鄭宇翔
commited on
Commit
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Parent(s):
Initial commit with LFS
Browse files- .DS_Store +0 -0
- .gitattributes +1 -0
- README.md +13 -0
- app.py +34 -0
- letter_cnn_model.h5 +3 -0
- requirements.txt +5 -0
- static/script.js +44 -0
- templates/index.html +20 -0
.DS_Store
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Binary file (6.15 kB). View file
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.gitattributes
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*.h5 filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title: Boddyletter Recognizer
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emoji: 👀
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colorFrom: pink
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colorTo: indigo
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sdk: gradio
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sdk_version: 5.36.2
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app_file: app.py
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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from flask import Flask, render_template, request, jsonify
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import numpy as np
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from PIL import Image
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import io
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import base64
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from tensorflow.keras.models import load_model
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app = Flask(__name__)
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model = load_model("letter_cnn_model.h5")
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labels = [chr(ord('a') + i) for i in range(26)]
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@app.route("/")
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def index():
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return render_template("index.html")
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@app.route("/predict", methods=["POST"])
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def predict():
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data = request.json["image"]
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image_data = base64.b64decode(data.split(",")[1])
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img = Image.open(io.BytesIO(image_data)).convert("L").resize((28, 28))
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img = 255 - np.array(img)
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img = img / 255.0
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img = img.reshape(1, 28, 28, 1)
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pred = model.predict(img)
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index = np.argmax(pred[0])
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label = labels[index].upper()
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confidence = float(pred[0][index])
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return jsonify({
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"label": label,
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"confidence": confidence
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})
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if __name__ == "__main__":
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app.run(debug=True)
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letter_cnn_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:bbcde11cd7b8cad7236355ab641b31f1b9d6c00cb21acabee1c5dd8f7b0d9ae8
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size 1514752
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requirements.txt
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flask
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tensorflow
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pillow
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numpy
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static/script.js
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const canvas = document.getElementById("canvas");
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const ctx = canvas.getContext("2d");
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ctx.fillStyle = "black";
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ctx.fillRect(0, 0, canvas.width, canvas.height);
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ctx.strokeStyle = "white";
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ctx.lineWidth = 16;
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ctx.lineCap = "round";
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let drawing = false;
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canvas.onmousedown = () => drawing = true;
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canvas.onmouseup = () => drawing = false;
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canvas.onmousemove = (e) => {
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if (drawing) {
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const rect = canvas.getBoundingClientRect();
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ctx.lineTo(e.clientX - rect.left, e.clientY - rect.top);
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ctx.stroke();
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ctx.beginPath();
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ctx.moveTo(e.clientX - rect.left, e.clientY - rect.top);
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}
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};
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function clearCanvas() {
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ctx.clearRect(0, 0, canvas.width, canvas.height);
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ctx.beginPath();
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ctx.fillRect(0, 0, canvas.width, canvas.height);
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document.getElementById("result").innerText = "";
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}
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function submit() {
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const image = canvas.toDataURL();
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fetch("/predict", {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify({ image })
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})
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.then(res => res.json())
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.then(data => {
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const text = `辨識結果：${data.label}\n信心值：${(data.confidence * 100).toFixed(2)}%`;
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document.getElementById("result").innerText = text;
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});
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}
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templates/index.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<title>手寫字母辨識</title>
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<style>
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canvas { background: black; border: 1px solid white; }
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body { text-align: center; background: #222; color: #eee; font-family: sans-serif; }
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</style>
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</head>
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<body>
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<h1>手寫字母辨識</h1>
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<canvas id="canvas" width="280" height="280"></canvas><br>
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<button onclick="clearCanvas()">清除</button>
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<button onclick="submit()">辨識</button>
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<h2 id="result"></h2>
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<script src="/static/script.js"></script>
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</body>
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</html>
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