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README.md

@@ -1,14 +1,60 @@
----
-title: HealthMateAI Medical Triage Assistant
-emoji: 📚
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 5.33.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: AI triage tool for symptoms, urgency, and clinic search
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🏥 HealthMate: AI Medical Triage Assistant
+
+HealthMate is an AI-powered medical triage assistant that helps users assess their symptoms and find nearby medical facilities. It uses Claude AI for medical analysis and DuckDuckGo for clinic lookups.
+
+## Features
+
+- 🤖 AI-powered symptom analysis using Claude
+- 🏥 Urgency classification (emergency/routine/home care)
+- 📋 Possible medical conditions assessment
+- 🔍 Nearby clinic recommendations
+- 🌐 DuckDuckGo-powered clinic search
+
+## Setup
+
+1. Clone this repository:
+```bash
+git clone https://github.com/yourusername/health-mate.git
+cd health-mate
+```
+
+2. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+3. Create a `.env` file with your Anthropic API key:
+```
+ANTHROPIC_API_KEY=your_api_key_here
+```
+
+4. Deploy the Modal backend:
+```bash
+modal deploy modal_clinic_lookup.py
+```
+
+5. Run the Gradio app:
+```bash
+python app.py
+```
+
+## Usage
+
+1. Enter your symptoms in the text area
+2. (Optional) Enter your city for nearby clinic recommendations
+3. Click "Get Medical Guidance"
+4. Review the AI-generated assessment and clinic recommendations
+
+## Important Notes
+
+- This is not a replacement for professional medical advice
+- Always consult a healthcare provider for serious symptoms
+- The clinic recommendations are based on web search results
+
+## Demo
+
+[Add your demo video link here]
+
+## Tags
+
+agent-demo-track

app.py

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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(
+        """
+        # 🏥 HealthMate: AI Medical Triage 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 headache and fever for the past 2 days...",
+                lines=4
+            )
+            city = gr.Textbox(
+                label="Your city (optional)",
+                placeholder="Example: San Francisco"
+            )
+            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) 

modal_clinic_lookup.py

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+import modal
+from duckduckgo_search import DDGS
+from bs4 import BeautifulSoup
+import requests
+
+# Create a Modal app
+app = modal.App("healthmate-clinic-lookup")
+
+# Define the base image with required dependencies
+image = modal.Image.debian_slim().pip_install(
+    "duckduckgo_search",
+    "beautifulsoup4",
+    "requests",
+    "fastapi[standard]"
+)
+
+@app.function(image=image)
+@modal.fastapi_endpoint()
+def search_clinics(city: str) -> list:
+    """
+    Search for clinics near the specified city using DuckDuckGo.
+    Returns a list of dictionaries containing clinic information.
+    """
+    if not city:
+        return [{"error": "Please provide a city name"}]
+    
+    try:
+        # Initialize DuckDuckGo search
+        with DDGS() as ddgs:
+            # Search for clinics
+            search_query = f"top medical clinics near {city}"
+            results = list(ddgs.text(search_query, max_results=3))
+            
+            if not results:
+                return [{"error": f"No clinics found near {city}"}]
+            
+            # Process and format results
+            clinics = []
+            for result in results:
+                clinic_info = {
+                    "name": result.get("title", "Unknown Clinic"),
+                    "link": result.get("link", "#"),
+                    "description": result.get("body", "No description available")
+                }
+                clinics.append(clinic_info)
+            
+            return clinics
+            
+    except Exception as e:
+        return [{"error": f"Error searching for clinics: {str(e)}"}]
+
+@app.local_entrypoint()
+def main():
+    # Test the function locally
+    results = search_clinics.remote("San Francisco")
+    print(results) 

requirements.txt

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+modal>=0.57.0
+gradio>=4.19.2
+anthropic>=0.18.1
+duckduckgo_search>=4.4.3
+beautifulsoup4>=4.12.3
+requests>=2.31.0
+python-dotenv>=1.0.1