Update app.py

app.py

@@ -3,12 +3,13 @@ import sys
 import json
 import logging
 import re
+import hashlib
 from datetime import datetime
 from typing import List, Dict, Optional, Tuple
 from enum import Enum
 
 from fastapi import FastAPI, HTTPException
-from fastapi.responses import StreamingResponse
+from，快api.responses import StreamingResponse
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 import asyncio
@@ -22,7 +23,7 @@ logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(
 logger = logging.getLogger("TxAgentAPI")
 
 # App
-app = FastAPI(title="TxAgent API", version="2.2.0")  # Version bump for new features
+app = FastAPI(title="TxAgent API", version="2.2.1")  # Version bump for hash-based analysis
 
 app.add_middleware(
     CORSMiddleware,
@@ -169,13 +170,26 @@ def serialize_patient(patient: dict) -> dict:
         patient_copy["_id"] = str(patient_copy["_id"])
     return patient_copy
 
+def compute_patient_data_hash(patient: dict) -> str:
+    """Compute SHA-256 hash of patient data."""
+    serialized = json.dumps(patient, sort_keys=True)  # Sort keys for consistent hashing
+    return hashlib.sha256(serialized.encode()).hexdigest()
+
 async def analyze_patient(patient: dict):
     try:
         serialized = serialize_patient(patient)
-        doc = json.dumps(serialized, indent=2)
-        logger.info(f"🧾 Analyzing patient: {serialized.get('fhir_id')}")
+        patient_id = serialized.get("fhir_id")
+        patient_hash = compute_patient_data_hash(serialized)
+        logger.info(f"🧾 Analyzing patient: {patient_id}")
+
+        # Check if analysis exists and hash matches
+        existing_analysis = await analysis_collection.find_one({"patient_id": patient_id})
+        if existing_analysis and existing_analysis.get("data_hash") == patient_hash:
+            logger.info(f"✅ No changes in patient data for {patient_id}, skipping analysis")
+            return  # Skip analysis if data hasn't changed
 
         # Main clinical analysis
+        doc = json.dumps(serialized, indent=2)
         message = (
             "You are a clinical decision support AI.\n\n"
             "Given the patient document below:\n"
@@ -197,26 +211,27 @@ async def analyze_patient(patient: dict):
             "factors": risk_factors
         }
         
-        # Store analysis
+        # Store analysis with data hash
         analysis_doc = {
-            "patient_id": serialized.get("fhir_id"),
+            "patient_id": patient_id,
             "timestamp": datetime.utcnow(),
             "summary": structured,
             "suicide_risk": suicide_risk,
-            "raw": raw
+            "raw": raw,
+            "data_hash": patient_hash  # Store the hash
         }
         
         await analysis_collection.update_one(
-            {"patient_id": serialized.get("fhir_id")},
+            {"patient_id": patient_id},
             {"$set": analysis_doc},
             upsert=True
         )
         
         # Create alert if risk is above threshold
         if risk_level in [RiskLevel.MODERATE, RiskLevel.HIGH, RiskLevel.SEVERE]:
-            await create_alert(serialized.get("fhir_id"), suicide_risk)
+            await create_alert(patient_id, suicide_risk)
             
-        logger.info(f"✅ Stored analysis for patient {serialized.get('fhir_id')}")
+        logger.info(f"✅ Stored analysis for patient {patient_id}")
 
     except Exception as e:
         logger.error(f"Error analyzing patient: {e}")
@@ -250,7 +265,7 @@ async def startup_event():
     db = get_mongo_client()["cps_db"]
     patients_collection = db["patients"]
     analysis_collection = db["patient_analysis_results"]
-    alerts_collection = db["clinical_alerts"]  # New collection for alerts
+    alerts_collection = db["clinical_alerts"]
     logger.info("📡 Connected to MongoDB")
 
     asyncio.create_task(analyze_all_patients())
@@ -260,7 +275,7 @@ async def status():
     return {
         "status": "running",
         "timestamp": datetime.utcnow().isoformat(),
-        "version": "2.2.0"
+        "version": "2.2.1"
     }
 
 @app.get("/patients/analysis-results")
@@ -295,8 +310,6 @@ async def get_patient_analysis_results(name: Optional[str] = Query(None)):
         logger.error(f"Error fetching analysis results: {e}")
         raise HTTPException(status_code=500, detail="Failed to retrieve analysis results")
 
-
-
 @app.post("/chat-stream")
 async def chat_stream_endpoint(request: ChatRequest):
     async def token_stream():