Atualizar modelo de previsão

app.py

@@ -1,20 +1,39 @@
-# uvicorn app:app --reload
-
-from fastapi import Body, FastAPI, UploadFile, File
+import uvicorn
+from fastapi import Body, FastAPI, UploadFile, File, Response 
 from fastapi.responses import JSONResponse
 from fastapi.middleware.cors import CORSMiddleware
-from detect import DengueDetector
-from predict import DenguePredictor
 import traceback
+import numpy as np 
+import json 
+
+from detect import DengueDetector
+from predict import DenguePredictor  
 
-# Inicializar detector e preditor
-detector = DengueDetector()
-predictor = DenguePredictor()
+def default_json_serializer(obj):
+    if isinstance(obj, np.integer):
+        return int(obj)
+    elif isinstance(obj, np.floating):
+        return float(obj)
+    elif isinstance(obj, np.ndarray):
+        return obj.tolist()
+    raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable")
+
+detector: DengueDetector = None
+predictor: DenguePredictor = None
 
 app = FastAPI()
 
+# --- Crie um evento de startup para carregar os modelos ---
+@app.on_event("startup")
+async def startup_event():
+    global detector, predictor
+    print("Executando evento de startup: Carregando os módulos de IA...")
+    detector = DengueDetector()
+    predictor = DenguePredictor()
+    print("Módulos de IA carregados com sucesso. API pronta.")
+
 # --- CORS ---
-origins = ["https://previdengue.vercel.app", "*"]
+origins = ["https://previdengue.vercel.app", "http://localhost:3000", "*"]
 app.add_middleware(
     CORSMiddleware,
     allow_origins=origins,
@@ -28,9 +47,10 @@ app.add_middleware(
 def health_check():
     return {"status": "ok", "message": "API de Dengue rodando!"}
 
-# --- Rota de detecção ---
 @app.post("/detect/")
 async def detect(file: UploadFile = File(...)):
+    if detector is None:
+        return JSONResponse(status_code=503, content={"error": "Detector ainda não foi inicializado."})
     try:
         content = await file.read()
         result = detector.detect_image(content)  
@@ -38,17 +58,22 @@ async def detect(file: UploadFile = File(...)):
     except Exception as e:
         return JSONResponse(status_code=500, content={"error": str(e)})
 
-# --- Rota de predição com lazy loading de scalers ---
+
 @app.post("/predict/")
 async def predict_dengue_route(payload: dict = Body(...)):
+    if predictor is None:
+        return JSONResponse(status_code=503, content={"error": "Preditor ainda não foi inicializado."})
     try:
-        ibge_code = payload.get("ibge_code")
-        weeks_to_predict = payload.get("weeks_to_predict")
-        if ibge_code is None or weeks_to_predict is None:
-            raise ValueError("ibge_code e weeks_to_predict são obrigatórios.")
-
-        result = predictor.predict(ibge_code, weeks_to_predict)
-        return result
+        ibge_code_str = payload.get("ibge_code")
+        if ibge_code_str is None:
+            raise ValueError("O campo 'ibge_code' é obrigatório.")
+        
+        ibge_code = int(ibge_code_str)
+        result = predictor.predict(ibge_code)
+        
+        json_content = json.dumps(result, default=default_json_serializer)
+        
+        return Response(content=json_content, media_type="application/json")
 
     except Exception as e:
         tb_str = traceback.format_exc()

models/{scalers/dynamics/1100023_dynamic.pkl → checkpoints/model_checkpoint_best_city.keras}

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