Create class_demo.ipynb

src/ac_training_lab/bambu_a1mini/class_demo.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3D Print Quality Measurement Demo\n",
+    "\n",
+    "This notebook demonstrates how to measure and evaluate 3D print quality using our measurement system.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Input Parameters\n",
+    "- **Nozzle Temperature** (180-250°C): Temperature of the printing nozzle\n",
+    "- **Print Speed** (20-150 mm/s): Movement speed of the print head\n",
+    "- **Layer Height** (0.1-0.4 mm): Height of each printed layer\n",
+    "- **Flow Rate** (90-110%): Material extrusion rate\n",
+    "- **Retraction Distance** (0-10 mm): Length of filament retraction\n",
+    "- **Fan Speed** (0-100%): Cooling fan speed\n",
+    "\n",
+    "### Output Measurements\n",
+    "- **Print Quality Score**: Measures surface quality, edge precision, and defect analysis\n",
+    "- **Speed Score**: Evaluates time efficiency of the print\n",
+    "- **Material Efficiency Score**: Measures material usage optimization\n",
+    "- **Total Performance Score**: Combined evaluation of all metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install required packages\n",
+    "!pip install requests pillow numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "\n",
+    "# Hugging Face Space API endpoint\n",
+    "API_URL = \"https://your-space-url/api\"  # Will be provided by instructor\n",
+    "\n",
+    "def evaluate_print(image_path, parameters):\n",
+    "    \"\"\"Evaluate print quality using the measurement system\n",
+    "    \n",
+    "    Args:\n",
+    "        image_path (str): Path to the print image\n",
+    "        parameters (dict): Printing parameters\n",
+    "        \n",
+    "    Returns:\n",
+    "        dict: Measurement scores\n",
+    "    \"\"\"\n",
+    "    # Load and prepare image\n",
+    "    with open(image_path, 'rb') as f:\n",
+    "        image_data = f.read()\n",
+    "    \n",
+    "    # Prepare request data\n",
+    "    files = {'image': image_data}\n",
+    "    data = {'parameters': json.dumps(parameters)}\n",
+    "    \n",
+    "    # Send request to API\n",
+    "    response = requests.post(f\"{API_URL}/evaluate\", files=files, data=data)\n",
+    "    \n",
+    "    return response.json()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example Usage\n",
+    "\n",
+    "Let's try evaluating a print with some example parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example parameters\n",
+    "test_parameters = {\n",
+    "    'nozzle_temp': 200,\n",
+    "    'print_speed': 60,\n",
+    "    'layer_height': 0.2,\n",
+    "    'flow_rate': 100,\n",
+    "    'retraction_distance': 5,\n",
+    "    'fan_speed': 100\n",
+    "}\n",
+    "\n",
+    "# Evaluate print (replace with your image path)\n",
+    "results = evaluate_print('your_print_image.jpg', test_parameters)\n",
+    "\n",
+    "# Display results\n",
+    "print(\"Measurement Results:\")\n",
+    "print(f\"Print Quality Score: {results['quality']:.4f}\")\n",
+    "print(f\"Speed Score: {results['speed']:.4f}\")\n",
+    "print(f\"Material Efficiency Score: {results['material']:.4f}\")\n",
+    "print(f\"Total Performance Score: {results['total']:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise\n",
+    "\n",
+    "Now it's your turn! Try different parameter combinations and analyze how they affect the measurement scores.\n",
+    "\n",
+    "1. What parameters seem to have the biggest impact on print quality?\n",
+    "2. Can you find parameters that give good scores across all metrics?\n",
+    "3. What happens when you use extreme parameter values?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your experiments here\n",
+    "your_parameters = {\n",
+    "    'nozzle_temp': 0,  # Replace with your value\n",
+    "    'print_speed': 0,  # Replace with your value\n",
+    "    'layer_height': 0,  # Replace with your value\n",
+    "    'flow_rate': 0,  # Replace with your value\n",
+    "    'retraction_distance': 0,  # Replace with your value\n",
+    "    'fan_speed': 0  # Replace with your value\n",
+    "}\n",
+    "\n",
+    "# Run your evaluation\n",
+    "# results = evaluate_print('your_image.jpg', your_parameters)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ }
+}