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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) |