from your code
         "prompt": "HL7 Message Analysis Request",
         "language": "English",
         "response_length": "Comprehensive",
-        "model_name": "DocuNexus v1.0",
+        "model_name": "Discharge Guard v1.0",
         "generated_text": "N/A",
         "input_file_types": ["HL7 Message"],
         "mode": "HL7 Message Analysis",
@@ -170,7 +177,7 @@ def analyze_hl7_content_ai(hl7_message_string): # Copied from your code
 
 
 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."""
+    """Analyzes generic CDA or XML file content using Discharge Guard 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()
@@ -178,7 +185,7 @@ def analyze_cda_xml_file_with_ai(cda_xml_file_path): # Modified to accept file p
             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."
+            report = f"Discharge Guard AI Analysis of Medical XML/CDA Data:\n\nMedical Document Analysis Report:\n{prediction_response}\n\n**Disclaimer:** The Discharge Guard 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."
@@ -190,11 +197,11 @@ def analyze_cda_xml_file_with_ai(cda_xml_file_path): # Modified to accept file p
         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)."""
+    """Analyzes generic CDA or XML content using Discharge Guard AI (more generalized version)."""
 
     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.
 
-    **Instructions for DocuNexus AI:**
+    **Instructions for Discharge Guard AI:**
 
     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:
@@ -208,7 +215,7 @@ def analyze_cda_xml_content_ai(cda_xml_content): # Copied from your code
         "prompt": "Generic CDA/XML Analysis Request",
         "language": "English",
         "response_length": "Comprehensive",
-        "model_name": "DocuNexus v1.0",
+        "model_name": "Discharge Guard v1.0",
         "generated_text": "N/A",
         "input_file_types": ["CDA/XML"],
         "mode": "Generic XML/CDA Analysis",
@@ -229,7 +236,7 @@ def analyze_cda_xml_content_ai(cda_xml_content): # Copied from your code
 
 
 def analyze_pdf_file_with_ai(pdf_file_path): # Modified to accept file path
-    """Analyzes PDF file content using DocuNexus AI."""
+    """Analyzes PDF file content using Discharge Guard 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
@@ -241,7 +248,7 @@ def analyze_pdf_file_with_ai(pdf_file_path): # Modified to accept file path
         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."
+            report = f"Discharge Guard AI Analysis of PDF Content:\n\nMedical Report Analysis Report:\n{prediction_response}\n\n**Disclaimer:** The Discharge Guard 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."
@@ -253,7 +260,7 @@ def analyze_pdf_file_with_ai(pdf_file_path): # Modified to accept file path
         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."""
+    """Analyzes PDF text content using Discharge Guard 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:**
@@ -269,7 +276,7 @@ def analyze_pdf_content_ai(pdf_text_content): # Copied from your code
         "prompt": "PDF Text Analysis Request",
         "language": "English",
         "response_length": "Comprehensive",
-        "model_name": "DocuNexus v1.0",
+        "model_name": "Discharge Guard v1.0",
         "generated_text": "N/A",
         "input_file_types": ["PDF Text"],
         "mode": "PDF Text Analysis",
@@ -290,14 +297,14 @@ def analyze_pdf_content_ai(pdf_text_content): # Copied from your code
 
 
 def analyze_csv_file_with_ai(csv_file_path): # Modified to accept file path
-    """Analyzes CSV file content using DocuNexus AI."""
+    """Analyzes CSV file content using Discharge Guard 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."
+            report = f"Discharge Guard AI Analysis of CSV Data:\n\nData Analysis Report:\n{prediction_response}\n\n**Disclaimer:** The Discharge Guard 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."
@@ -309,7 +316,7 @@ def analyze_csv_file_with_ai(csv_file_path): # Modified to accept file path
         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."""
+    """Analyzes CSV content (string) using Discharge Guard 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:**
@@ -325,7 +332,8 @@ def analyze_csv_content_ai(csv_content_string): # Copied from your code
         "prompt": "CSV Data Analysis Request",
         "language": "English",
         "response_length": "Comprehensive",
-        "model_name": "DocuNexus v1.0",
+        "model_name": "Discharge Guard
+ v1.0",
         "generated_text": "N/A",
         "input_file_types": ["CSV Data"],
         "mode": "CSV Data Analysis",
@@ -793,7 +801,7 @@ with gr.Blocks() as demo:
         )
 
     with gr.Tab("Medical File Analysis"):
-        gr.Markdown("## Analyze Medical Files with DocuNexus AI")
+        gr.Markdown("## Analyze Medical Files with Discharge Guard AI")
         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)