Update app.py

app.py

@@ -4,13 +4,14 @@ from contextlib import asynccontextmanager
 # ─── 1. MANDATORY BACKEND CONFIG (Must be at the very top) ───────────────────
 os.environ["KERAS_BACKEND"] = "jax"
 os.environ["JAX_PLATFORM_NAME"] = "cpu"
-os.environ["HF_HOME"] = "/tmp/huggingface"  # HF Spaces writable directory
+os.environ["HF_HOME"] = "/tmp/huggingface"
 
 import base64
 import logging
 import numpy as np
 import cv2
 import requests
+import tempfile
 from typing import List, Dict, Any, Optional
 
 import keras
@@ -18,19 +19,22 @@ from fastapi import FastAPI, File, UploadFile, HTTPException
 from fastapi.responses import JSONResponse
 from huggingface_hub import hf_hub_download
 from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
+from inference_sdk import InferenceHTTPClient
 
 # ─── 2. LOGGING & CONFIG ─────────────────────────────────────────────────────
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
 
 API_KEY = os.getenv("ROBOFLOW_API_KEY")
-WORKFLOW_URL = "https://serverless.roboflow.com/my-workspace-iy9ll/find-teeth"
+WORKSPACE_NAME = "my-workspace-iy9ll"
+WORKFLOW_ID = "find-teeth"
 IMG_SIZE = 256
 THRESHOLD = 0.05
 MODEL_REPO = "sairamv1/TeethCariesDetection_CNNAutoencoder"
 MODEL_FILENAME = "autoencoder.keras"
 
 MODEL = None
+roboflow_client = None
 
 # ─── 3. RETRY-ENABLED MODEL DOWNLOAD ─────────────────────────────────────────
 @retry(
@@ -49,6 +53,22 @@ def download_model() -> str:
     logger.info(f"✅ Model downloaded to {local_path}")
     return local_path
 
+def init_roboflow_client():
+    """Initialize Roboflow client if API key is available."""
+    global roboflow_client
+    if API_KEY:
+        try:
+            roboflow_client = InferenceHTTPClient(
+                api_url="https://serverless.roboflow.com",
+                api_key=API_KEY
+            )
+            logger.info("✅ Roboflow client initialized")
+        except Exception as e:
+            logger.error(f"❌ Roboflow client initialization failed: {e}")
+            roboflow_client = None
+    else:
+        logger.warning("⚠️ ROBOFLOW_API_KEY not set")
+
 # ─── 4. LIFESPAN EVENT HANDLER ───────────────────────────────────────────────
 @asynccontextmanager
 async def lifespan(app: FastAPI):
@@ -59,13 +79,14 @@ async def lifespan(app: FastAPI):
         logger.info("🔄 Loading Keras model with JAX backend...")
         MODEL = keras.saving.load_model(local_model_path)
         logger.info(f"✅ Model loaded successfully! Input shape: {MODEL.input_shape}")
+        
+        # Initialize Roboflow client
+        init_roboflow_client()
     except Exception as e:
-        logger.error(f"❌ Model load failed: {e}")
+        logger.error(f"❌ Startup failed: {e}")
         MODEL = None
     
-    yield  # Application runs here
-    
-    # Cleanup (if needed)
+    yield
     logger.info("🛑 Shutting down...")
 
 app = FastAPI(
@@ -75,22 +96,19 @@ app = FastAPI(
     lifespan=lifespan
 )
 
-# ─── 5. ROBOFLOW API WITH RETRY ──────────────────────────────────────────────
-@retry(
-    stop=stop_after_attempt(2),
-    wait=wait_exponential(multiplier=1, min=1, max=5),
-    retry=retry_if_exception_type(requests.RequestException)
-)
-def call_roboflow_api(image_base64: str) -> Dict:
-    """Call Roboflow workflow with retry logic."""
-    response = requests.post(
-        WORKFLOW_URL,
-        params={"api_key": API_KEY},
-        json={"inputs": {"image": {"type": "base64", "value": image_base64}}},
-        timeout=30
+# ─── 5. ROBOFLOW API CALL ────────────────────────────────────────────────────
+def call_roboflow_api(image_path: str) -> Dict:
+    """Call Roboflow workflow using inference SDK."""
+    if not roboflow_client:
+        raise Exception("Roboflow client not initialized")
+    
+    result = roboflow_client.run_workflow(
+        workspace_name=WORKSPACE_NAME,
+        workflow_id=WORKFLOW_ID,
+        images={"image": image_path},
+        use_cache=False
     )
-    response.raise_for_status()
-    return response.json()
+    return result
 
 # ─── 6. ROUTES ───────────────────────────────────────────────────────────────
 @app.get("/")
@@ -100,7 +118,8 @@ async def root():
         "status": "active",
         "model_ready": MODEL is not None,
         "backend": os.environ.get("KERAS_BACKEND"),
-        "api_configured": API_KEY is not None
+        "api_configured": API_KEY is not None,
+        "roboflow_ready": roboflow_client is not None
     }
 
 @app.get("/health")
@@ -111,7 +130,11 @@ async def health():
             status_code=503,
             content={"status": "unhealthy", "reason": "Model not loaded"}
         )
-    return {"status": "healthy", "model_input_shape": str(MODEL.input_shape)}
+    return {
+        "status": "healthy",
+        "model_input_shape": str(MODEL.input_shape),
+        "roboflow_status": "ready" if roboflow_client else "not configured"
+    }
 
 @app.post("/analyze")
 async def analyze(file: UploadFile = File(...)):
@@ -123,7 +146,7 @@ async def analyze(file: UploadFile = File(...)):
     if MODEL is None:
         raise HTTPException(status_code=503, detail="Model not initialized. Please retry later.")
     
-    if not API_KEY:
+    if not roboflow_client:
         raise HTTPException(status_code=500, detail="ROBOFLOW_API_KEY not configured")
 
     # ─── Read and validate image ─────────────────────────────────────────────
@@ -149,30 +172,36 @@ async def analyze(file: UploadFile = File(...)):
         raise HTTPException(status_code=400, detail=f"Image processing failed: {str(e)}")
 
     # ─── Call Roboflow detection ─────────────────────────────────────────────
+    tmp_path = None
     try:
-        image_base64 = base64.b64encode(contents).decode("utf-8")
-        result_json = call_roboflow_api(image_base64)
-    except requests.Timeout:
-        raise HTTPException(status_code=504, detail="Detection service timeout")
-    except requests.RequestException as e:
-        logger.error(f"Roboflow API error: {e}")
-        raise HTTPException(status_code=502, detail="Detection service unavailable")
+        # Save to temp file for inference_sdk
+        with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
+            tmp.write(contents)
+            tmp_path = tmp.name
+        
+        result_json = call_roboflow_api(tmp_path)
+        
     except Exception as e:
-        logger.error(f"Unexpected error: {e}")
-        raise HTTPException(status_code=500, detail="Internal processing error")
+        logger.error(f"Roboflow workflow error: {e}")
+        raise HTTPException(status_code=502, detail=f"Detection service failed: {str(e)}")
+    finally:
+        # Clean up temp file
+        if tmp_path and os.path.exists(tmp_path):
+            os.remove(tmp_path)
 
     # ─── Parse detections ────────────────────────────────────────────────────
     try:
         if isinstance(result_json, list):
             output_data = result_json[0].get("outputs", {})
         else:
-            output_data = result_json.get("outputs", [{}])[0]
+            output_data = result_json.get("outputs", [{}])[0] if isinstance(result_json.get("outputs"), list) else result_json.get("outputs", {})
         
         predictions = output_data.get("predictions", [])
         if isinstance(predictions, dict):
             predictions = predictions.get("predictions", [])
     except Exception as e:
         logger.warning(f"Response parsing issue: {e}")
+        logger.warning(f"Raw response: {result_json}")
         predictions = []
 
     if not predictions:
@@ -187,7 +216,7 @@ async def analyze(file: UploadFile = File(...)):
     batch_images = []
     metadata = []
 
-    for i, det in enumerate(predictions[:12]):  # Limit for performance
+    for i, det in enumerate(predictions[:12]):
         try:
             xc, yc, w, h = det["x"], det["y"], det["width"], det["height"]
             x1 = max(0, int(xc - w/2))