Upload 4 files

Dockerfile

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+# Use the official Python 3.10.11 image
+FROM python:3.10.11
+
+# Copy the current directory contents into the container at .
+COPY . .
+
+# Set the working directory to /
+WORKDIR /
+
+# Install requirements.txt 
+RUN pip install --no-cache-dir --upgrade -r /requirements.txt
+
+# Start the FastAPI app on port 7860, the default port expected by Spaces
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import numpy as np
+import cv2
+from ultralytics import YOLO
+from PIL import Image
+import base64
+from io import BytesIO
+
+app = FastAPI()
+model = YOLO("yolov8n.pt")
+
+origins = ["*"]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.post("/detect/")
+async def detect_objects(file: UploadFile):
+ # Process the uploaded image for object detection
+ image_bytes = await file.read()
+ image = np.frombuffer(image_bytes, dtype=np.uint8)
+ image = cv2.imdecode(image, cv2.IMREAD_COLOR)
+ 
+ # Perform object detection with YOLOv8
+ detections = model(image)
+ 
+ return detections[0].tojson()
+
+class ImageData(BaseModel):
+    image: str  # Data gambar dalam format base64
+    
+@app.post("/uploadimage")
+async def upload_image(image_data: ImageData):
+    # Mengonversi base64 ke gambar
+    base64_data = image_data.image.split(',')[1]
+    image = Image.open(BytesIO(base64.b64decode(base64_data)))
+    detections = model(image)
+    
+    return detections[0].tojson()
+    

yolov8n.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f59b3d833e2ff32e194b5bb8e08d211dc7c5bdf144b90d2c8412c47ccfc83b36
+size 6549796