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#!/usr/bin/env python3
"""
Modal Functions for FhirFlame - L4 GPU Only + MCP Integration
Aligned with Modal documentation and integrated with FhirFlame MCP Server
"""
import modal
import json
import time
import os
import sys
from typing import Dict, Any, Optional
# Add src to path for monitoring
sys.path.append('/app/src')
try:
from monitoring import monitor
except ImportError:
# Fallback for Modal environment
class DummyMonitor:
def log_modal_function_call(self, *args, **kwargs): pass
def log_modal_scaling_event(self, *args, **kwargs): pass
def log_error_event(self, *args, **kwargs): pass
def log_medical_entity_extraction(self, *args, **kwargs): pass
def log_medical_processing(self, *args, **kwargs): pass
monitor = DummyMonitor()
def calculate_real_modal_cost(processing_time: float, gpu_type: str = "L4") -> float:
"""Calculate real Modal cost for L4 GPU processing"""
# L4 GPU pricing from environment
l4_hourly_rate = float(os.getenv("MODAL_L4_HOURLY_RATE", "0.73"))
platform_fee = float(os.getenv("MODAL_PLATFORM_FEE", "15")) / 100
hours_used = processing_time / 3600
total_cost = l4_hourly_rate * hours_used * (1 + platform_fee)
return round(total_cost, 6)
# Create Modal App following official documentation
app = modal.App("fhirflame-medical-ai-fresh")
# Define optimized image for medical AI processing with optional cache busting
cache_bust_commands = []
if os.getenv("MODAL_NO_CACHE", "false").lower() == "true":
# Add cache busting command with timestamp
import time
cache_bust_commands.append(f"echo 'Cache bust: {int(time.time())}'")
image = (
modal.Image.debian_slim(python_version="3.11")
.run_commands([
"pip install --upgrade pip",
"echo 'Fresh build with fixed Langfuse tracking'",
] + cache_bust_commands)
.pip_install([
"transformers==4.35.0",
"torch==2.1.0",
"fhir-resources==7.1.0", # Compatible with pydantic 2.x
"pydantic>=2.7.2",
"httpx>=0.25.0",
"regex>=2023.10.3"
])
.run_commands([
"pip cache purge || echo 'Cache purge not available, continuing...'"
])
)
# L4 GPU Function - Main processor for MCP Server integration
@app.function(
image=image,
gpu="L4", # RTX 4090 equivalent - only GPU we use
timeout=300,
scaledown_window=60, # Updated parameter name for Modal 1.0
min_containers=0,
max_containers=15,
memory=8192,
cpu=4.0,
secrets=[modal.Secret.from_name("fhirflame-env")]
)
def process_medical_document(
document_content: str,
document_type: str = "clinical_note",
processing_mode: str = "comprehensive",
include_validation: bool = True
) -> Dict[str, Any]:
"""
Process medical documents using L4 GPU
Returns structured medical data with cost tracking
"""
start_time = time.time()
try:
monitor.log_modal_function_call(
function_name="process_medical_document",
gpu_type="L4",
document_type=document_type,
processing_mode=processing_mode
)
# Initialize transformers pipeline
from transformers import pipeline
import torch
# Check GPU availability
device = 0 if torch.cuda.is_available() else -1
monitor.log_modal_scaling_event("GPU_DETECTED", {"cuda_available": torch.cuda.is_available()})
# Medical NER pipeline
ner_pipeline = pipeline(
"ner",
model="d4data/biomedical-ner-all",
aggregation_strategy="simple",
device=device
)
# Extract medical entities
entities = ner_pipeline(document_content)
# Process entities into structured format
processed_entities = {}
for entity in entities:
entity_type = entity['entity_group']
if entity_type not in processed_entities:
processed_entities[entity_type] = []
processed_entities[entity_type].append({
'text': entity['word'],
'confidence': float(entity['score']),
'start': int(entity['start']),
'end': int(entity['end'])
})
# Calculate processing metrics
processing_time = time.time() - start_time
cost = calculate_real_modal_cost(processing_time, "L4")
monitor.log_medical_entity_extraction(
entities_found=len(entities),
processing_time=processing_time,
cost=cost
)
# Basic medical document structure (without FHIR for now)
result = {
"document_type": document_type,
"processing_mode": processing_mode,
"entities": processed_entities,
"processing_metadata": {
"processing_time_seconds": processing_time,
"estimated_cost_usd": cost,
"gpu_type": "L4",
"entities_extracted": len(entities),
"timestamp": time.time()
},
"medical_insights": {
"entity_types_found": list(processed_entities.keys()),
"total_entities": len(entities),
"confidence_avg": sum(e['score'] for e in entities) / len(entities) if entities else 0
}
}
monitor.log_medical_processing(
success=True,
processing_time=processing_time,
cost=cost,
entities_count=len(entities)
)
return result
except Exception as e:
processing_time = time.time() - start_time
cost = calculate_real_modal_cost(processing_time, "L4")
monitor.log_error_event(
error_type=type(e).__name__,
error_message=str(e),
processing_time=processing_time,
cost=cost
)
return {
"error": True,
"error_type": type(e).__name__,
"error_message": str(e),
"processing_metadata": {
"processing_time_seconds": processing_time,
"estimated_cost_usd": cost,
"gpu_type": "L4",
"timestamp": time.time()
}
}
# MCP Integration Endpoint
@app.function(
image=image,
gpu="L4",
timeout=300,
scaledown_window=60,
min_containers=0,
max_containers=10,
memory=8192,
cpu=4.0,
secrets=[modal.Secret.from_name("fhirflame-env")]
)
def mcp_medical_processing_endpoint(
request_data: Dict[str, Any]
) -> Dict[str, Any]:
"""
MCP-compatible endpoint for medical document processing
Used by FhirFlame MCP Server
"""
start_time = time.time()
try:
# Extract request parameters
document_content = request_data.get("document_content", "")
document_type = request_data.get("document_type", "clinical_note")
processing_mode = request_data.get("processing_mode", "comprehensive")
if not document_content:
return {
"success": False,
"error": "No document content provided",
"mcp_response": {
"status": "error",
"message": "Document content is required"
}
}
# Process document
result = process_medical_document.local(
document_content=document_content,
document_type=document_type,
processing_mode=processing_mode
)
# Format for MCP response
mcp_response = {
"success": not result.get("error", False),
"data": result,
"mcp_metadata": {
"endpoint": "mcp-medical-processing",
"version": "1.0",
"timestamp": time.time()
}
}
return mcp_response
except Exception as e:
processing_time = time.time() - start_time
cost = calculate_real_modal_cost(processing_time, "L4")
return {
"success": False,
"error": str(e),
"mcp_response": {
"status": "error",
"message": f"Processing failed: {str(e)}",
"cost": cost,
"processing_time": processing_time
}
}
# Health check endpoint
@app.function(
image=image,
timeout=30,
scaledown_window=30,
min_containers=1, # Keep one warm for health checks
max_containers=3,
memory=1024,
cpu=1.0
)
def health_check() -> Dict[str, Any]:
"""Health check endpoint for Modal functions"""
return {
"status": "healthy",
"timestamp": time.time(),
"app": "fhirflame-medical-ai-fresh",
"functions": ["process_medical_document", "mcp_medical_processing_endpoint"],
"gpu_support": "L4"
}
if __name__ == "__main__":
print("FhirFlame Modal Functions - L4 GPU Medical Processing")
print("Available functions:")
print("- process_medical_document: Main medical document processor")
print("- mcp_medical_processing_endpoint: MCP-compatible endpoint")
print("- health_check: System health monitoring") |