app.py

import gradio as gr
import os
import json
import pandas as pd
from data_service import DataAssessmentService
from datetime import datetime
import gspread
from oauth2client.service_account import ServiceAccountCredentials
from sheets_integration import SheetsLogger

# Initialize services
service = DataAssessmentService(api_key=os.environ.get("OPENAI_API_KEY"))
sheets_logger = SheetsLogger()

EXAMPLE_REQUESTS = """
### Example 1: Clinical Data Request
I need OPD patient statistics for the Cardiology department from January to June 2024, including daily patient volume, types of cardiac conditions (ICD-10 codes), average waiting times, and number of follow-up vs. new cases. This data will be used for department capacity planning and resource allocation.

### Example 2: Quality Improvement Request
Requesting waiting time analysis for all OPD clinics for Q1 2024, including:
- Registration to first nurse contact time
- Nurse station to doctor examination time
- Doctor examination duration
- Time at pharmacy
- Total visit duration
Break down by day of week and time slots (morning/afternoon). This data will help identify service bottlenecks.

### Example 3: Department Performance Analysis
Need Emergency Department performance data for March 2024:
- Daily patient volume by triage level
- Door-to-doctor times
- Length of stay in ED
- Admission rates
- Transfer rates to other departments
Purpose: Monthly performance review and staff allocation planning.
"""

# Department mapping from abbreviations to full names
DEPARTMENTS = {
    "Executive Management": "บริหาร ผอ รอง ผู้ช่วย",
    "Education Support": "สนับสนุนการศึกษา",
    "Medicine": "MED",
    "Cardiology": "Cardio",
    "Gastroenterology": "GI",
    "Medical Oncology": "Onco MED",
    "Hematology": "Hematology",
    "Operating Room": "OR",
    "Surgery": "Sx",
    "Orthopedics": "Ortho",
    "Obstetrics and Gynecology": "OBgyne",
    "Ophthalmology": "Oph",
    "Ear, Nose, and Throat": "ENT",
    "Anesthesiology": "Anes",
    "Emergency Medicine & EMS": "ER and EMS",
    "Pediatrics": "PED",
    "Family Medicine & Preventive Medicine": "GP Fammed preventive med",
    "Psychiatry": "Psych",
    "Physical Medicine & Rehabilitation": "PM&R Physiotherapy",
    "Pathology": "Patho",
    "Radiology": "Xray",
    "Radiation Oncology": "Onco radiology",
    "Cyclotron": "Cyclotron",
    "Inpatient Department": "IPD",
    "Outpatient Department": "OPD",
    "Pharmacy": "Pharmacy",
    "Dentistry": "dentistry",
    "Nutrition": "Nutrition",
    "Medical Records": "เวชระเบียน",
    "Finance": "การเงิน",
    "Other": "Other"
}

# Frequency options
FREQUENCIES = ["One-time request", "Weekly", "Monthly"]

# Urgency options
URGENCY = ["Within a week", "Within a month", "Within a year"]

def save_to_sheet(data):
    """Save request data to Google Sheet"""
    # TODO: Implement Google Sheets integration
    pass

def process_request(name, employee_id, email, department, other_dept, request_details, frequency, urgency):
    # Combine department info
    final_department = other_dept if department == "Other" else department
    
    # Process the request through GPT
    result = service.assess_request(request_details)
    
    # Prepare data for logging
    request_data = {
        "name": name,
        "employee_id": employee_id,
        "email": email,
        "department": final_department,
        "request_details": request_details,
        "frequency": frequency,
        "urgency": urgency,
        "system_response": json.dumps(result, ensure_ascii=False)
    }
    
    # Log to Google Sheets
    sheets_logger.log_request(request_data)
    
    return result

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Hospital Data Request System")
    gr.Markdown("Please fill in the following information to request data access.")
    
    with gr.Row():
        with gr.Column():
            name = gr.Textbox(label="Full Name", placeholder="Enter your full name")
            employee_id = gr.Textbox(label="Employee ID", placeholder="Enter your employee ID")
            email = gr.Textbox(label="Email", placeholder="Enter your email for contact")
    
    with gr.Row():
        with gr.Column():
            department = gr.Dropdown(
                choices=list(DEPARTMENTS.keys()) + ["Other"],
                label="Department",
                info="Select your department"
            )
            other_dept = gr.Textbox(
                label="Other Department",
                placeholder="Specify your department if not in the list",
                visible=False
            )
            
            department.change(
                fn=lambda x: gr.update(visible=x=="Other"),
                inputs=[department],
                outputs=[other_dept]
            )
    
    # Add collapsible examples section
    with gr.Accordion("📝 Click here to see example requests", open=False):
        gr.Markdown(EXAMPLE_REQUESTS)
    
    with gr.Row():
        request_details = gr.Textbox(
            label="Request Details",
            placeholder="Please describe in detail what data you need, including time period, specific parameters, etc.",
            lines=5
        )
    
    with gr.Row():
        with gr.Column():
            frequency = gr.Dropdown(
                choices=FREQUENCIES,
                label="Request Frequency",
                info="How often do you need this data?"
            )
            urgency = gr.Dropdown(
                choices=URGENCY,
                label="Urgency Level",
                info="How urgent is your request?"
            )
    
    submit_btn = gr.Button("Submit Request")
    output = gr.JSON(label="Analysis Result")
    
    submit_btn.click(
        fn=process_request,
        inputs=[name, employee_id, email, department, other_dept, 
                request_details, frequency, urgency],
        outputs=[output]
    )
    
    gr.Markdown("""
    ### Notes:
    - Please provide detailed information about the data you need
    - Include specific time periods and parameters
    - Clearly state the purpose of your request
    - All communications will be sent to the provided email
    """)

if __name__ == "__main__":
    demo.launch()