Add files

Dockerfile

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+FROM ultralytics/ultralytics:latest
+
+WORKDIR /usr/src/ultralytics/
+
+COPY ./requirements.txt /requirements.txt
+
+RUN python3 -m pip install -r requirements.txt
+
+COPY app.y .
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,10 +1,8 @@
 ---
-title: Yolo Test
-emoji: 📈
-colorFrom: green
-colorTo: green
+title: Basic Docker SDK Space
+emoji: 🐳
+colorFrom: purple
+colorTo: gray
 sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+app_port: 7860
+---

app.py

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+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from ultralytics import YOLO
+import requests
+from PIL import Image
+import numpy as np
+import cv2
+import io
+
+app = FastAPI()
+
+# Mount a static directory for serving images
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+# Templates for rendering HTML
+templates = Jinja2Templates(directory="templates")
+
+
+def predict_yolo(image_path):
+    # Assuming you have a YOLO API endpoint that accepts an image and returns predictions
+    with open(image_path, "rb") as file:
+        files = {"file": file}
+        # Load a model
+        model = YOLO('yolov8n.pt')  # pretrained YOLOv8n model
+
+        # Run batched inference on a list of images
+        results = model(image_path)  # return a list of Results objects
+
+        # Process results list
+        for result in results:
+            boxes = result.boxes  # Boxes object for bbox outputs
+            # masks = result.masks  # Masks object for segmentation masks outputs
+            # keypoints = result.keypoints  # Keypoints object for pose outputs
+            # probs = result.probs  # Probs object for classification outputs
+        predictions = boxes.json()
+    return predictions
+
+
+def draw_boxes(image, boxes):
+    for box in boxes:
+        x, y, w, h = box["bbox"]
+        cv2.rectangle(image, (int(x), int(y)), (int(x + w), int(y + h)), (0, 255, 0), 2)
+    return image
+
+
+@app.post("/uploadfile/")
+async def create_upload_file(file: UploadFile = File(...)):
+    contents = await file.read()
+    image = Image.open(io.BytesIO(contents))
+    
+    # Save the image to a static directory
+    save_path = f"static/{file.filename}"
+    image.save(save_path)
+    
+    # Perform YOLO prediction
+    predictions = predict_yolo(save_path)
+    
+    # Draw bounding boxes on the image
+    image_np = np.array(image)
+    image_with_boxes = draw_boxes(image_np, predictions)
+    
+    # Save the image with bounding boxes
+    image_with_boxes_path = f"static/{file.filename.split('.')[0]}_with_boxes.jpg"
+    cv2.imwrite(image_with_boxes_path, cv2.cvtColor(image_with_boxes, cv2.COLOR_RGB2BGR))
+    
+    # Render the HTML with the image and bounding boxes
+    return templates.TemplateResponse("prediction.html", {"request": file, "image_path": image_with_boxes_path})
+
+
+@app.get("/")
+async def read_root():
+    return {"message": "Hello, this is a YOLO prediction API using FastAPI!"}