Atualizar código e adicionar modelos e dados grandes via LFS

.gitattributes

@@ -1,3 +1,4 @@
+pip install git-filter-repo
 *.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
@@ -33,3 +34,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/checkpoints/*.keras filter=lfs diff=lfs merge=lfs -text
+*.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,107 +1,72 @@
 #     uvicorn app:app --reload
 
-import uuid
-from fastapi import FastAPI, UploadFile, File
+from fastapi import Body, FastAPI, UploadFile, File, HTTPException
 from fastapi.responses import JSONResponse
 from fastapi.middleware.cors import CORSMiddleware
-from PIL import Image
-import os
-from collections import Counter
-from ultralytics import YOLO
+import numpy as np
 import cv2
+from detect import DengueDetector
+from predict import DenguePredictor
+from contextlib import asynccontextmanager
 
-app = FastAPI()
+import joblib
 
-origins = [
-    "https://previdengue.vercel.app",
-]
+app = FastAPI()
 
+# --- CORS ---
+origins = ["https://previdengue.vercel.app", "*"]
 app.add_middleware(
     CORSMiddleware,
     allow_origins=origins,
     allow_credentials=True,
     allow_methods=["*"],
-    allow_headers=["*"],
+    allow_headers=["*"]
 )
 
-detect_model_path = "./models/DetectsmallTest1.pt"
-detect_model = YOLO(detect_model_path)
-
-@app.get("/")
-def read_root():
-    return {"message": "Hello!"}
-
+detector = DengueDetector()
+predictor = DenguePredictor()
+SCALER_CACHE = {}
 
-def caculateIntensity(objetos):
-    count_piscina = sum(1 for o in objetos if o["class"] == "piscina")
-    count_caixa = sum(1 for o in objetos if o["class"] == "caixa_agua")
-    count_carro = sum(1 for o in objetos if o["class"] == "carro")
 
-    w_piscina = 9
-    w_caixa = 4
-    w_carro = 1
-
-    score = count_piscina * w_piscina + count_caixa * w_caixa + count_carro * w_carro
-    return score
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    SCALER_CACHE.clear()
+    SCALER_DIR = predictor.scaler_dir
+    for f in SCALER_DIR.glob("*_dynamic.pkl"):
+        ibge = int(f.stem.split("_")[0])
+        dynamic_scaler = joblib.load(f)
+        static_scaler = joblib.load(SCALER_DIR / f"{ibge}_static.pkl")
+        SCALER_CACHE[ibge] = {"dynamic": dynamic_scaler, "static": static_scaler}
+    print(f"Cache de scalers carregado para {len(SCALER_CACHE)} municípios.")
+    yield 
 
+# --- Rotas ---
+@app.get("/")
+def health_check():
+    return {"status": "ok", "message": "API de Dengue rodando!"}
 
+# --- Rota de detecção ---
 @app.post("/detect/")
-async def upload_image(file: UploadFile = File(...)):
-    print("-" * 50)
-    print("Detecting...")
-
-    image_uuid = str(uuid.uuid4())
-    temp_dir = "/tmp"
-    image_path = os.path.join(temp_dir, f"{image_uuid}.png")
-
-    contents = await file.read()
-    with open(image_path, "wb") as f:
-        f.write(contents)
-
+async def detect(file: UploadFile = File(...)):
     try:
-        with Image.open(image_path) as image:
-            width, height = image.size
-
-            results = detect_model(image_path)
-            result = results[0]
-            boxes = result.boxes
-            names = detect_model.names
-            
-            class_ids = boxes.cls.tolist()
-            confidences = boxes.conf.tolist()
-            class_names = [names[int(cls)] for cls in class_ids]
-            counts = Counter(class_names)
-
-            detections = []
-            for i in range(len(boxes)):
-                x1, y1, x2, y2 = map(float, boxes.xyxy[i])
-                conf = float(confidences[i])
-                cls_id = int(class_ids[i])
-                detections.append({
-                    "class": names[cls_id],
-                    "confidence": round(conf, 4),
-                    "box": {
-                        "x1": x1,
-                        "y1": y1,
-                        "x2": x2,
-                        "y2": y2,
-                        "original_width": width,
-                        "original_height": height
-                    }
-                })
-
-        os.remove(image_path)
+        content = await file.read()
+        nparr = np.frombuffer(content, np.uint8)
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+        result = detector.detect_image_cv(img)  
+        return JSONResponse(content=result)
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": str(e)})
 
-        intensity_score = caculateIntensity(detections)
+@app.post("/predict/")
+async def predict_dengue_route(payload: dict = Body(...)):
+    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.")
 
-        return JSONResponse(content={
-            "total": len(class_ids),
-            "contagem": counts,
-            "objetos": detections,
-            "intensity_score": intensity_score
-        })
+        result = predictor.predict(ibge_code, weeks_to_predict, use_cache=True, scaler_cache=SCALER_CACHE)
+        return result
 
     except Exception as e:
-        if os.path.exists(image_path):
-            os.remove(image_path)
-        return JSONResponse(status_code=500, content={"error": str(e)})
+        return JSONResponse(status_code=500, content={"error": str(e)})

data/inference_data.parquet

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detect.py

@@ -0,0 +1,54 @@
+from collections import Counter
+import numpy as np
+from PIL import Image
+from io import BytesIO
+from ultralytics import YOLO
+
+class DengueDetector:
+    def __init__(self, model_path="./models/DetectsmallTest1.pt"):
+        self.model = YOLO(model_path)
+        self.names = self.model.names
+
+    def calculate_intensity(self, objects):
+        weights = {"piscina": 9, "caixa_agua": 4, "carro": 1}
+        score = sum(weights.get(obj["class"], 0) for obj in objects)
+        return score
+
+    def detect_image(self, image_bytes):
+        # Carregar imagem da memória
+        img = Image.open(BytesIO(image_bytes)).convert("RGB")
+        img_np = np.array(img)  # YOLO aceita np.array diretamente
+        height, width = img_np.shape[:2]
+
+        # Detectar objetos
+        results = self.model(img_np)
+        result = results[0]
+        boxes = result.boxes
+        class_ids = boxes.cls.tolist()
+        confidences = boxes.conf.tolist()
+        class_names = [self.names[int(cls)] for cls in class_ids]
+        counts = Counter(class_names)
+
+        # Construir lista de detecções
+        detections = []
+        for i in range(len(boxes)):
+            x1, y1, x2, y2 = map(float, boxes.xyxy[i])
+            conf = float(confidences[i])
+            cls_id = int(class_ids[i])
+            detections.append({
+                "class": self.names[cls_id],
+                "confidence": round(conf, 4),
+                "box": {
+                    "x1": x1, "y1": y1, "x2": x2, "y2": y2,
+                    "original_width": width, "original_height": height
+                }
+            })
+
+        intensity_score = self.calculate_intensity(detections)
+
+        return {
+            "total": len(class_ids),
+            "contagem": counts,
+            "objetos": detections,
+            "intensity_score": intensity_score
+        }

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