Create app.py

app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+import pandas as pd
+import io
+import tempfile
+import os
+
+# 直接複製 main.py 的處理邏輯
+
+def parse_accel_hex(tokens):
+    if len(tokens) < 6:
+        return None, None, None
+    acc_x = int(tokens[0], 16) << 8 | int(tokens[1], 16)
+    acc_y = int(tokens[2], 16) << 8 | int(tokens[3], 16)
+    acc_z = int(tokens[4], 16) << 8 | int(tokens[5], 16)
+    def to_int16(val):
+        return val - 0x10000 if val & 0x8000 else val
+    return to_int16(acc_x), to_int16(acc_y), to_int16(acc_z)
+
+def process_file(file_obj, acc_sensitivity):
+    # 支援 gradio 3.x/4.x: file_obj 可能是檔案物件或路徑字串或 NamedString
+    if hasattr(file_obj, 'read'):
+        # 檔案物件
+        file_obj.seek(0)
+        lines = file_obj.read().decode('utf-8').splitlines()
+    else:
+        # 路徑字串或 NamedString
+        with open(str(file_obj), 'r', encoding='utf-8') as f:
+            lines = f.read().splitlines()
+    output = io.StringIO()
+    output.write('Timestamp,AccelX,AccelY,AccelZ\n')
+    data = []
+    for line in lines:
+        if 'INFO' not in line:
+            continue
+        parts = line.split()
+        if not parts:
+            continue
+        timestamp = parts[0]
+        idx = line.find('value (0x):')
+        if idx == -1:
+            continue
+        hex_str = line[idx + len('value (0x):'):].strip()
+        tokens = [tok for tok in hex_str.split('-') if tok]
+        acc_x, acc_y, acc_z = parse_accel_hex(tokens)
+        if acc_x is None:
+            continue
+        f_acc_x = acc_x / acc_sensitivity
+        f_acc_y = acc_y / acc_sensitivity
+        f_acc_z = acc_z / acc_sensitivity
+        output.write(f"{timestamp},{f_acc_x:.4f},{f_acc_y:.4f},{f_acc_z:.4f}\n")
+        data.append((timestamp, f_acc_x, f_acc_y, f_acc_z))
+    output.seek(0)
+    return output, data
+
+def gradio_interface(file, acc_sensitivity):
+    if file is None:
+        return None, None, None
+    csv_io, data = process_file(file, acc_sensitivity)
+    # 下載用：寫入臨時檔案，回傳路徑
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.csv', mode='w', encoding='utf-8') as tmp:
+        tmp.write(csv_io.getvalue())
+        tmp_path = tmp.name
+    # 視覺化
+    if not data:
+        return tmp_path, None, "No valid data."
+    df = pd.DataFrame(data, columns=['Timestamp', 'AccelX', 'AccelY', 'AccelZ'])
+    # 將 Timestamp 轉為 datetime
+    df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')
+    fig, ax = plt.subplots(figsize=(10,6))
+    ax.plot(df['Timestamp'], df['AccelX'], label='Ax')
+    ax.plot(df['Timestamp'], df['AccelY'], label='Ay')
+    ax.plot(df['Timestamp'], df['AccelZ'], label='Az')
+    ax.set_xlabel('Time')
+    ax.set_ylabel('Acceleration (g)')
+    ax.set_title('Accelerometer Data')
+    ax.legend()
+    fig.autofmt_xdate()
+    plt.tight_layout()
+    return tmp_path, fig, None
+
+demo = gr.Blocks()
+with demo:
+    gr.Markdown("""
+    # 加速度資料轉換與視覺化
+    上傳原始檔案（如 data2.txt），調整 ACC_SENSITIVITY，下載轉換後的 csv，並預覽加速度圖表。
+    """)
+    with gr.Row():
+        file_input = gr.File(label="上傳原始檔案（txt）")
+        acc_input = gr.Number(value=4096.0, label="ACC_SENSITIVITY (例如 4096.0)")
+    with gr.Row():
+        csv_output = gr.File(label="下載轉換後的 CSV")
+        plot_output = gr.Plot(label="加速度圖表")
+    msg_output = gr.Textbox(label="訊息", interactive=False)
+    btn = gr.Button("執行轉換與繪圖")
+    btn.click(fn=gradio_interface, inputs=[file_input, acc_input], outputs=[csv_output, plot_output, msg_output])
+
+demo.launch()