diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..21a9240
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,19 @@
+# Byte-compiled / build artifacts
+__pycache__/
+*.py[cod]
+*.egg-info/
+build/
+dist/
+
+# Virtual envs / tooling
+.venv/
+.uv/
+
+# Generated outputs (only at repo root; bundled assets must stay)
+/trajectories.png
+/mouse.onnx
+/mouse.onnx.data
+
+# IDE
+.idea/
+.vscode/
diff --git a/README.md b/README.md
index 9636c04..97293ac 100644
--- a/README.md
+++ b/README.md
@@ -1,55 +1,148 @@
 # mouse_control
-一种基于神经网络来模拟人手移动鼠标的方法  
+
+一种基于神经网络来模拟人手移动鼠标的方法。
+
 ## 简介
-本项目源于该死的轨迹检测。经过初步验证，本项目是有效的。  
-<img src="./imgs/Figure_1.png">  
-上图为真人轨迹移动得到的散点图。这里我们用10个点来拟合轨迹。  
-可以看到，人手的移动其实和pid，以及其他的曲线是有一定区别的。  
-因此，我们选用神经网络来拟合真人鼠标移动轨迹。  
-该神经网络其实很简单，只有三个全连接层。  
-输入为目标距离当前位置的dx，dy。  
-输出为10个点用来模拟真人的轨迹。  
-## 收集鼠标轨迹
-首先运行collect_data.py。运行后，我们会看到这样一个界面。  
-<img src="./imgs/collect.png">  
-我们需要点击红球，就会开始记录鼠标轨迹，点击蓝球，结束记录。这样我们就成功收集到一条鼠标轨迹的数据。  
-重复这样，每收集100次程序会退出。我们一共要收集约300条数据。  
-这样，我们就收集好了数据。  
-## 划分数据集（可选）
-我们将mouse_data.csv用vscode打开，选择一些数据剪切到mouse_data_test.csv中。  
-## 训练模型
-运行train.py，程序就会开始训练模型。最终我们能看到控制台打印出的一条test数据。是一个dx，dy拟合出的十个点。  
-## 验证
-我们将刚刚得到的十个点放到show.py中，观察散点图，发现其轨迹类似于本人鼠标移动轨迹。  
-<img src="./imgs/Figure_2.png"> 
-## c++推理
 
-```c++
-cv::dnn::Net net;
-net = cv::dnn::readNetFromONNX("mouse.onnx");
-if (!net.empty())
-{
-  cv::Mat blob(1, 2, CV_32F, cv::Scalar(100, 100));//这里的100，100是输入dx，dy
-  int sizes[] = { 1, 10, 2 };
-  cv::Mat mat(3, sizes, CV_32F);
-  net.setInput(blob,"input");
-  cv::Mat output = net.forward("output");//输出为1*10*2
-  std::cout << output.at<float>(0,8,0) << std::endl;
+本项目源于该死的轨迹检测。我们用一个简单的三层全连接神经网络去拟合真人鼠标移动轨迹。
 
-}
+<img src="./imgs/Figure_1.png">
+
+上图为真人轨迹移动得到的散点图——用 10 个关键点拟合一条轨迹。可以看到，人手移动和 PID、贝塞尔等曲线有明显区别，因此用神经网络来拟合更合适。
+
+**网络结构**：
+
+- 输入：`(dx, dy)` —— 目标点相对当前位置的偏移
+- 三层全连接：`Linear(2 → 64) → ReLU → Linear(64 → 32) → ReLU → Linear(32 → 20)`
+- 输出：`(10, 2)` —— 10 个轨迹点
+
+仓库内含 `data/` 下的 400 条采集数据和 `src/mouse_control/assets/mouse.onnx` 预训练模型，clone 之后可以直接 `mouse-visualize` 看效果。
+
+## 安装
+
+使用 [uv](https://docs.astral.sh/uv/) 管理：
+
+```bash
+uv sync
 ```
-## python推理
+
+或从构建产物安装：
+
+```bash
+uv pip install dist/mouse_control-0.1.0-py3-none-any.whl
+```
+
+要求 Python ≥ 3.12。
+
+## 项目结构
+
+```
+mouse_control/
+├── pyproject.toml
+├── README.md
+├── data/                       # 训练 / 测试数据 (380 + 20 条)
+│   ├── mouse_data.csv
+│   └── mouse_data_test.csv
+├── imgs/                       # README 图片
+└── src/mouse_control/          # 包源码 (src layout)
+    ├── __init__.py             # 公共 API
+    ├── model.py                # SimpleNet 网络定义
+    ├── collect.py              # 数据采集 GUI
+    ├── train.py                # 训练 + ONNX 导出
+    ├── visualize.py            # 轨迹可视化
+    ├── inference.py            # 模型加载 / 推理辅助
+    └── assets/
+        └── mouse.onnx          # 随包发布的预训练模型
+```
+
+## 命令行用法
+
+安装后可以直接调用三个命令（也可以用 `uv run <cmd>`）：
+
+### 1. 采集真人鼠标轨迹
+
+```bash
+uv run mouse-collect -o data/mouse_data.csv -n 100
+```
+
+<img src="./imgs/collect.png">
+
+全屏界面：点**红球**开始记录、移动到**蓝球**点击结束记录，每条轨迹保存 10 个关键点到 CSV。
+
+- 默认采集 100 条后自动退出，按 `Esc` 提前退出
+- 推荐采集 ≥ 300 条以获得较好的训练效果
+
+### 2. 划分训练 / 测试集
+
+把 `mouse_data.csv` 里**剪切**若干行（10-20 条）到 `mouse_data_test.csv` 即可。
+
+### 3. 训练模型
+
+```bash
+uv run mouse-train \
+  --train-csv data/mouse_data.csv \
+  --test-csv data/mouse_data_test.csv \
+  --output mouse.onnx \
+  --epochs 1000
+```
+
+训练结束后在当前目录生成 `mouse.onnx`。其它参数 `--batch-size` `--lr` 可调。
+
+### 4. 可视化效果
+
+```bash
+uv run mouse-visualize --n-trajectories 10 --seed 42
+```
+
+<img src="./imgs/Figure_2.png">
+
+随机生成若干目标点，每条轨迹包含 10 个模型输出点 + 80 个三次样条插值点。`--no-show` 跳过 GUI 窗口、只写 PNG；`--model` 指定其它 ONNX 文件。
+
+## Python API
 
 ```python
-from cv2 import dnn
-import numpy as np
- 
-net = dnn.readNetFromONNX("mouse.onnx")
-matblob = np.array([[100,200]])
-net.setInput(matblob)
-print('input = {}'.format(matblob))
-output = net.forward()
-print('output = {}'.format(output))
+import mouse_control as mc
+
+# 加载模型：优先 ./mouse.onnx，否则回退到包内打包的预训练模型
+session = mc.load_model()
+
+# 推理：输入 (dx, dy)，输出 (10, 2) 轨迹点
+pts = mc.predict(session, 120, -80)
+# -> ndarray, shape (10, 2), dtype float32
+
+# 直接使用模型类（用于自训练 / 微调）
+from mouse_control import SimpleNet
 ```
 
+## ONNX 推理（其它语言）
 
+### Python (onnxruntime)
+
+```python
+import numpy as np
+import onnxruntime as ort
+
+session = ort.InferenceSession("mouse.onnx")
+inp = np.array([[[100.0, 200.0]]], dtype=np.float32)  # dx=100, dy=200
+output = session.run(None, {"input": inp})[0]          # shape (1, 10, 2)
+```
+
+### C++ (OpenCV DNN)
+
+```cpp
+cv::dnn::Net net = cv::dnn::readNetFromONNX("mouse.onnx");
+cv::Mat blob(1, 2, CV_32F, cv::Scalar(100, 100));  // 输入 dx=100, dy=100
+net.setInput(blob, "input");
+cv::Mat output = net.forward("output");            // 输出 1*10*2
+std::cout << output.at<float>(0, 8, 0) << std::endl;
+```
+
+## 打包发布
+
+```bash
+uv build
+# -> dist/mouse_control-0.1.0-py3-none-any.whl
+# -> dist/mouse_control-0.1.0.tar.gz
+```
+
+wheel 内含 `assets/mouse.onnx` 预训练模型，安装后直接 `mc.load_model()` 即可使用。
diff --git a/collect_mouse_data.py b/collect_mouse_data.py
deleted file mode 100644
index 68e9f91..0000000
--- a/collect_mouse_data.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import math
-import random
-import tkinter as tk
-import matplotlib.pyplot as plt
-import csv
-from tkinter import Label
-
-# 创建窗口
-root = tk.Tk()
-root.attributes('-fullscreen', True)  # 全屏显示
-
-label_n = Label(root, text="n: 0", font=("Helvetica", 16))
-label_n.pack()
-
-csv_file_path = "mouse_data.csv"
-
-screen_width = root.winfo_screenwidth()
-screen_height = root.winfo_screenheight()
-
-# 设置小球的初始位置
-ball1_pos = (screen_width/2, screen_height/2)
-ball2_pos = (ball1_pos[0] + random.randint(-200, 200),ball1_pos[1] + random.randint(-200, 200))
-
-# 设置小球的半径
-ball_radius = 20
-
-# 设置鼠标记录状态
-recording = False
-mouse_path = []
-
-n=0
-
-# 鼠标移动事件处理函数
-def motion(event):
-    global recording, mouse_path, n
-    if recording:
-        mouse_path.append((event.x, event.y))
-
-# 鼠标点击事件处理函数
-def mouse_click(event):
-    global recording, mouse_path, ball2_pos, n
-
-    if event.x >= ball1_pos[0] - ball_radius and event.x <= ball1_pos[0] + ball_radius and event.y >= ball1_pos[1] - ball_radius and event.y <= ball1_pos[1] + ball_radius:
-        recording = True
-        mouse_path = [(event.x, event.y)]
-    elif event.x >= ball2_pos[0] - ball_radius and event.x <= ball2_pos[0] + ball_radius and event.y >= ball2_pos[1] - ball_radius and event.y <= ball2_pos[1] + ball_radius:
-        recording = False
-        canvas.delete("ball2")
-        #visualize_path(mouse_path)  # 可视化鼠标轨迹
-        save_to_csv(mouse_path)
-        n = n+1
-        if n == 100:
-            root.destroy()
-        label_n.config(text=f"n: {n}")
-        mouse_path = []
-
-        # 重新生成第二个小球的位置
-        ball2_pos = (ball1_pos[0] + random.randint(-200, 200), ball1_pos[1] + random.randint(-200, 200))
-        
-        # 绘制新的第二个小球
-        canvas.create_oval(ball2_pos[0]-ball_radius, ball2_pos[1]-ball_radius, ball2_pos[0]+ball_radius, ball2_pos[1]+ball_radius, fill="blue", tags="ball2")
-
-
-# 键盘事件处理函数
-def key(event):
-    if event.keysym == "Escape":
-        root.destroy()
-
-def save_to_csv(path):
-    # 将路径坐标转换为相对于起点的坐标
-    x_rel = [px - path[0][0] for px, py in path]
-    y_rel = [-(py - path[0][1]) for px, py in path]
-
-    # 计算每个点相对于起点的距离，用于z轴表示
-    distances = [math.sqrt((x_rel[0] - px)**2 + (y_rel[0] - py)**2) for px, py in zip(x_rel, y_rel)]
-
-    # 选择10个关键点
-    key_points_indices = [int(i) for i in range(0, len(path), max(1, len(path)//10))]
-    key_points_x = [x_rel[i] for i in key_points_indices]
-    key_points_y = [y_rel[i] for i in key_points_indices]
-    key_points_distances = [distances[i] for i in key_points_indices]
-
-    # 打开 CSV 文件进行写操作
-    with open(csv_file_path, mode='a', newline='') as csv_file:
-        csv_writer = csv.writer(csv_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
-        # 写入一行数据
-        csv_writer.writerow([f"{key_points_x[-1]},{key_points_y[-1]}"] + [f"{key_points_x[i]},{key_points_y[i]}" for i in range(0,10)])
-
-
-def visualize_path(path):
-    # 将路径坐标转换为相对于起点的坐标
-    x_rel = [px - path[0][0] for px, py in path]
-    y_rel = [-(py - path[0][1]) for px, py in path]
-
-    # 计算每个点相对于起点的距离，用于z轴表示
-    distances = [math.sqrt((x_rel[0] - px)**2 + (y_rel[0] - py)**2) for px, py in zip(x_rel, y_rel)]
-
-    # 选择10个关键点
-    key_points_indices = [int(i) for i in range(0, len(path), max(1, len(path)//10))]
-    key_points_x = [x_rel[i] for i in key_points_indices]
-    key_points_y = [y_rel[i] for i in key_points_indices]
-    key_points_distances = [distances[i] for i in key_points_indices]
-
-    # 使用z轴信息，通过颜色表示距离的远近
-    plt.scatter(key_points_x, key_points_y, c=key_points_distances, cmap='viridis', marker='o', s=50)
-
-    # 在关键点位置添加文本标签，显示终点到起点的距离
-    
-    plt.text(key_points_x[-1], key_points_y[-1], f'Distance to Origin: {key_points_distances[-1]:.2f}', ha='right', va='bottom', bbox=dict(facecolor='white', alpha=0.5))
-
-    # 添加颜色条，表示z轴信息
-    plt.colorbar(label='Distance to Endpoint')
-
-    plt.show()
-
-# 绘制小球
-canvas = tk.Canvas(root, width=root.winfo_screenwidth(), height=root.winfo_screenheight())
-canvas.pack()
-canvas.create_oval(ball1_pos[0]-ball_radius, ball1_pos[1]-ball_radius, ball1_pos[0]+ball_radius, ball1_pos[1]+ball_radius, fill="red")
-canvas.create_oval(ball2_pos[0]-ball_radius, ball2_pos[1]-ball_radius, ball2_pos[0]+ball_radius, ball2_pos[1]+ball_radius, fill="blue", tags="ball2")
-
-# 绑定鼠标事件
-canvas.bind('<Motion>', motion)
-canvas.bind('<Button-1>', mouse_click)
-
-# 绑定键盘事件
-root.bind('<Key>', key)
-
-# 运行窗口
-root.mainloop()
diff --git a/data/mouse_data.csv b/data/mouse_data.csv
new file mode 100644
index 0000000..67c736e
--- /dev/null
+++ b/data/mouse_data.csv
@@ -0,0 +1,380 @@
+"189,-42","0,0","7,-1","58,-25","136,-49","188,-57","219,-60","216,-54","211,-51","205,-47","196,-43"
+"117,102","0,0","12,7","36,24","72,45","96,60","108,71","110,77","112,83","114,89","116,96"
+"-83,87","0,0","-4,2","-11,10","-26,28","-40,44","-56,60","-63,69","-70,75","-76,79","-80,83"
+"-55,-84","0,0","-6,-4","-12,-15","-22,-32","-33,-46","-41,-57","-45,-64","-46,-69","-49,-73","-51,-78"
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+"65,106","0,0","-4,-1","2,5","11,18","28,34","47,54","55,70","59,83","63,96","65,106"
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+"-146,38","0,0","-11,0","-39,3","-80,11","-125,18","-149,19","-159,21","-164,23","-168,26","-157,32"
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+"-9,5","0,0","6,-5","15,-8","20,-9","25,-9","28,-7","25,-5","18,-2","11,0","6,1"
+"200,-109","0,0","2,-4","8,-11","30,-36","59,-60","85,-73","120,-87","152,-98","171,-104","184,-106"
+"-15,184","0,0","2,19","2,50","-3,83","-17,112","-20,132","-16,151","-16,164","-17,170","-18,176"
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+"-186,185","0,0","-10,13","-58,72","-106,118","-118,141","-133,158","-143,166","-152,171","-163,179","-181,190"
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+"-45,-81","0,0","-1,-16","-4,-45","-11,-60","-16,-73","-20,-82","-26,-87","-33,-89","-39,-91","-43,-88"
+"-18,44","0,0","0,2","-1,7","-4,20","-6,24","-7,29","-9,33","-11,36","-12,38","-13,39"
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+"-124,-125","0,0","-8,-5","-26,-20","-54,-41","-79,-68","-100,-95","-115,-114","-124,-122","-128,-125","-126,-128"
+"-109,62","0,0","-10,8","-22,21","-34,33","-56,48","-79,60","-86,63","-92,64","-98,64","-104,63"
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+"-102,152","0,0","-5,17","-38,85","-54,104","-64,113","-73,124","-79,130","-84,137","-90,144","-96,149"
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+"-76,155","0,0","-2,17","-9,48","-23,78","-38,99","-48,115","-53,125","-55,131","-59,135","-65,144"
+"116,81","0,0","10,7","24,17","61,41","74,59","79,63","83,69","94,74","101,77","109,79"
+"19,169","0,0","7,12","15,43","23,78","26,105","22,126","20,141","19,149","18,155","18,161"
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diff --git a/data/mouse_data_test.csv b/data/mouse_data_test.csv
new file mode 100644
index 0000000..2612eab
--- /dev/null
+++ b/data/mouse_data_test.csv
@@ -0,0 +1,20 @@
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diff --git a/mouse_data.csv b/mouse_data.csv
deleted file mode 100644
index d3f5a12..0000000
--- a/mouse_data.csv
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/mouse_data_test.csv b/mouse_data_test.csv
deleted file mode 100644
index d3f5a12..0000000
--- a/mouse_data_test.csv
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..a3f33f4
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,44 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "mouse-control"
+version = "0.1.0"
+description = "Neural-network mouse trajectory generation that mimics human motion."
+readme = "README.md"
+requires-python = ">=3.12"
+keywords = ["mouse", "trajectory", "neural-network", "onnx", "anti-detection"]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.12",
+    "Operating System :: Microsoft :: Windows",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+]
+dependencies = [
+    "matplotlib>=3.10",
+    "numpy>=2.0",
+    "onnxruntime>=1.20",
+    "onnxscript>=0.7",
+    "pandas>=2.0",
+    "scipy>=1.14",
+    "torch>=2.5",
+]
+
+[project.scripts]
+mouse-collect = "mouse_control.collect:main"
+mouse-train = "mouse_control.train:main"
+mouse-visualize = "mouse_control.visualize:main"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/mouse_control"]
+artifacts = ["src/mouse_control/assets/*.onnx"]
+
+[tool.hatch.build.targets.sdist]
+include = [
+    "src/",
+    "data/",
+    "imgs/",
+    "README.md",
+    "pyproject.toml",
+]
diff --git a/show.py b/show.py
deleted file mode 100644
index 754b077..0000000
--- a/show.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import matplotlib.pyplot as plt
-import torch
-
-# 您的tensor数据
-points = torch.tensor([
-    [   0.4762,    0.9082],
-        [ -31.5831,  -39.9315],
-        [ -52.1844,  -71.0071],
-        [ -60.4211,  -83.4873],
-        [ -69.3365, -100.6035],
-        [ -80.3101, -110.0435],
-        [ -86.7765, -120.0472],
-        [ -93.2574, -127.8565],
-        [ -98.5992, -135.3300]
-])
-
-# 提取x和y坐标
-x = points[:, 0].tolist()
-y = points[:, 1].tolist()
-
-# 绘制散点图
-plt.scatter(x, y)
-
-# 显示图形
-plt.show()
\ No newline at end of file
diff --git a/src/mouse_control/__init__.py b/src/mouse_control/__init__.py
new file mode 100644
index 0000000..7dcc508
--- /dev/null
+++ b/src/mouse_control/__init__.py
@@ -0,0 +1,7 @@
+"""Neural-network mouse trajectory generation that mimics human motion."""
+
+from mouse_control.inference import load_model, predict
+from mouse_control.model import SimpleNet
+
+__version__ = "0.1.0"
+__all__ = ["SimpleNet", "load_model", "predict", "__version__"]
diff --git a/src/mouse_control/assets/__init__.py b/src/mouse_control/assets/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mouse_control/assets/mouse.onnx b/src/mouse_control/assets/mouse.onnx
new file mode 100644
index 0000000..d768196
Binary files /dev/null and b/src/mouse_control/assets/mouse.onnx differ
diff --git a/src/mouse_control/collect.py b/src/mouse_control/collect.py
new file mode 100644
index 0000000..1ac453d
--- /dev/null
+++ b/src/mouse_control/collect.py
@@ -0,0 +1,133 @@
+import argparse
+import csv
+import random
+import tkinter as tk
+from pathlib import Path
+
+BG_COLOR = "#2b2b2b"
+BALL_RADIUS = 20
+TARGET_OFFSET_RANGE = 200
+N_KEYPOINTS = 10
+
+
+class TrajectoryCollector:
+    def __init__(self, csv_path: Path, n_trajectories: int = 100) -> None:
+        self.csv_path = Path(csv_path)
+        self.n_target = n_trajectories
+
+        self.root = tk.Tk()
+        self.root.attributes("-fullscreen", True)
+        self.root.configure(bg=BG_COLOR)
+
+        screen_w = self.root.winfo_screenwidth()
+        screen_h = self.root.winfo_screenheight()
+        self.ball1_pos = (screen_w / 2, screen_h / 2)
+        self.ball2_pos = self._new_target()
+
+        self.label = tk.Label(
+            self.root, text="n: 0", font=("Helvetica", 16),
+            bg=BG_COLOR, fg="white",
+        )
+        self.label.pack()
+        self.canvas = tk.Canvas(
+            self.root, width=screen_w, height=screen_h,
+            bg=BG_COLOR, highlightthickness=0,
+        )
+        self.canvas.pack()
+        self._draw_ball(self.ball1_pos, "red")
+        self._draw_ball(self.ball2_pos, "blue", tags="ball2")
+
+        self.recording = False
+        self.mouse_path: list[tuple[int, int]] = []
+        self.n = 0
+
+        self.canvas.bind("<Motion>", self._on_motion)
+        self.canvas.bind("<Button-1>", self._on_click)
+        self.root.bind("<Key>", self._on_key)
+
+    def _new_target(self) -> tuple[float, float]:
+        cx, cy = self.ball1_pos
+        return (
+            cx + random.randint(-TARGET_OFFSET_RANGE, TARGET_OFFSET_RANGE),
+            cy + random.randint(-TARGET_OFFSET_RANGE, TARGET_OFFSET_RANGE),
+        )
+
+    def _draw_ball(self, pos: tuple[float, float], color: str, tags: str = "") -> None:
+        x, y = pos
+        self.canvas.create_oval(
+            x - BALL_RADIUS, y - BALL_RADIUS,
+            x + BALL_RADIUS, y + BALL_RADIUS,
+            fill=color, tags=tags,
+        )
+
+    @staticmethod
+    def _hit(event: tk.Event, pos: tuple[float, float]) -> bool:
+        return (
+            abs(event.x - pos[0]) <= BALL_RADIUS
+            and abs(event.y - pos[1]) <= BALL_RADIUS
+        )
+
+    def _on_motion(self, event: tk.Event) -> None:
+        if self.recording:
+            self.mouse_path.append((event.x, event.y))
+
+    def _on_click(self, event: tk.Event) -> None:
+        if self._hit(event, self.ball1_pos):
+            self.recording = True
+            self.mouse_path = [(event.x, event.y)]
+        elif self._hit(event, self.ball2_pos):
+            if not self.recording or len(self.mouse_path) < N_KEYPOINTS:
+                return
+            self.recording = False
+            self.canvas.delete("ball2")
+            self._save_path(self.mouse_path)
+            self.n += 1
+            if self.n >= self.n_target:
+                self.root.destroy()
+                return
+            self.label.config(text=f"n: {self.n}")
+            self.mouse_path = []
+            self.ball2_pos = self._new_target()
+            self._draw_ball(self.ball2_pos, "blue", tags="ball2")
+
+    def _on_key(self, event: tk.Event) -> None:
+        if event.keysym == "Escape":
+            self.root.destroy()
+
+    def _save_path(self, path: list[tuple[int, int]]) -> None:
+        origin_x, origin_y = path[0]
+        x_rel = [px - origin_x for px, _ in path]
+        y_rel = [-(py - origin_y) for _, py in path]
+        step = max(1, len(path) // N_KEYPOINTS)
+        idx = list(range(0, len(path), step))
+        kx = [x_rel[i] for i in idx]
+        ky = [y_rel[i] for i in idx]
+        with open(self.csv_path, "a", newline="") as f:
+            writer = csv.writer(f, quoting=csv.QUOTE_MINIMAL)
+            row = [f"{kx[-1]},{ky[-1]}"] + [
+                f"{kx[i]},{ky[i]}" for i in range(N_KEYPOINTS)
+            ]
+            writer.writerow(row)
+
+    def run(self) -> None:
+        self.root.mainloop()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Collect human mouse trajectories via a fullscreen Tk GUI.",
+    )
+    parser.add_argument(
+        "--output", "-o", type=Path, default=Path("mouse_data.csv"),
+        help="CSV file to append trajectories to (default: mouse_data.csv).",
+    )
+    parser.add_argument(
+        "--count", "-n", type=int, default=100,
+        help="Number of trajectories to collect before exiting (default: 100).",
+    )
+    args = parser.parse_args()
+    TrajectoryCollector(args.output, args.count).run()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/mouse_control/inference.py b/src/mouse_control/inference.py
new file mode 100644
index 0000000..31309b7
--- /dev/null
+++ b/src/mouse_control/inference.py
@@ -0,0 +1,28 @@
+from importlib.resources import files
+from pathlib import Path
+
+import numpy as np
+import onnxruntime as ort
+
+
+def load_model(model_path: str | Path | None = None) -> ort.InferenceSession:
+    """Load the ONNX mouse-trajectory model.
+
+    Resolution order when ``model_path`` is None:
+    1. ``./mouse.onnx`` in the current working directory (freshly trained).
+    2. The model bundled inside the installed package.
+    """
+    if model_path is not None:
+        return ort.InferenceSession(str(model_path))
+    cwd_model = Path("mouse.onnx")
+    if cwd_model.is_file():
+        return ort.InferenceSession(str(cwd_model))
+    data = files("mouse_control.assets").joinpath("mouse.onnx").read_bytes()
+    return ort.InferenceSession(data)
+
+
+def predict(session: ort.InferenceSession, dx: float, dy: float) -> np.ndarray:
+    """Return a (10, 2) array of trajectory points for the given displacement."""
+    inp = np.array([[[float(dx), float(dy)]]], dtype=np.float32)
+    input_name = session.get_inputs()[0].name
+    return session.run(None, {input_name: inp})[0][0]
diff --git a/src/mouse_control/model.py b/src/mouse_control/model.py
new file mode 100644
index 0000000..0007629
--- /dev/null
+++ b/src/mouse_control/model.py
@@ -0,0 +1,17 @@
+import torch
+import torch.nn as nn
+
+
+class SimpleNet(nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.fc1 = nn.Linear(2, 64)
+        self.fc2 = nn.Linear(64, 32)
+        self.fc3 = nn.Linear(32, 20)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = torch.flatten(x, start_dim=1)
+        x = nn.functional.relu(self.fc1(x))
+        x = nn.functional.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x.view(-1, 10, 2)
diff --git a/src/mouse_control/train.py b/src/mouse_control/train.py
new file mode 100644
index 0000000..dbbeb70
--- /dev/null
+++ b/src/mouse_control/train.py
@@ -0,0 +1,113 @@
+import argparse
+from pathlib import Path
+
+import pandas as pd
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, Dataset
+
+from mouse_control.model import SimpleNet
+
+
+class TrajectoryDataset(Dataset):
+    def __init__(self, dx_dy: torch.Tensor, labels: torch.Tensor) -> None:
+        self.dx_dy = dx_dy
+        self.labels = labels
+
+    def __len__(self) -> int:
+        return len(self.dx_dy)
+
+    def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]:
+        return self.dx_dy[idx], self.labels[idx]
+
+
+def _load_csv(csv_path: Path) -> tuple[torch.Tensor, torch.Tensor]:
+    df = pd.read_csv(csv_path, header=None)
+    inputs = df.iloc[:, 0].apply(lambda x: list(map(int, x.split(","))))
+    labels = df.apply(
+        lambda row: [list(map(int, row[i].split(","))) for i in range(1, 11)],
+        axis=1,
+    )
+    inputs_t = torch.Tensor(inputs.tolist()).unsqueeze(1)
+    labels_t = torch.Tensor(labels.tolist())
+    return inputs_t, labels_t
+
+
+def train(
+    train_csv: Path,
+    test_csv: Path,
+    output: Path,
+    epochs: int = 1000,
+    batch_size: int = 64,
+    lr: float = 1e-3,
+) -> None:
+    train_x, train_y = _load_csv(train_csv)
+    test_x, test_y = _load_csv(test_csv)
+
+    train_loader = DataLoader(
+        TrajectoryDataset(train_x, train_y), batch_size=batch_size, shuffle=True,
+    )
+    test_loader = DataLoader(
+        TrajectoryDataset(test_x, test_y), batch_size=batch_size, shuffle=False,
+    )
+
+    model = SimpleNet()
+    criterion = nn.MSELoss()
+    optimizer = optim.Adam(model.parameters(), lr=lr)
+    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.9)
+
+    model.train()
+    for epoch in range(epochs):
+        epoch_loss = 0.0
+        for batch_x, batch_y in train_loader:
+            optimizer.zero_grad()
+            output_t = model(batch_x)
+            loss = criterion(output_t, batch_y)
+            loss.backward()
+            optimizer.step()
+            scheduler.step()
+            epoch_loss += loss.item()
+        if (epoch + 1) % 50 == 0 or epoch == 0:
+            print(f"epoch {epoch + 1:>4}/{epochs}  train_loss={epoch_loss / len(train_loader):.4f}")
+
+    model.eval()
+    with torch.no_grad():
+        total = 0.0
+        last_batch = None
+        for batch_x, batch_y in test_loader:
+            pred = model(batch_x)
+            total += criterion(pred, batch_y).item()
+            last_batch = (batch_x, pred)
+        print(f"test_loss={total / len(test_loader):.4f}")
+        if last_batch is not None:
+            sample_x, sample_pred = last_batch
+            print("sample input :", sample_x[0].tolist())
+            print("sample output:", sample_pred[0].tolist())
+
+    dummy = torch.randn(1, 1, 2)
+    torch.onnx.export(
+        model, dummy, str(output),
+        input_names=["input"], output_names=["output"],
+        external_data=False,
+    )
+    print(f"exported -> {output}")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Train the mouse trajectory model.")
+    parser.add_argument("--train-csv", type=Path, default=Path("data/mouse_data.csv"))
+    parser.add_argument("--test-csv", type=Path, default=Path("data/mouse_data_test.csv"))
+    parser.add_argument("--output", "-o", type=Path, default=Path("mouse.onnx"))
+    parser.add_argument("--epochs", type=int, default=1000)
+    parser.add_argument("--batch-size", type=int, default=64)
+    parser.add_argument("--lr", type=float, default=1e-3)
+    args = parser.parse_args()
+    train(
+        args.train_csv, args.test_csv, args.output,
+        epochs=args.epochs, batch_size=args.batch_size, lr=args.lr,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/mouse_control/visualize.py b/src/mouse_control/visualize.py
new file mode 100644
index 0000000..fe3507a
--- /dev/null
+++ b/src/mouse_control/visualize.py
@@ -0,0 +1,104 @@
+import argparse
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.interpolate import CubicSpline
+
+from mouse_control.inference import load_model, predict
+
+
+def _interpolate(xs: np.ndarray, ys: np.ndarray, n_points: int) -> tuple[np.ndarray, np.ndarray]:
+    chord = np.cumsum(np.sqrt(np.diff(xs) ** 2 + np.diff(ys) ** 2))
+    t = np.concatenate([[0.0], chord])
+    if t[-1] == 0:
+        return xs, ys
+    cs_x = CubicSpline(t, xs)
+    cs_y = CubicSpline(t, ys)
+    t_dense = np.linspace(0, t[-1], n_points)
+    return cs_x(t_dense), cs_y(t_dense)
+
+
+def visualize(
+    model_path: Path | None = None,
+    n_trajectories: int = 10,
+    n_interp: int = 80,
+    seed: int | None = None,
+    save_path: Path | None = Path("trajectories.png"),
+    show: bool = True,
+) -> None:
+    rng = np.random.default_rng(seed)
+    targets = rng.integers(low=-200, high=201, size=(n_trajectories, 2))
+
+    session = load_model(model_path)
+    cmap = plt.get_cmap("tab10")
+    fig, ax = plt.subplots(figsize=(10, 8))
+
+    for i, (dx, dy) in enumerate(targets):
+        pred = predict(session, dx, dy)  # (10, 2)
+        xs = np.concatenate([[0.0], pred[:, 0]])
+        ys = np.concatenate([[0.0], pred[:, 1]])
+        xs_dense, ys_dense = _interpolate(xs, ys, n_interp)
+
+        color = cmap(i % cmap.N)
+        ax.plot(xs_dense, ys_dense, "-", color=color, alpha=0.85, linewidth=1.6)
+        ax.scatter(xs_dense, ys_dense, color=color, s=6, alpha=0.5, zorder=2)
+        ax.scatter(
+            pred[:, 0], pred[:, 1], color=color, s=35, zorder=3,
+            edgecolors="white", linewidths=0.6,
+        )
+        ax.scatter(
+            [dx], [dy], color=color, marker="x", s=140, linewidths=2.5,
+            zorder=4, label=f"target ({dx}, {dy})",
+        )
+
+    ax.scatter([0], [0], color="black", s=90, zorder=5, label="start (0, 0)")
+    ax.axhline(0, color="gray", linewidth=0.5, alpha=0.5)
+    ax.axvline(0, color="gray", linewidth=0.5, alpha=0.5)
+    ax.set_aspect("equal")
+    ax.set_title(
+        f"Mouse trajectories — 10 model points + cubic-spline interpolation ({n_interp} pts)",
+    )
+    ax.set_xlabel("dx")
+    ax.set_ylabel("dy")
+    ax.legend(loc="best", fontsize=8, ncol=2)
+    ax.grid(True, alpha=0.3)
+    plt.tight_layout()
+
+    if save_path is not None:
+        plt.savefig(save_path, dpi=120)
+        print(f"saved -> {save_path}")
+    if show:
+        plt.show()
+    plt.close(fig)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Render predicted mouse trajectories for random targets.",
+    )
+    parser.add_argument(
+        "--model", type=Path, default=None,
+        help="Path to ONNX model (defaults to ./mouse.onnx or bundled model).",
+    )
+    parser.add_argument("--n-trajectories", "-n", type=int, default=10)
+    parser.add_argument("--n-interp", type=int, default=80)
+    parser.add_argument("--seed", type=int, default=None)
+    parser.add_argument("--save", type=Path, default=Path("trajectories.png"))
+    parser.add_argument(
+        "--no-show", action="store_true",
+        help="Skip opening the matplotlib window (still writes --save).",
+    )
+    args = parser.parse_args()
+    visualize(
+        model_path=args.model,
+        n_trajectories=args.n_trajectories,
+        n_interp=args.n_interp,
+        seed=args.seed,
+        save_path=args.save,
+        show=not args.no_show,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/train.py b/train.py
deleted file mode 100644
index 12c1d64..0000000
--- a/train.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import numpy as np
-import pandas as pd
-import torch
-from torch.utils.data import Dataset, DataLoader
-import torch.nn as nn
-import torch.optim as optim
-import onnxruntime
-import onnx
-from onnxsim import simplify
-
-# 读取CSV文件
-train_csv_path = 'mouse_data.csv'  # 替换为你的CSV文件路径
-train_csv = pd.read_csv(train_csv_path, header=None)
-test_csv_path = 'mouse_data_test.csv'  # 替换为你的CSV文件路径
-test_csv = pd.read_csv(test_csv_path, header=None)
-
-# 提取dx, dy和标签
-dx_dy_train = train_csv.iloc[:, 0].apply(lambda x: list(map(int, x.split(','))))
-dx_dy_labels_train = train_csv.apply(lambda row: [list(map(int, row[i].split(','))) for i in range(1,11)], axis=1)
-dx_dy_test = test_csv.iloc[:, 0].apply(lambda x: list(map(int, x.split(','))))
-dx_dy_labels_test = test_csv.apply(lambda row: [list(map(int, row[i].split(','))) for i in range(1,11)], axis=1)
-
-# 转换为PyTorch Tensor
-dx_dy_train_tensor = torch.Tensor(dx_dy_train.tolist())
-dx_dy_train_tensor = dx_dy_train_tensor.unsqueeze(1)
-labels_train_tensor = torch.Tensor(dx_dy_labels_train.tolist())
-dx_dy_test_tensor = torch.Tensor(dx_dy_test.tolist())
-dx_dy_test_tensor = dx_dy_test_tensor.unsqueeze(1)
-labels_test_tensor = torch.Tensor(dx_dy_labels_test.tolist())
-
-# 创建自定义Dataset
-class CustomDataset(Dataset):
-    def __init__(self, dx_dy, labels):
-        self.dx_dy = dx_dy
-        self.labels = labels
-
-    def __len__(self):
-        return len(self.dx_dy)
-
-    def __getitem__(self, idx):
-        return self.dx_dy[idx], self.labels[idx]
-
-# 创建Dataset和DataLoader
-train_dataset = CustomDataset(dx_dy_train_tensor, labels_train_tensor)
-test_dataset = CustomDataset(dx_dy_test_tensor, labels_test_tensor)
-batch_size = 64
-train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
-test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
-
-# 定义神经网络模型
-class SimpleNet(nn.Module):
-    def __init__(self):
-        super(SimpleNet, self).__init__()
-        self.fc1 = nn.Linear(2, 64)
-        self.fc2 = nn.Linear(64, 32)
-        self.fc3 = nn.Linear(32, 20)
-
-    def forward(self, input_data):
-        x = torch.flatten(input_data, start_dim=1)
-        x = self.fc1(x)
-        x = nn.ReLU()(x)
-        x = self.fc2(x)
-        x = nn.ReLU()(x)
-        x = self.fc3(x)
-        return x.view(-1, 10, 2)
-
-# 初始化模型、损失函数和优化器
-model = SimpleNet()
-criterion = nn.MSELoss()
-optimizer = optim.Adam(model.parameters(), lr=0.001)
-scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.9)
-
-# 训练模型
-epochs = 1000
-for epoch in range(epochs):
-    for batch_dx_dy, batch_labels in train_dataloader:
-        optimizer.zero_grad()
-        output = model(batch_dx_dy)
-        loss = criterion(output, batch_labels)
-        print(loss)
-        loss.backward()
-        optimizer.step()
-        scheduler.step()
-print('test')
-for idx, data in enumerate(test_dataloader):
-        batch_dx_dy, batch_labels = data
-        output = model(batch_dx_dy)
-        loss = criterion(output, batch_labels)
-        print(loss)
-        if idx == len(test_dataloader)-1:
-            print(batch_dx_dy[0])
-            print(output[0])
-
-model.eval()
-onnx_name = 'mouse.onnx'
-dummy = torch.randn(1, 1, 2)
-torch.onnx.export(model, dummy, onnx_name,verbose=True, input_names=['input'], output_names=['output'])
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..7721a1a
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1167 @@
+version = 1
+revision = 3
+requires-python = ">=3.12"
+resolution-markers = [
+    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'emscripten'",
+    "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'emscripten'",
+    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "contourpy"
+version = "1.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
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