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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "17e1f6f6",
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"metadata": {},
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"source": [
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"# 00 - Environment Setup\n",
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"\n",
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"## CyberForge AI - ML Pipeline Environment Configuration\n",
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"\n",
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"This notebook sets up the complete environment for the CyberForge AI machine learning pipeline.\n",
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"\n",
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"### What this notebook does:\n",
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"1. Validates Python version and system requirements\n",
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"2. Installs and pins all dependencies\n",
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"3. Configures GPU/CPU detection\n",
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"4. Sets up Gemini API connectivity\n",
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"5. Validates Web Scraper API connection\n",
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"6. Creates necessary directories\n",
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"\n",
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"### Prerequisites:\n",
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"- Python 3.10+ (3.11 recommended)\n",
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"- Access to Gemini API (API key required)\n",
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"- Access to WebScrapper.live API"
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]
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},
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{
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"cell_type": "markdown",
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"id": "33029fa4",
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"metadata": {},
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"source": [
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"## 1. System Validation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "076fa991",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"import platform\n",
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"import os\n",
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"from pathlib import Path\n",
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"\n",
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"print(\"=\" * 60)\n",
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"print(\"CYBERFORGE AI - ENVIRONMENT VALIDATION\")\n",
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"print(\"=\" * 60)\n",
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"\n",
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| 52 |
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"# Python version check\n",
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"python_version = sys.version_info\n",
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"print(f\"\\n✓ Python Version: {python_version.major}.{python_version.minor}.{python_version.micro}\")\n",
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"\n",
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"if python_version.major < 3 or (python_version.major == 3 and python_version.minor < 10):\n",
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" raise EnvironmentError(\"Python 3.10+ is required. Please upgrade your Python installation.\")\n",
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"\n",
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"# System info\n",
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"print(f\"✓ Platform: {platform.system()} {platform.release()}\")\n",
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"print(f\"✓ Architecture: {platform.machine()}\")\n",
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"print(f\"✓ Processor: {platform.processor() or 'Unknown'}\")\n",
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"\n",
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"# Memory info\n",
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"try:\n",
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" import psutil\n",
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" memory = psutil.virtual_memory()\n",
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" print(f\"✓ Available Memory: {memory.available / (1024**3):.2f} GB / {memory.total / (1024**3):.2f} GB\")\n",
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"except ImportError:\n",
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" print(\"⚠ psutil not installed - memory check skipped\")\n",
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"\n",
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"print(\"\\n\" + \"=\" * 60)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "45e95831",
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"metadata": {},
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"source": [
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"## 2. Install Dependencies"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "faa9b079",
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"metadata": {},
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"outputs": [],
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"source": [
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| 90 |
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"# Core dependencies with pinned versions for reproducibility\n",
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"DEPENDENCIES = \"\"\"\n",
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"# Core ML/AI\n",
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"numpy>=1.24.0,<2.0.0\n",
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"pandas>=2.0.0\n",
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"scikit-learn>=1.3.0\n",
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"scipy>=1.11.0\n",
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"\n",
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"# Deep Learning\n",
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"torch>=2.0.0\n",
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"transformers>=4.30.0\n",
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"\n",
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"# Gemini API\n",
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"google-generativeai>=0.3.0\n",
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"\n",
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"# Data Processing\n",
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"joblib>=1.3.0\n",
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"tqdm>=4.65.0\n",
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"\n",
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"# Feature Engineering\n",
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"tldextract>=5.0.0\n",
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"validators>=0.22.0\n",
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"ipaddress>=1.0.23\n",
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"\n",
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"# Web/API\n",
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"httpx>=0.25.0\n",
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"aiohttp>=3.8.0\n",
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"requests>=2.31.0\n",
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"\n",
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"# Hugging Face\n",
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"huggingface_hub>=0.19.0\n",
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"\n",
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"# Utilities\n",
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"python-dotenv>=1.0.0\n",
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"pyyaml>=6.0.0\n",
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"psutil>=5.9.0\n",
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"\"\"\"\n",
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"\n",
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"# Write requirements file\n",
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"requirements_path = Path(\"../requirements_notebooks.txt\")\n",
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"requirements_path.write_text(DEPENDENCIES.strip())\n",
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"print(f\"✓ Requirements written to: {requirements_path.absolute()}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7dc8c6ca",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Install dependencies\n",
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"import subprocess\n",
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"\n",
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"print(\"Installing dependencies... This may take a few minutes.\")\n",
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"result = subprocess.run(\n",
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" [sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"-r\", str(requirements_path)],\n",
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" capture_output=True,\n",
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" text=True\n",
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")\n",
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"\n",
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"if result.returncode == 0:\n",
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" print(\"✓ All dependencies installed successfully!\")\n",
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"else:\n",
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" print(f\"⚠ Installation warnings: {result.stderr[:500] if result.stderr else 'None'}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c11760cc",
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"metadata": {},
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"source": [
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"## 3. GPU/CPU Detection"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d1b948c4",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"\n",
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"print(\"=\" * 60)\n",
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"print(\"COMPUTE DEVICE DETECTION\")\n",
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"print(\"=\" * 60)\n",
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"\n",
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"# Check CUDA availability\n",
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"cuda_available = torch.cuda.is_available()\n",
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"print(f\"\\n✓ PyTorch Version: {torch.__version__}\")\n",
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"print(f\"✓ CUDA Available: {cuda_available}\")\n",
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"\n",
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"if cuda_available:\n",
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" print(f\"✓ CUDA Version: {torch.version.cuda}\")\n",
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" print(f\"✓ GPU Count: {torch.cuda.device_count()}\")\n",
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| 186 |
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" for i in range(torch.cuda.device_count()):\n",
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" props = torch.cuda.get_device_properties(i)\n",
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" print(f\" - GPU {i}: {props.name} ({props.total_memory / (1024**3):.2f} GB)\")\n",
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" DEVICE = torch.device(\"cuda\")\n",
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"else:\n",
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" print(\"⚠ No GPU detected - using CPU for training\")\n",
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" DEVICE = torch.device(\"cpu\")\n",
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"\n",
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"# Check MPS (Apple Silicon)\n",
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"if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():\n",
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" print(\"✓ Apple MPS (Metal) available\")\n",
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" DEVICE = torch.device(\"mps\")\n",
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"\n",
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"print(f\"\\n✓ Selected Device: {DEVICE}\")\n",
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"print(\"=\" * 60)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d39ddbf5",
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"metadata": {},
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"source": [
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"## 4. Environment Variables & API Configuration"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0f63a5ce",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import os\n",
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"from pathlib import Path\n",
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"\n",
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"# Load configuration from notebook_config.json first (for HF Spaces)\n",
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"config_json_path = Path(\"notebook_config.json\")\n",
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"if config_json_path.exists():\n",
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" with open(config_json_path, \"r\") as f:\n",
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" loaded_config = json.load(f)\n",
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" print(f\"✓ Loaded configuration from: {config_json_path.absolute()}\")\n",
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"else:\n",
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" loaded_config = {}\n",
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" print(f\"⚠ No notebook_config.json found, using defaults\")\n",
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"\n",
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"# Try loading .env file as fallback (for local dev)\n",
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"try:\n",
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" from dotenv import load_dotenv\n",
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" env_path = Path(\"../.env\")\n",
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" if env_path.exists():\n",
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" load_dotenv(env_path)\n",
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" print(f\"✓ Loaded environment from: {env_path.absolute()}\")\n",
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| 239 |
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"except ImportError:\n",
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" pass\n",
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"\n",
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"# Configuration class\n",
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"class Config:\n",
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" # API Keys - priority: config.json > env vars > HF secrets\n",
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" GEMINI_API_KEY = loaded_config.get(\"gemini_api_key\") or os.getenv(\"GEMINI_API_KEY\", \"AIzaSyA3HdWTLk_zJQ5P9G8Z8a8BEYSTPvLglhs\")\n",
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| 246 |
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" HUGGINGFACE_TOKEN = os.getenv(\"HUGGINGFACE_API_TOKEN\", os.getenv(\"HF_TOKEN\", \"\"))\n",
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| 247 |
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" WEBSCRAPER_API_KEY = loaded_config.get(\"webscraper_api_key\", \"sk-fd14eaa7bceb478db7afc7256e514d2b\")\n",
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| 248 |
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" WEBSCRAPER_API_URL = loaded_config.get(\"webscraper_api_url\", \"http://webscrapper.live/api/scrape\")\n",
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" \n",
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" # Gemini model\n",
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" GEMINI_MODEL = loaded_config.get(\"gemini_model\", \"gemini-2.5-flash\")\n",
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" \n",
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| 253 |
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" # Paths\n",
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| 254 |
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" BASE_DIR = Path(\"..\").resolve()\n",
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| 255 |
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" DATASETS_DIR = BASE_DIR / \"datasets\"\n",
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" MODELS_DIR = BASE_DIR / \"models\"\n",
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| 257 |
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" ARTIFACTS_DIR = BASE_DIR / \"artifacts\"\n",
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" \n",
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| 259 |
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" # ML Settings\n",
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| 260 |
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" RANDOM_STATE = loaded_config.get(\"random_state\", 42)\n",
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| 261 |
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" TEST_SIZE = loaded_config.get(\"test_size\", 0.2)\n",
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| 262 |
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" CV_FOLDS = loaded_config.get(\"cv_folds\", 5)\n",
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" \n",
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| 264 |
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" # Device\n",
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| 265 |
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" DEVICE = DEVICE\n",
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"\n",
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| 267 |
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"config = Config()\n",
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"\n",
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| 269 |
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"# Validate required API keys\n",
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| 270 |
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"print(\"\\n\" + \"=\" * 60)\n",
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| 271 |
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"print(\"API CONFIGURATION STATUS\")\n",
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| 272 |
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"print(\"=\" * 60)\n",
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| 273 |
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"print(f\"✓ Gemini API Key: {'Configured (' + config.GEMINI_API_KEY[:10] + '...)' if config.GEMINI_API_KEY else '⚠ NOT SET'}\")\n",
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| 274 |
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"print(f\"✓ Gemini Model: {config.GEMINI_MODEL}\")\n",
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| 275 |
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"print(f\"✓ HuggingFace Token: {'Configured' if config.HUGGINGFACE_TOKEN else '⚠ NOT SET (optional)'}\")\n",
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| 276 |
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"print(f\"✓ WebScraper API: Configured\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "126b5f7f",
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"metadata": {},
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"source": [
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| 284 |
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"## 5. Gemini API Connectivity Test"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "14cef3bc",
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| 291 |
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"metadata": {},
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| 292 |
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"outputs": [],
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| 293 |
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"source": [
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| 294 |
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"import google.generativeai as genai\n",
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"\n",
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| 296 |
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"def test_gemini_connection():\n",
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| 297 |
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" \"\"\"Test Gemini API connectivity\"\"\"\n",
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| 298 |
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" if not config.GEMINI_API_KEY:\n",
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| 299 |
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" return False, \"API key not configured\"\n",
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| 300 |
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" \n",
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| 301 |
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" try:\n",
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| 302 |
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" genai.configure(api_key=config.GEMINI_API_KEY)\n",
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| 303 |
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" # Use the configured model (gemini-2.5-flash)\n",
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| 304 |
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" model = genai.GenerativeModel(config.GEMINI_MODEL)\n",
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| 305 |
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" response = model.generate_content(\"Respond with only: OK\")\n",
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| 306 |
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" return True, f\"Model: {config.GEMINI_MODEL}, Response: {response.text.strip()}\"\n",
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| 307 |
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" except Exception as e:\n",
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| 308 |
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" # Fallback to gemini-1.5-flash if 2.5 not available\n",
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| 309 |
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" try:\n",
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| 310 |
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" model = genai.GenerativeModel('gemini-1.5-flash')\n",
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| 311 |
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" response = model.generate_content(\"Respond with only: OK\")\n",
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| 312 |
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" return True, f\"Model: gemini-1.5-flash (fallback), Response: {response.text.strip()}\"\n",
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| 313 |
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" except Exception as e2:\n",
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| 314 |
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" return False, str(e2)\n",
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| 315 |
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"\n",
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| 316 |
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"print(\"Testing Gemini API connection...\")\n",
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| 317 |
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"success, message = test_gemini_connection()\n",
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| 318 |
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"\n",
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| 319 |
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"if success:\n",
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| 320 |
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" print(f\"✓ Gemini API: {message}\")\n",
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| 321 |
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"else:\n",
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| 322 |
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" print(f\"⚠ Gemini API: Connection failed - {message}\")"
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]
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},
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{
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"cell_type": "markdown",
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| 327 |
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"id": "628ac121",
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| 328 |
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"metadata": {},
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| 329 |
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"source": [
|
| 330 |
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"## 6. Web Scraper API Connectivity Test"
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| 331 |
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "beb1b036",
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| 337 |
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"metadata": {},
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| 338 |
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"outputs": [],
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| 339 |
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"source": [
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| 340 |
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"import httpx\n",
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| 341 |
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"\n",
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| 342 |
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"async def test_webscraper_connection():\n",
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| 343 |
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" \"\"\"Test WebScrapper.live API connectivity\"\"\"\n",
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| 344 |
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" try:\n",
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| 345 |
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" async with httpx.AsyncClient(timeout=30.0) as client:\n",
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| 346 |
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" response = await client.post(\n",
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| 347 |
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" config.WEBSCRAPER_API_URL,\n",
|
| 348 |
-
" json={\"url\": \"https://example.com\"},\n",
|
| 349 |
-
" headers={\n",
|
| 350 |
-
" \"Content-Type\": \"application/json\",\n",
|
| 351 |
-
" \"X-API-Key\": config.WEBSCRAPER_API_KEY\n",
|
| 352 |
-
" }\n",
|
| 353 |
-
" )\n",
|
| 354 |
-
" if response.status_code == 200:\n",
|
| 355 |
-
" return True, \"Connected\"\n",
|
| 356 |
-
" else:\n",
|
| 357 |
-
" return False, f\"Status {response.status_code}\"\n",
|
| 358 |
-
" except Exception as e:\n",
|
| 359 |
-
" return False, str(e)\n",
|
| 360 |
-
"\n",
|
| 361 |
-
"print(\"Testing Web Scraper API connection...\")\n",
|
| 362 |
-
"\n",
|
| 363 |
-
"# Run async test\n",
|
| 364 |
-
"import asyncio\n",
|
| 365 |
-
"try:\n",
|
| 366 |
-
" loop = asyncio.get_event_loop()\n",
|
| 367 |
-
"except RuntimeError:\n",
|
| 368 |
-
" loop = asyncio.new_event_loop()\n",
|
| 369 |
-
" asyncio.set_event_loop(loop)\n",
|
| 370 |
-
"\n",
|
| 371 |
-
"success, message = loop.run_until_complete(test_webscraper_connection())\n",
|
| 372 |
-
"\n",
|
| 373 |
-
"if success:\n",
|
| 374 |
-
" print(f\"✓ WebScraper API: Connected successfully\")\n",
|
| 375 |
-
"else:\n",
|
| 376 |
-
" print(f\"⚠ WebScraper API: {message}\")"
|
| 377 |
-
]
|
| 378 |
-
},
|
| 379 |
-
{
|
| 380 |
-
"cell_type": "markdown",
|
| 381 |
-
"id": "75ee0f51",
|
| 382 |
-
"metadata": {},
|
| 383 |
-
"source": [
|
| 384 |
-
"## 7. Create Directory Structure"
|
| 385 |
-
]
|
| 386 |
-
},
|
| 387 |
-
{
|
| 388 |
-
"cell_type": "code",
|
| 389 |
-
"execution_count": null,
|
| 390 |
-
"id": "776236f8",
|
| 391 |
-
"metadata": {},
|
| 392 |
-
"outputs": [],
|
| 393 |
-
"source": [
|
| 394 |
-
"# Create necessary directories\n",
|
| 395 |
-
"directories = [\n",
|
| 396 |
-
" config.DATASETS_DIR,\n",
|
| 397 |
-
" config.MODELS_DIR,\n",
|
| 398 |
-
" config.ARTIFACTS_DIR,\n",
|
| 399 |
-
" config.BASE_DIR / \"logs\",\n",
|
| 400 |
-
" config.BASE_DIR / \"cache\",\n",
|
| 401 |
-
"]\n",
|
| 402 |
-
"\n",
|
| 403 |
-
"print(\"Creating directory structure...\")\n",
|
| 404 |
-
"for directory in directories:\n",
|
| 405 |
-
" directory.mkdir(parents=True, exist_ok=True)\n",
|
| 406 |
-
" print(f\" ✓ {directory}\")\n",
|
| 407 |
-
"\n",
|
| 408 |
-
"print(\"\\n✓ Directory structure ready!\")"
|
| 409 |
-
]
|
| 410 |
-
},
|
| 411 |
-
{
|
| 412 |
-
"cell_type": "markdown",
|
| 413 |
-
"id": "a6fe27eb",
|
| 414 |
-
"metadata": {},
|
| 415 |
-
"source": [
|
| 416 |
-
"## 8. Save Configuration for Other Notebooks"
|
| 417 |
-
]
|
| 418 |
-
},
|
| 419 |
-
{
|
| 420 |
-
"cell_type": "code",
|
| 421 |
-
"execution_count": null,
|
| 422 |
-
"id": "6b854bac",
|
| 423 |
-
"metadata": {},
|
| 424 |
-
"outputs": [],
|
| 425 |
-
"source": [
|
| 426 |
-
"import json\n",
|
| 427 |
-
"\n",
|
| 428 |
-
"# Export configuration for other notebooks\n",
|
| 429 |
-
"notebook_config = {\n",
|
| 430 |
-
" \"device\": str(DEVICE),\n",
|
| 431 |
-
" \"python_version\": f\"{python_version.major}.{python_version.minor}.{python_version.micro}\",\n",
|
| 432 |
-
" \"torch_version\": torch.__version__,\n",
|
| 433 |
-
" \"cuda_available\": cuda_available,\n",
|
| 434 |
-
" \"base_dir\": str(config.BASE_DIR),\n",
|
| 435 |
-
" \"datasets_dir\": str(config.DATASETS_DIR),\n",
|
| 436 |
-
" \"models_dir\": str(config.MODELS_DIR),\n",
|
| 437 |
-
" \"artifacts_dir\": str(config.ARTIFACTS_DIR),\n",
|
| 438 |
-
" \"random_state\": config.RANDOM_STATE,\n",
|
| 439 |
-
" \"test_size\": config.TEST_SIZE,\n",
|
| 440 |
-
" \"cv_folds\": config.CV_FOLDS,\n",
|
| 441 |
-
" \"gemini_configured\": bool(config.GEMINI_API_KEY),\n",
|
| 442 |
-
" \"huggingface_configured\": bool(config.HUGGINGFACE_TOKEN),\n",
|
| 443 |
-
" \"created_at\": str(pd.Timestamp.now())\n",
|
| 444 |
-
"}\n",
|
| 445 |
-
"\n",
|
| 446 |
-
"config_path = config.BASE_DIR / \"notebook_config.json\"\n",
|
| 447 |
-
"with open(config_path, \"w\") as f:\n",
|
| 448 |
-
" json.dump(notebook_config, f, indent=2)\n",
|
| 449 |
-
"\n",
|
| 450 |
-
"print(f\"✓ Configuration saved to: {config_path}\")\n",
|
| 451 |
-
"print(\"\\n\" + json.dumps(notebook_config, indent=2))"
|
| 452 |
-
]
|
| 453 |
-
},
|
| 454 |
-
{
|
| 455 |
-
"cell_type": "markdown",
|
| 456 |
-
"id": "ac7ada25",
|
| 457 |
-
"metadata": {},
|
| 458 |
-
"source": [
|
| 459 |
-
"## 9. Environment Summary"
|
| 460 |
-
]
|
| 461 |
-
},
|
| 462 |
-
{
|
| 463 |
-
"cell_type": "code",
|
| 464 |
-
"execution_count": null,
|
| 465 |
-
"id": "f409be56",
|
| 466 |
-
"metadata": {},
|
| 467 |
-
"outputs": [],
|
| 468 |
-
"source": [
|
| 469 |
-
"print(\"\\n\" + \"=\" * 60)\n",
|
| 470 |
-
"print(\"ENVIRONMENT SETUP COMPLETE\")\n",
|
| 471 |
-
"print(\"=\" * 60)\n",
|
| 472 |
-
"print(f\"\"\"\n",
|
| 473 |
-
"✅ Python: {python_version.major}.{python_version.minor}.{python_version.micro}\n",
|
| 474 |
-
"✅ Device: {DEVICE}\n",
|
| 475 |
-
"✅ PyTorch: {torch.__version__}\n",
|
| 476 |
-
"✅ Gemini API: {'Ready' if config.GEMINI_API_KEY else 'Not configured'}\n",
|
| 477 |
-
"✅ HuggingFace: {'Ready' if config.HUGGINGFACE_TOKEN else 'Not configured'}\n",
|
| 478 |
-
"✅ WebScraper API: Ready\n",
|
| 479 |
-
"✅ Directories: Created\n",
|
| 480 |
-
"\n",
|
| 481 |
-
"You can now proceed to the next notebook:\n",
|
| 482 |
-
" → 01_data_acquisition.ipynb\n",
|
| 483 |
-
"\"\"\")\n",
|
| 484 |
-
"print(\"=\" * 60)"
|
| 485 |
-
]
|
| 486 |
-
}
|
| 487 |
-
],
|
| 488 |
-
"metadata": {
|
| 489 |
-
"language_info": {
|
| 490 |
-
"name": "python"
|
| 491 |
-
}
|
| 492 |
-
},
|
| 493 |
-
"nbformat": 4,
|
| 494 |
-
"nbformat_minor": 5
|
| 495 |
-
}
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