Update app.py

app.py

@@ -1,9 +1,9 @@
-from fastapi import FastAPI, UploadFile, File, HTTPException, Request, Depends
+from fastapi import FastAPI, UploadFile, File, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, StreamingResponse
 from fastapi.staticfiles import StaticFiles
-from fastapi_limiter import FastAPILimiter
-from fastapi_limiter.depends import RateLimiter
+from slowapi import Limiter
+from slowapi.util import get_remote_address
 import tensorflow as tf
 from tensorflow.keras.models import Model, load_model
 from tensorflow.keras.preprocessing.image import img_to_array
@@ -21,14 +21,18 @@ import os
 # Configuration
 MAX_FILE_SIZE = 10 * 1024 * 1024  # 10MB
 HEATMAP_EXPIRY = 300  # 5 minutes in seconds
-RATE_LIMIT = "5/minute"  # 5 requests per minute
 
+# Initialize FastAPI with rate limiting
 app = FastAPI(
     title="ChexNet Medical Imaging API",
     description="API for chest X-ray analysis with Grad-CAM visualization",
-    version="1.1.0"
+    version="2.0.0"
 )
 
+# Rate limiter setup
+limiter = Limiter(key_func=get_remote_address)
+app.state.limiter = limiter
+
 # Mount static files
 app.mount("/static", StaticFiles(directory="static"), name="static")
 
@@ -41,11 +45,6 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# Initialize rate limiter (in-memory)
-@app.on_event("startup")
-async def startup():
-    await FastAPILimiter.init()
-
 # Session storage for heatmaps
 heatmap_store: Dict[str, dict] = {}
 
@@ -134,8 +133,8 @@ async def health_check():
 async def get_class_names():
     return {"classes": class_names}
 
-@app.post("/analyze",
-          dependencies=[Depends(RateLimiter(times=RATE_LIMIT))])
+@app.post("/analyze")
+@limiter.limit("5/minute")
 async def analyze_image(request: Request, file: UploadFile = File(...)):
     """
     Analyze chest X-ray image and return predictions with Grad-CAM visualization
@@ -210,7 +209,7 @@ async def model_info():
         "input_size": "540x540",
         "classes": len(class_names),
         "gradcam_layer": layer_name,
-        "rate_limit": RATE_LIMIT
+        "rate_limit": "5 requests/minute"
     }
 
 # Error handlers