app.py

import os
import gradio as gr
from anthropic import Anthropic
import requests
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Initialize Anthropic client
client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))

MODAL_CLINIC_ENDPOINT = "https://aayushraj0324--healthmate-clinic-lookup-search-clinics.modal.run"

def classify_urgency(symptoms: str) -> str:
    """Classify the urgency level of the symptoms using Claude."""
    prompt = f"""You are a medical triage assistant. Given this symptom description: {symptoms}, \nclassify it as: emergency / routine visit / home care. Explain briefly."""
    message = client.messages.create(
        model="claude-sonnet-4-20250514",
        #model="claude-3-sonnet-20240229",
        max_tokens=150,
        temperature=0.1,
        system="You are a medical triage assistant. Provide clear, concise classifications.",
        messages=[{"role": "user", "content": prompt}]
    )
    return message.content[0].text

def get_possible_conditions(symptoms: str) -> str:
    """Get possible medical conditions based on symptoms using Claude."""
    prompt = f"""List 2–4 possible medical conditions that match these symptoms: {symptoms}. \nKeep it non-technical and easy to understand."""
    message = client.messages.create(
        model="claude-sonnet-4-20250514",
        #model="claude-3-sonnet-20240229",
        max_tokens=200,
        temperature=0.1,
        system="You are a medical assistant. Provide clear, non-technical explanations of possible conditions.",
        messages=[{"role": "user", "content": prompt}]
    )
    return message.content[0].text

def lookup_clinics(city: str) -> str:
    try:
        response = requests.get(MODAL_CLINIC_ENDPOINT, params={"city": city}, timeout=20)
        response.raise_for_status()
        clinics = response.json()
        if clinics and isinstance(clinics, list) and "error" not in clinics[0]:
            return "\n\n".join([
                f"🏥 {clinic['name']}\n🔗 {clinic['link']}\n📝 {clinic['description']}"
                for clinic in clinics
            ])
        else:
            return clinics[0].get("error", "No clinics found.")
    except Exception as e:
        return f"Error finding clinics: {str(e)}"

def process_input(symptoms: str, city: str) -> tuple:
    """Process the input and return all results."""
    # Get urgency classification
    urgency = classify_urgency(symptoms)
    
    # Get possible conditions
    conditions = get_possible_conditions(symptoms)
    
    # Get nearby clinics if city is provided
    if city:
        clinic_text = lookup_clinics(city)
    else:
        clinic_text = "Please provide a city to find nearby clinics."
    
    return urgency, conditions, clinic_text

# Create the Gradio interface
with gr.Blocks(css=".gradio-container {max-width: 800px; margin: auto;}") as demo:
    gr.Markdown(
        """
        # 🏥 AI Emergency Surgery Assistant
        Enter your symptoms and optionally your city to get medical guidance and nearby clinic recommendations.
        """
    )
    
    with gr.Row():
        with gr.Column():
            symptoms = gr.Textbox(
                label="Describe your symptoms",
                placeholder="Example: I have a severe abdominal pain and vomitus for the past 2 hours...",
                lines=4
            )
            city = gr.Textbox(
                label="Your city (optional)",
                placeholder="Example: Gomel"
            )
            submit_btn = gr.Button("Get Medical Guidance", variant="primary")
    
    with gr.Row():
        with gr.Column():
            urgency = gr.Textbox(label="Urgency Classification")
            conditions = gr.Textbox(label="Possible Conditions")
            clinics = gr.Textbox(label="Nearby Clinics")
    
    submit_btn.click(
        fn=process_input,
        inputs=[symptoms, city],
        outputs=[urgency, conditions, clinics]
    )

if __name__ == "__main__":
    demo.launch(share=True, pwa=True)