improve one click discharge paper

app.py

@@ -18,7 +18,7 @@ logger = logging.getLogger(__name__)
 
 # Import PDF utilities
 from utils.pdfutils import PDFGenerator, generate_discharge_summary
-
+from utils.oneclick import generate_discharge_paper_one_click
 # Import necessary libraries for new file types and AI analysis functions
 import pydicom  # For DICOM
 import hl7  # For HL7
@@ -394,25 +394,30 @@ with gr.Blocks(theme=cyberpunk_theme) as demo: # Apply the theme here
             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],
-        )
+with gr.Tab("One-Click Discharge Paper (AI)", elem_classes="cyberpunk-tab"):
+    gr.Markdown("<h2 style='color:#00FFFF; text-shadow: 0 0 3px #00FFFF;'>One-Click Medical Discharge Paper Generation with AI Content</h2>")
+    with gr.Row():
+        patient_id_input = gr.Textbox(label="Patient ID (Optional)", placeholder="Enter Patient ID")
+        first_name_input = gr.Textbox(label="First Name (Optional)", placeholder="Enter First Name")
+        last_name_input = gr.Textbox(label="Last Name (Optional)", placeholder="Enter Last Name")
+    one_click_ai_pdf_button = gr.Button(
+        "Generate Discharge Paper with AI (One-Click)", elem_classes="cyberpunk-button"
+    )
+    one_click_ai_pdf_status = gr.Textbox(label="Discharge Paper Generation Status (AI)")
+    one_click_ai_pdf_download = gr.File(label="Download Discharge Paper (AI)")
+
+    # Initialize MeldRxAPI (ensure client_id, client_secret, workspace_id are set in environment variables)
+    client_id = os.getenv("APPID")
+    client_secret = os.getenv("CLIENT_SECRET")  # Optional, set if required
+    workspace_id = os.getenv("WORKSPACE_URL")
+    redirect_uri = "https://multitransformer-discharge-guard.hf.space/callback"
+    meldrx_api = MeldRxAPI(client_id, client_secret, workspace_id, redirect_uri)
+
+    one_click_ai_pdf_button.click(
+        fn=lambda pid, fname, lname: generate_discharge_paper_one_click(meldrx_api, pid, fname, lname),
+        inputs=[patient_id_input, first_name_input, last_name_input],
+        outputs=[one_click_ai_pdf_download, one_click_ai_pdf_status],
+    )
 
     # Connect the patient data buttons
     patient_data_button.click(

utils/oneclick.py

@@ -0,0 +1,177 @@
+import os
+import json
+import logging
+from typing import Optional, Dict, Any
+from huggingface_hub import InferenceClient
+from utils.meldrx import MeldRxAPI
+from utils.pdfutils import PDFGenerator
+from datetime import datetime
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Initialize Hugging Face Inference Client
+HF_TOKEN = os.getenv("HF_TOKEN")
+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"  # Model to use for discharge summary generation
+
+def generate_ai_discharge_summary(patient_data: Dict[str, Any]) -> Optional[str]:
+    """
+    Generate a discharge summary using the Hugging Face Inference Client based on patient data.
+
+    Args:
+        patient_data (Dict[str, Any]): Patient data in FHIR JSON format.
+
+    Returns:
+        Optional[str]: Generated discharge summary text or None if generation fails.
+    """
+    try:
+        # Extract relevant patient information
+        name = patient_data.get("name", [{}])[0]
+        full_name = f"{name.get('given', ['Unknown'])[0]} {name.get('family', 'Unknown')}"
+        gender = patient_data.get("gender", "Unknown").capitalize()
+        birth_date = patient_data.get("birthDate", "Unknown")
+        age = calculate_age(birth_date) if birth_date != "Unknown" else "Unknown"
+
+        # Placeholder for additional clinical data (e.g., diagnosis, treatment)
+        # In a real scenario, this would come from related FHIR resources like Encounter, Condition, etc.
+        patient_info = (
+            f"Patient Name: {full_name}\n"
+            f"Gender: {gender}\n"
+            f"Age: {age}\n\n"
+            f"Presentation and Diagnosis:\n[Diagnosis data not provided in this snippet; assumed from related FHIR resources]\n\n"
+            f"Hospital Course:\n[Treatment data not provided in this snippet; assumed from related FHIR resources]\n\n"
+            f"Outcome:\n[Outcome data not provided in this snippet; assumed from related FHIR resources]"
+        )
+
+        # Define the prompt for the AI model
+        messages = [
+            {"role": "user", "content": ""},
+            {
+                "role": "assistant",
+                "content": (
+                    "You are a senior expert medical health practitioner known for producing discharge papers. "
+                    "You will receive patient information and treatment details. Produce a complete discharge summary "
+                    "based on the information provided."
+                )
+            },
+            {"role": "user", "content": patient_info}
+        ]
+
+        # Generate discharge summary using streaming
+        stream = client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=messages,
+            temperature=0.4,
+            max_tokens=3584,
+            top_p=0.7,
+            stream=True
+        )
+
+        discharge_summary = ""
+        for chunk in stream:
+            content = chunk.choices[0].delta.content
+            if content:
+                discharge_summary += content
+
+        return discharge_summary.strip()
+
+    except Exception as e:
+        logger.error(f"Error generating AI discharge summary: {str(e)}")
+        return None
+
+def calculate_age(birth_date: str) -> str:
+    """
+    Calculate age from birth date.
+
+    Args:
+        birth_date (str): Birth date in YYYY-MM-DD format.
+
+    Returns:
+        str: Calculated age or 'Unknown' if calculation fails.
+    """
+    try:
+        birth = datetime.strptime(birth_date, "%Y-%m-%d")
+        today = datetime.today()
+        age = today.year - birth.year - ((today.month, today.day) < (birth.month, birth.day))
+        return str(age)
+    except ValueError:
+        return "Unknown"
+
+def generate_discharge_paper_one_click(
+    meldrx_api: MeldRxAPI,
+    patient_id: str = None,
+    first_name: str = None,
+    last_name: str = None
+) -> tuple[Optional[str], str]:
+    """
+    Generate a discharge paper with AI content in one click.
+
+    Args:
+        meldrx_api (MeldRxAPI): Initialized MeldRxAPI instance.
+        patient_id (str, optional): Patient ID to fetch specific patient data.
+        first_name (str, optional): First name for patient lookup if patient_id is not provided.
+        last_name (str, optional): Last name for patient lookup if patient_id is not provided.
+
+    Returns:
+        tuple[Optional[str], str]: (PDF file path, Status message)
+    """
+    try:
+        # Authenticate if not already authenticated
+        if not meldrx_api.access_token and not meldrx_api.authenticate():
+            return None, "Error: Authentication failed. Please authenticate first."
+
+        # Fetch patient data
+        if patient_id:
+            # Fetch specific patient by ID (assuming FHIR Patient resource endpoint supports this)
+            patient_data = meldrx_api.get_patients()  # Simplified; assumes ID filtering in real API
+            if not patient_data or "entry" not in patient_data:
+                return None, "Error: Failed to fetch patient data by ID."
+            patients = [entry["resource"] for entry in patient_data.get("entry", [])]
+            patient = next((p for p in patients if p.get("id") == patient_id), None)
+            if not patient:
+                return None, f"Error: Patient with ID {patient_id} not found."
+        else:
+            # Fetch all patients and filter by name if provided
+            patient_data = meldrx_api.get_patients()
+            if not patient_data or "entry" not in patient_data:
+                return None, "Error: Failed to fetch patient data."
+            patients = [entry["resource"] for entry in patient_data.get("entry", [])]
+            if first_name and last_name:
+                patient = next(
+                    (p for p in patients if
+                     p.get("name", [{}])[0].get("given", [""])[0].lower() == first_name.lower() and
+                     p.get("name", [{}])[0].get("family", "").lower() == last_name.lower()),
+                    None
+                )
+                if not patient:
+                    return None, f"Error: Patient with name {first_name} {last_name} not found."
+            else:
+                # Default to first patient if no specific ID or name provided
+                patient = patients[0] if patients else None
+                if not patient:
+                    return None, "Error: No patients found in the workspace."
+
+        # Generate AI discharge summary
+        ai_content = generate_ai_discharge_summary(patient)
+        if not ai_content:
+            return None, "Error: Failed to generate AI discharge summary."
+
+        # Generate PDF
+        pdf_generator = PDFGenerator()
+        pdf_path = pdf_generator.generate_pdf_from_text(
+            ai_content,
+            f"discharge_summary_{patient.get('id', 'unknown')}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf"
+        )
+
+        if pdf_path:
+            return pdf_path, f"Success: Discharge paper generated for {patient.get('name', [{}])[0].get('given', ['Unknown'])[0]} {patient.get('name', [{}])[0].get('family', 'Unknown')}"
+        else:
+            return None, "Error: Failed to generate PDF."
+
+    except Exception as e:
+        logger.error(f"Error in one-click discharge generation: {str(e)}")
+        return None, f"Error: {str(e)}"