Add application file

Dockerfile

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+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+#     uvicorn app:app --reload
+
+import uuid
+from fastapi import FastAPI, UploadFile, File
+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
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+detect_model_path = "./models/DetectsmallTest1.pt"
+detect_model = YOLO(detect_model_path)
+
+@app.get("/")
+def read_root():
+    return {"message": "Hello!"}
+
+
+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
+
+
+@app.post("/detect/")
+async def upload_image(file: UploadFile = File(...)):
+    print("-" * 50)
+    print("Detecting...")
+
+    image_uuid = str(uuid.uuid4())
+    image_path = f"{image_uuid}.png"
+
+    contents = await file.read()
+    with open(image_path, "wb") as f:
+        f.write(contents)
+
+    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)
+
+        intensity_score = caculateIntensity(detections)
+
+        return JSONResponse(content={
+            "total": len(class_ids),
+            "contagem": counts,
+            "objetos": detections,
+            "intensity_score": intensity_score
+        })
+
+    except Exception as e:
+        if os.path.exists(image_path):
+            os.remove(image_path)
+
+        return JSONResponse(status_code=500, content={"error": str(e)})
+    

models/DetectsmallTest1.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3b4b91b6b7d56edae2d369cb6b7306690a9864a1a51d2bf4f1bd6d2877ccf79c
+size 22508451

requirements.txt

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+fastapi==0.115.12
+uvicorn==0.34.0
+pillow==11.1.0
+opencv-python==4.11.0.86
+numpy==2.1.1
+ultralytics==8.3.105
+torch==2.6.0
+python-multipart