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Dockerfile

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

README.md

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 ---
-title: Ctrl
-emoji: 📊
-colorFrom: gray
-colorTo: green
+title: Test 1
+emoji: 📈
+colorFrom: purple
+colorTo: gray
 sdk: docker
 pinned: false
+short_description: test server
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import base64
+import cv2
+import numpy as np
+import random
+import uvicorn
+
+app = FastAPI()
+
+# 啟用 CORS 支援（允許 Unity WebGL 或跨網域連線）
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 定義 Unity 傳過來的資料格式
+class ClientRequest(BaseModel):
+    message: str
+
+# 輔助函式：動態生成一張隨機的 2D 圖片，並轉為 Base64 字串
+def generate_random_image_base64():
+    # 1. 建立一張 400x400 的空白黑色圖片 (3 個顏色通道)
+    width, height = 400, 400
+    img = np.zeros((height, width, 3), dtype=np.uint8)
+    
+    # 2. 隨機填滿背景顏色
+    bg_color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
+    img[:] = bg_color
+    
+    # 3. 在圖片上畫一些隨機的彩色圓形或方形
+    for _ in range(5):
+        color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
+        thickness = random.choice([-1, 2, 4]) # -1 代表填滿
+        
+        shape_type = random.choice(["circle", "rectangle"])
+        if shape_type == "circle":
+            center = (random.randint(0, width), random.randint(0, height))
+            radius = random.randint(20, 80)
+            cv2.circle(img, center, radius, color, thickness)
+        else:
+            pt1 = (random.randint(0, width), random.randint(0, height))
+            pt2 = (random.randint(0, width), random.randint(0, height))
+            cv2.rectangle(img, pt1, pt2, color, thickness)
+
+    # 4. 將 OpenCV 的圖片物件編碼為 JPG 格式的二進位數據
+    _, buffer = cv2.imencode('.jpg', img)
+    
+    # 5. 將二進位數據轉為 Base64 字串
+    img_base64 = base64.b64encode(buffer).decode('utf-8')
+    return img_base64
+
+@app.post("/get_image_and_text")
+def handle_unity_request(request: ClientRequest):
+    # 檢查客戶端傳來的是不是 "ok" (不區分大小寫)
+    if request.message.strip().lower() != "ok":
+        raise HTTPException(status_code=400, detail="Invalid message. Please send 'ok'.")
+    
+    # 生成隨機圖片的 Base64 字串
+    base64_image = generate_random_image_base64()
+    
+    # 準備隨機的回覆文字
+    random_responses = [
+        "Server received OK! Here is your lucky image.",
+        "System Status: Perfect. Image generated successfully.",
+        "Hello from Hugging Face! Processing done.",
+        "OK received. Sending custom 2D matrix back to Unity."
+    ]
+    selected_text = random.choice(random_responses)
+    
+    # 回傳給 Unity
+    return {
+        "status": "success",
+        "reply_text": selected_text,
+        "image_base64": base64_image
+    }
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)

gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz 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

note.txt

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+HTTP + WebSocket 混合架構 (Hybrid Architecture)
+
+HTTP 通道：用來處理不需要即時、但需要高穩定度的單次請求（例如載入設定、儲存最後結果）。
+WebSocket 通道：用來開啟一條不會斷的管道，讓 Unity 源源不絕地把鏡頭畫面推給雲端 AI 模型（如 YOLO），並即時拿回辨識結果。
+
+About the cloud server, 
+HTTP + WebSocket(Hybrid Architecture) may be used
+
+HTTP Channel: Used to handle one-off requests that do not require real-time processing but demand high stability (e.g., loading settings, saving final results).
+WebSocket Channel: Used to establish a persistent connection, enabling Unity to continuously stream camera footage to a cloud-based AI model (such as YOLO) and receive recognition results in real time.

requirements.txt

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+fastapi
+uvicorn
+pydantic
+opencv-python-headless
+numpy