app.py

import gradio as gr
import os
import sys
from openai import OpenAI
import os

api_key = os.getenv("key")
client = OpenAI(api_key=api_key)

first = 1

focused_section = "SECTION"

first_prompt = """
    You are a helpful assistant giving real-time feedback to a Community Health Worker.
    I want you to act as an attending physician to give immediate, specific and
    actionable feedback to a community health worker who is right now with
    a patient. They are in the process of creating an initial SOAP note of their current patient
    interaction, but it is important to remember the note is incomplete. I’ll first give some information about the clinic and then about
    the community health-worker. Remember that the SOAP note may contain both data 
    collected today in the clinic as well as data reported from the past and both 
    types of data might be relevant.

    CLINIC CONTEXT: The clinic serves patients in Kano, Nigeria. The
    clinic focuses on acute, immediate care and cannot expect to see the
    same patient for consistent follow-up care. Sometimes the clinic requires
    that healthcare workers create conditional treatments plans before test
    results are returned. The clinic has limited testing capabilities consisting
    of only screening tests ((Malaria RDT, Typhoid/Paratyphoid RDT, PCV,
    Pregnancy test, Urine Dipstick Test, H. Pylori, HIV confirmatory test
    (ELFA)), and a mission to avoid low value care. There are no imaging
    or ultrasound or other testing capabilities. There are locally prevalent
    illnesses such as malaria and other infectious diseases. Top causes of
    death in Nigeria include: Neonatal complications, Lower respiratory
    infections, malaria, diarrhea, TB, heart disease, stroke, maternal condi-
    tions, meningitis and HIV.

    COMMUNITY HEALTH WORKER: The community healthcare worker
    seeing the patient has three years of training and is experienced with
    maternal and child health conditions such as malaria, diarrhea, pneu-
    monia, etc, but they are generally limited in their diagnositc abilities

    DESIRED FEEDBACK: The caregiver is currently
    sitting with this patient. Please give concrete advice on additional actions
    to take and point out inconsistencies or errors in the SOAP note. The caregiver will read the response while
    with the patient and can act on the advice immediately, but cannot spend
    a lot of time reading. Your feedback should be concise and focused,
    and if there are no issues with the SOAP note, a very short encouraging
    response is sufficient.

    DISEASE-SPECIFIC GUIDANCE: If the patient presents with a fever and there is evidence of an alternative explanation, such as upper respiratory infection or UTI, then this fever alone does not warrant a malaria test.
    Malaria treatment only when malaria RDT is positive; treat with oral ACT for 3 days; if unable to take oral, use injectable ACT.
    Typhoid test only if patient has fever for 3 days or more with abdominal complaints such as pain, diarrhea or constipation.
    Typhoid treatment with oral Ciprofloxacin or Azithromycin for 5 days.
    H pylori test if the patient has symptoms of gastritis for more than three weeks and recurrence after treatment with antacids or PPI.
    H pylori positive test must be treated with triple therapy.
    Anemia test if patient has chronic symptoms of fatigue or shortness of breath on exertion or lack of energy or dizziness.
    Anemia treatment with oral iron for three months if anemia test is positive.
    Hypertension considered a low-priority issue by this clinic, and should only be commented on in the event of other signs of a serious cardiac condition.

    That concludes the context for this clinic.

    TASK: Give ONLY TWO SENTENCES of specific, actionable feedback to the CHEW based on what they have so far. Your feedback should only pertain to the SECTION section of the note.
    If a section is completely blank, don't comment on it; the CHEW likely just hasn't gotten to writing it yet.
    You should feel willing to make suggestions for other things to examine or additional questions, since the CHEW is with the patient right now.

    PATIENT RECORD:
    """

second_prompt = """
    With your feedback so far in mind, the CHEW has changed their note to the note below. What other comments do you have for them?
    If they have addressed all of your previous feedback, there is no need to ask further questions.

    PATIENT RECORD:
    """

example_subjective = """SUBJECTIVE
AGE
2y
Gender
female
Chief Complaint
Running Nose And Cough
History of presenting Illness (HPI)
Patient seen with the complain of Running nose and cough 3/7, the catarrh are associated with fever and body malaise from the patient, there is mucus when coughing more common on the night 
Medical History
No any medical history 
Gynaecology Summary
Nil
"""

example_objective = """OBJECTIVE
Review of Systems
Running of nose, vomiting, cough, fever and slight changes in her breathing patterns mostly on the night period 
Physical Examination
Patient is mildly I'll looking not dehydrated and the conjunctiva is not pale, no lost of weight, no abdominal tenderness and distention no any physical discomfort 
Vital Signs
Blood pressure(0/0 mm[Hg])
Temperature(37°C)
Anthropometry
Height(78 cm) Weight(11 kg) BMI(18.1 kg/m2)
"""

example_assessment = """ASSESSMENT
Diagnosis
Cough, Plasmodium falciparum malaria, unspecified
Differential Diagnosis
Cough, Plasmodium falciparum malaria, unspecified
"""

example_plan = """PLAN
Treatment plan
To do mRDT
* Syr Loratidine 5mls bd 5/7
* Syr Paracetamol 5mls 5mls tds 3/7
* Syr Emzolyn 5ml tds 3/7
If malaria test reveals positive then add
* Syr Arthemter 5mls bd 3/7
Health educate the patient on personal hygiene and environmental hygiene 
"""

old_soap_note = ""
response = ""

def predict(messages):
    response = client.chat.completions.create(
        model="gpt-4o-2024-08-06",  # GPT-4 Omni
        messages=messages,
    )
    out = response.choices[0].message.content
    return out

# Define your custom function to process the input text
def process_text(text1, text2, text3, text4, this_section):
    global first, old_soap_note, response, focused_section  # Declare them as global to access the variables outside the function
    curr_soap_note = f"SUBJECTIVE:\n{text1}\nOBJECTIVE:\n{text2}\nASSESSMENT:\n{text3}\nPLAN:\n{text4}"
    prompt = None    
    first_prompt.replace(focused_section, this_section)
    focused_section = this_section
    if first == 1:
        prompt = [
            {"role": "user", "content": first_prompt},
            {"role": "user", "content": curr_soap_note}
        ]
    old_soap_note = curr_soap_note  # Store the current SOAP note
    
    response = predict(prompt)
    return response


# Create the Gradio app
with gr.Blocks() as demo:
    gr.Markdown("# Enter Your SOAP Note Sections Below and Hit Enter to Get Your Feedback")
    with gr.Row():
        with gr.Column():
            text_box1 = gr.Textbox(label="Subjective")
            text_box2 = gr.Textbox(label="Objective")
            text_box3 = gr.Textbox(label="Assessment")
            text_box4 = gr.Textbox(label="Plan")
        with gr.Column():
            output_box1 = gr.Textbox(label="Subjective Feedback", interactive=False)
            output_box2 = gr.Textbox(label="Objective Feedback", interactive=False)
            output_box3 = gr.Textbox(label="Assessment Feedback", interactive=False)
            output_box4 = gr.Textbox(label="Plan Feedback", interactive=False)
    with gr.Row():
        with gr.Column():
            example_note_s = gr.Textbox(label="Example Subjective", value = example_subjective)
            example_note_o = gr.Textbox(label="Example Objective", value = example_objective)
            example_note_a = gr.Textbox(label="Example Assessment", value = example_assessment)
            example_note_p = gr.Textbox(label="Example Plan", value = example_plan)
            
    # Submit events without "this_section" as a direct string in the inputs
    text_box1.submit(lambda t1, t2, t3, t4: process_text(t1, t2, t3, t4, "SUBJECTIVE"), 
                     [text_box1, text_box2, text_box3, text_box4], output_box1)
    text_box2.submit(lambda t1, t2, t3, t4: process_text(t1, t2, t3, t4, "OBJECTIVE"), 
                     [text_box1, text_box2, text_box3, text_box4], output_box2)
    text_box3.submit(lambda t1, t2, t3, t4: process_text(t1, t2, t3, t4, "ASSESSMENT"), 
                     [text_box1, text_box2, text_box3, text_box4], output_box3)
    text_box4.submit(lambda t1, t2, t3, t4: process_text(t1, t2, t3, t4, "PLAN"), 
                     [text_box1, text_box2, text_box3, text_box4], output_box4)

# Launch the app
demo.launch()