GitHub Actions commited on
Commit
2b57106
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1 Parent(s): f181ff4

Auto-deploy from GitHub

Browse files
api/.DS_Store → .DS_Store RENAMED
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api/.gitattributes → .gitattributes RENAMED
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README.md DELETED
@@ -1,14 +0,0 @@
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- ---
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- title: PreviDengueAPI
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- repository: https://github.com/IonMateus/PreviDengue
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- subdirectory: api/
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- emoji: 🦟
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- colorFrom: blue
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- colorTo: green
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- sdk: docker
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- pinned: false
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- license: mit
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- short_description: 'Identificação de Focos e Surtos de Dengue'
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- ---
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-
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
api/app.py → app.py RENAMED
@@ -23,7 +23,7 @@ predictor: DenguePredictor = None
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  app = FastAPI()
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- # --- Crie um evento de startup para carregar os modelos ---
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  @app.on_event("startup")
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  async def startup_event():
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  global detector, predictor
 
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  app = FastAPI()
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+ # --- evento de startup para carregar os modelos ---
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  @app.on_event("startup")
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  async def startup_event():
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  global detector, predictor
api/detect.py → detect.py RENAMED
@@ -17,10 +17,9 @@ class DengueDetector:
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  def detect_image(self, image_bytes):
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  # Carregar imagem da memória
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  img = Image.open(BytesIO(image_bytes)).convert("RGB")
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- img_np = np.array(img) # YOLO aceita np.array diretamente
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  height, width = img_np.shape[:2]
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- # Detectar objetos
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  results = self.model(img_np)
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  result = results[0]
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  boxes = result.boxes
@@ -29,7 +28,6 @@ class DengueDetector:
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  class_names = [self.names[int(cls)] for cls in class_ids]
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  counts = Counter(class_names)
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- # Construir lista de detecções
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  detections = []
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  for i in range(len(boxes)):
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  x1, y1, x2, y2 = map(float, boxes.xyxy[i])
 
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  def detect_image(self, image_bytes):
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  # Carregar imagem da memória
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  img = Image.open(BytesIO(image_bytes)).convert("RGB")
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+ img_np = np.array(img)
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  height, width = img_np.shape[:2]
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  results = self.model(img_np)
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  result = results[0]
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  boxes = result.boxes
 
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  class_names = [self.names[int(cls)] for cls in class_ids]
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  counts = Counter(class_names)
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  detections = []
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  for i in range(len(boxes)):
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  x1, y1, x2, y2 = map(float, boxes.xyxy[i])
{api/models → models}/.DS_Store RENAMED
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{api/models → models}/DetectsmallTest1.pt RENAMED
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{api/models → models}/checkpoints/model_checkpoint_best_city.keras RENAMED
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{api/models → models}/checkpoints/test_checkpoint1.keras RENAMED
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{api/models → models}/scalers/scaler_dyn_global.pkl RENAMED
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{api/models → models}/scalers/scaler_static_global.pkl RENAMED
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{api/models → models}/scalers/scaler_target_global.pkl RENAMED
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api/predict.py → predict.py RENAMED
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api/requirements.txt → requirements.txt RENAMED
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