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Upload 6 files
Browse files- Dockerfile +8 -0
- app.py +62 -0
- requirements.txt +5 -0
- static/preview.js +33 -0
- templates/.DS_Store +0 -0
- templates/index.html +29 -0
Dockerfile
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FROM python:3.12-slim
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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COPY . .
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ENV PORT 10000
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EXPOSE 10000
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CMD ["gunicorn", "app:app", "--bind", "0.0.0.0:10000", "--workers", "1"]
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app.py
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import os
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import hashlib
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import cv2
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from flask import Flask, render_template, request, jsonify, send_from_directory
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from ultralytics import YOLO
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from huggingface_hub import hf_hub_download
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# ----- 下載模型 -----
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MODEL_REPO = "sonia2025/best.pt" # 改成你 HF 上的 repo 名稱
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MODEL_FILENAME = "best.pt"
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# 這行會把 best.pt 下載到本地快取目錄
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weights_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILENAME)
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# 印出 hash 以便驗證
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with open(weights_path, "rb") as f:
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print(">> Loaded weights sha256:", hashlib.sha256(f.read()).hexdigest())
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# 載入模型
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model = YOLO(weights_path)
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# ----- Flask App -----
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app = Flask(__name__)
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# 上傳／輸出資料夾
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UPLOAD_FOLDER = os.path.join(app.root_path, "uploads")
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OUTPUT_FOLDER = os.path.join(app.root_path, "outputs")
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os.makedirs(UPLOAD_FOLDER, exist_ok=True)
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os.makedirs(OUTPUT_FOLDER, exist_ok=True)
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@app.route("/", methods=["GET", "POST"])
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def index():
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if request.method == "POST":
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try:
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file = request.files["image"]
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img_path = os.path.join(UPLOAD_FOLDER, file.filename)
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file.save(img_path)
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# 推論 & 標註
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results = model(img_path)
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annotated = results[0].plot()
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# 存檔
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out_path = os.path.join(OUTPUT_FOLDER, file.filename)
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cv2.imwrite(out_path, annotated)
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print("DEBUG -> out_path:", out_path, "exists?", os.path.exists(out_path))
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# 回傳
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return send_from_directory(OUTPUT_FOLDER, file.filename, mimetype="image/jpeg")
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except Exception as e:
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app.logger.error("Detection error", exc_info=e)
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return jsonify({"error": str(e)}), 500
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# GET 時顯示頁面
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return render_template("index.html")
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if __name__ == "__main__":
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# 讀取 Dockerfile 設定的環境變數 PORT
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port = int(os.environ.get("PORT", 5000))
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app.run(host="0.0.0.0", port=port, debug=True)
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requirements.txt
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flask
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ultralytics
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opencv-python
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gunicorn
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huggingface-hub
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static/preview.js
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const input = document.getElementById('image-input');
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const preview = document.getElementById('preview');
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const result = document.getElementById('result');
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const form = document.getElementById('upload-form');
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input.addEventListener('change', () => {
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const file = input.files[0];
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if (!file) return;
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const reader = new FileReader();
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reader.onload = e => {
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preview.src = e.target.result;
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preview.style.display = 'block';
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};
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reader.readAsDataURL(file);
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});
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// 表單送出後，顯示伺服器回傳的效果圖
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form.addEventListener('submit', async e => {
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e.preventDefault();
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const formData = new FormData(form);
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const resp = await fetch('/', {
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method: 'POST',
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body: formData
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});
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if (!resp.ok) {
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alert('辨識失敗');
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return;
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}
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const blob = await resp.blob();
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result.src = URL.createObjectURL(blob);
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result.style.display = 'block';
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});
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templates/.DS_Store
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Binary file (6.15 kB). View file
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templates/index.html
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<!DOCTYPE html>
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<html lang="zh-Hant">
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<head>
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<meta charset="UTF-8" />
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<title>台灣黑熊辨識 Demo</title>
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</head>
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<body>
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<h1>台灣黑熊影像辨識</h1>
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<form id="upload-form" method="POST" enctype="multipart/form-data">
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<input type="file" id="image-input" name="image" accept="image/*" required />
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<button type="submit">上傳並辨識</button>
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</form>
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<!-- 顯示上傳前預覽 -->
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<div>
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<h3>上傳預覽：</h3>
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<img id="preview" src="#" style="max-width: 400px; display: none;" />
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</div>
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<!-- 顯示辨識後結果 -->
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<div>
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<h3>辨識結果：</h3>
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<img id="result" src="#" style="max-width: 400px; display: none;" />
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</div>
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<script src="{{ url_for('static', filename='preview.js') }}"></script>
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</body>
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</html>
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