chore: remove helper scripts

notebooks/cell_dump.txt

@@ -1,40 +0,0 @@
-=== Accident_EDA_&_Hotspot_Generator_chatbot_service_data_accidents_3.ipynb ===
-Code Cell 0: # Cell 0 — Upload Dataset | from google.colab import files | print("▶ UPLOAD your accidents CSV dataset NOW:")
-Code Cell 1: # Cell 1 — Read baseline datasets | import pandas as pd, json | df = pd.read_csv(filename)
-Code Cell 2: # Cell 2 — Generate Summary JSON | state_col = next((c for c in df.columns if 'state' in c), None) | acc_col = next((c for c in df.columns if 'accident' in c), df.columns[2])
-Code Cell 3: # Cell 3 — Process raw GPS tags into hotspot clusters | lat_col = next((c for c in df.columns if 'lat' in c), None) | lon_col = next((c for c in df.columns if 'lon' in c or 'lng' in c), None)
-
-=== ChromaDB_RAG_Vectorstore_Build_chatbot_service_data_chroma_db_2.ipynb ===
-Code Cell 0: import time | from IPython.display import Javascript | display(Javascript('''
-Code Cell 1: # Cell 1 — Install RAG tools | !pip install chromadb==0.5.3 sentence-transformers pypdf pdfplumber langchain langchain-community pandas "numpy<2.0.0" -q | print("✅ Installations complete")
-Code Cell 2: # Cell 2 — Initialize Embedder Models | # We use TWO embedders: English for legal text, Multilingual for standard files | from sentence_transformers import SentenceTransformer
-Code Cell 3: # Cell 3 — Initialize Chroma Collections | import chromadb | client = chromadb.PersistentClient(path='/content/chroma_db')
-Code Cell 4: # Cell 3.5 — Create Folders and Upload Files | import os, shutil | from google.colab import files
-Code Cell 5: # Cell 4 — Helper functions for Document Chunking | from langchain_community.document_loaders import PyPDFLoader | from langchain_text_splitters import RecursiveCharacterTextSplitter
-Code Cell 6: # Cell 5 — Parse & Embed strictly Legal PDFs | for pdf in glob.glob('/content/rag_data/legal/*.pdf'): | add_pdf(pdf, legal_col, en_embedder, os.path.basename(pdf))
-Code Cell 7: # Cell 6 — Parse & Embed Multilingual CSV/Rules | if os.path.exists('/content/rag_data/violations_seed.csv'): | print("Violations file found, embedding into general_col...")
-Code Cell 8: # Cell 7 — Download Database zip | import shutil | from google.colab import files
-Code Cell 9: print("🔍 Testing Vector Database Retrieval...") | # Test 1: Ask the violations (general_col) about helmets | print("\n--- Testing Violations Database ---")
-
-=== Risk_Model_ONNX_Training_frontend_public_models_5.ipynb ===
-Code Cell 0: # Cell 1 — Install ML Toolkit | !pip install scikit-learn skl2onnx pandas numpy -q | print("✅ Toolkit installed")
-Code Cell 1: # Cell 2 — Build synthetic data matching sensor ingestion structure | import pandas as pd, numpy as np | # Feature: NH=0, SH=1, MDR=2, VR=3
-Code Cell 2: # Cell 3 — Train the GradientBoosting Classifier | from sklearn.ensemble import GradientBoostingClassifier | model = GradientBoostingClassifier(n_estimators=50, max_depth=4)
-Code Cell 3: # Cell 4 — Package as ONNX and Export | from skl2onnx import convert_sklearn | from skl2onnx.common.data_types import FloatTensorType
-
-=== Roads_Data_Processing_backend_data_4.ipynb ===
-Code Cell 0: # Cell 1 — Toll Plazas Lite | import pandas as pd, json | from google.colab import files
-
-=== YOLOv8_Pothole_Detector_Training_frontend_public_models_1.ipynb ===
-Code Cell 0: import time | from IPython.display import Javascript | display(Javascript('''
-Code Cell 1: !pip install ultralytics roboflow -q | !pip install onnx onnxruntime -q
-Code Cell 2: import os | from ultralytics import YOLO | print("✅ Ultralytics ready, CUDA:", os.popen("nvidia-smi --query-gpu=name --format=csv,noheader").read().strip())
-Code Cell 3: from google.colab import files | uploaded = files.upload()
-Code Cell 4: # Cell 3 — Extract the main zipped dataset | import zipfile | with zipfile.ZipFile('/content/archive.zip', 'r') as z:
-Code Cell 5: # Cell 4 — Setup master directory structure for merged datasets | import os, shutil | from pathlib import Path
-Code Cell 6: # Cell 5 — Copy all images and labels into the merged folder (Bulletproof Search) | # List of directories containing datasets (Assuming they are available in /content/) | DATASETS = [
-Code Cell 7: # Cell 6 — Create data.yaml mapping to our 3 classes | data_yaml = """ | train: /content/merged/train/images
-Code Cell 8: # Cell 7 — Train (45-60 min on T4 GPU) | model = YOLO('yolov8n.pt') | results = model.train(
-Code Cell 9: # Cell 8 — Export ONNX | best_model = YOLO('/content/runs/detect/pothole_v1/weights/best.pt') | best_model.export(format='onnx', imgsz=640, opset=12, simplify=True)
-Code Cell 10: # Cell 9 — Download the weights | import shutil | from google.colab import files
-

notebooks/check_cells.py

@@ -1,17 +0,0 @@
-import json, glob
-
-with open('cell_dump.txt', 'w', encoding='utf-8') as out:
-    for nb_path in glob.glob('*.ipynb'):
-        out.write(f'=== {nb_path} ===\n')
-        with open(nb_path, 'r', encoding='utf-8') as f:
-            nb = json.load(f)
-        c_idx = 0
-        for cell in nb['cells']:
-            if cell['cell_type'] == 'code':
-                lines = cell['source']
-                # Get first 3 nonempty lines
-                non_empty = [l.strip() for l in lines if l.strip()]
-                prefix = ' | '.join(non_empty[:3])
-                out.write(f'Code Cell {c_idx}: {prefix}\n')
-                c_idx += 1
-        out.write('\n')

notebooks/smart_inject.py

@@ -1,132 +0,0 @@
-import json
-
-def md(lines):
-    return {"cell_type": "markdown", "metadata": {}, "source": lines}
-
-# Definitions of Markdowns
-nb1_mds = [
-    md(["# 🚗 YOLOv8 Pothole & Road Damage Detector — Training Pipeline\n", "\n", "**Part of:** SafeVisionAI · IIT Madras Road Safety Hackathon 2026\n", "**Output:** `pothole_v1/weights/best.onnx` → deployed to `frontend/public/models/`\n", "\n", "This notebook trains a YOLOv8n object detection model to identify **potholes, cracks, and manholes** on Indian roads.\n", "The trained model is exported to ONNX format for in-browser inference via `onnxruntime-web`.\n", "\n", "---\n", "### 📋 Pipeline Overview\n", "| Step | What happens |\n", "|------|-------------|\n", "| 1 | Install Ultralytics + ONNX and verify GPU |\n", "| 2 | Upload `archive.zip` dataset (road_damage_2025) |\n", "| 3 | Extract the zip into `/content/pothole_data/` |\n", "| 4 | Create master merged directory structure |\n", "| 5 | Merge all dataset images + labels into one folder |\n", "| 6 | Write `data.yaml` for 3-class detection |\n", "| 7 | Train YOLOv8n for 50 epochs on T4 GPU (~45 min) |\n", "| 8 | Export best weights to ONNX |\n", "\n", "> ⚠️ **Requires GPU runtime:** Runtime → Change runtime type → T4 GPU"]),
-    md(["## 🔧 Step 1 — Environment Setup\n", "\n", "Keeps the Colab session alive during long training runs and installs all required libraries.\n", "- `ultralytics` — YOLOv8 training framework by Ultralytics\n", "- `roboflow` — dataset management (optional augmentation)\n", "- `onnx` + `onnxruntime` — ONNX export and validation"]),
-    md(["## ✅ Step 2 — Verify GPU & Import YOLO\n", "\n", "Confirms that the Tesla T4 GPU is available and the Ultralytics framework is ready."]),
-    md(["## 📁 Step 3 — Upload Dataset\n", "\n", "Upload the `archive.zip` file from the Hub:\n", "```\n", "chatbot_service/data/pothole_training/road_damage_2025/archive.zip\n", "```\n", "> 📂 This contains ~2,009 labeled road damage images in YOLO format (potholes, cracks, manholes)."]),
-    md(["## 📦 Step 4 — Extract Dataset Archive\n", "\n", "Extracts `archive.zip` into `/content/pothole_data/`.\n", "This creates the raw YOLO-format dataset structure: `images/` and `labels/` subfolders."]),
-    md(["## 🗂️ Step 5 — Create Master Directory Structure\n", "\n", "Creates a unified `merged/` folder with separate `train/` and `valid/` splits.\n", "This allows merging images from multiple datasets (sachin_patel, andrew_mvd) if available."]),
-    md(["## 🔀 Step 6 — Merge Datasets (Bulletproof Search)\n", "\n", "Recursively searches all dataset folders for `.jpg` images and `.txt` YOLO labels,\n", "then copies them all into the master `merged/train/` directory.\n", "\n", "> ✅ Result: **2,009 training images** merged from road_damage_2025."]),
-    md(["## 📝 Step 7 — Write `data.yaml`\n", "\n", "Creates the YOLO dataset configuration file defining:\n", "- 3 detection classes: `['pothole', 'crack', 'manhole']`\n", "- Train and validation paths\n", "\n", "The `nc: 3` setting overrides the default YOLOv8 ImageNet classes."]),
-    md(["## 🚀 Step 8 — Train YOLOv8n (50 Epochs) & Export ONNX\n", "\n", "Trains YOLOv8 nano on the merged dataset using these hyperparameters:\n", "\n", "| Parameter | Value | Reason |\n", "|-----------|-------|--------|\n", "| `model` | yolov8n.pt | Smallest model — runs well in browser via ONNX |\n", "| `epochs` | 50 | Balanced between accuracy and training time |\n", "| `imgsz` | 640 | Standard YOLO input resolution |\n", "| `batch` | 16 | Fits T4 14GB VRAM |\n", "| `device` | 0 (GPU) | CUDA training |\n", "\n", "> ⏱️ Expected training time: **~45 minutes** on Tesla T4\n", "> 📈 Final mAP@50: ~**0.75+** after 50 epochs\n","\n**It will then export to ONNX and download the final model.**"])
-]
-
-nb2_mds = [
-    md(["# 🧠 ChromaDB RAG Vectorstore — Legal & Medical PDF Ingestion\n", "\n", "**Part of:** SafeVisionAI · IIT Madras Road Safety Hackathon 2026\n", "**Output:** `chroma_db/` directory → deployed to `chatbot_service/data/chroma_db/`\n", "\n", "This notebook builds the **Retrieval-Augmented Generation (RAG)** knowledge base for the SafeVisionAI chatbot.\n", "It ingests Indian legal documents (Motor Vehicles Act, MoRTH circulars) and first-aid medical PDFs,\n", "chunks them, embeds them using `sentence-transformers`, and stores them in a **ChromaDB** vector store.\n", "\n", "---\n", "### 🗂️ Source Documents\n", "| Category | Files | Source |\n", "|----------|-------|--------|\n", "| Legal | Motor Vehicles Act 2019, MoRTH 2022 | `download_legal_pdfs.py` |\n", "| Medical | First Aid guides, Emergency protocols | `download_legal_pdfs.py` |\n", "\n", "### 🔄 Pipeline\n", "```\n", "PDFs → pdfplumber chunks → sentence-transformer embeddings → ChromaDB index\n", "```\n", "\n", "> 💡 The resulting `chroma_db/` is what the chatbot queries at runtime for grounded answers."]),
-    md(["## 🔧 Step 1 — Install Dependencies\n", "\n", "Installs the full RAG stack:\n", "- `chromadb` — local vector database for semantic search\n", "- `sentence-transformers` — `all-MiniLM-L6-v2` model for text embeddings\n", "- `pdfplumber` — PDF text extraction with page layout awareness\n", "- `langchain` — document chunking utilities"]),
-    md(["## 📂 Step 2 — Upload PDF Documents\n", "\n", "Upload all legal and medical PDFs from:\n", "```\n", "chatbot_service/data/legal/\n", "chatbot_service/data/medical/\n", "```\n", "\n", "> 📄 Expected PDFs: Motor_Vehicles_Act.pdf, MoRTH_2022_Report.pdf, first_aid_guide.pdf, etc."]),
-    md(["## ✂️ Step 3 — Extract & Chunk PDF Text\n", "\n", "Uses `pdfplumber` to extract text from each PDF page,\n", "then splits into fixed-size chunks (512 tokens) with 50-token overlap.\n", "\n", "Chunking ensures the embedding model sees coherent, context-rich passages\n", "rather than arbitrarily cut sentences."]),
-    md(["## 🔢 Step 4 — Generate Embeddings\n", "\n", "Uses the `all-MiniLM-L6-v2` sentence-transformer model to convert each text chunk\n", "into a 384-dimensional embedding vector.\n", "\n", "| Model | Dimensions | Speed | Quality |\n", "|-------|-----------|-------|---------|\n", "| all-MiniLM-L6-v2 | 384 | Fast | Good for semantic QA |"]),
-    md(["## 💾 Step 5 — Build & Persist ChromaDB Index\n", "\n", "Creates a persistent ChromaDB collection and upserts all embedded chunks.\n", "The resulting `chroma_db/` folder contains the SQLite + vector index files.\n", "\n", "> 📦 Output size: ~50-100MB depending on number of PDFs ingested."]),
-    md(["## 📥 Step 6 — Download ChromaDB\n", "\n", "Zips the `chroma_db/` directory and downloads it for deployment.\n", "Place the extracted folder at: `chatbot_service/data/chroma_db/`\n", "\n", "The chatbot service auto-loads this at startup — no rebuild needed."]),
-]
-
-nb3_mds = [
-    md(["# 🗺️ Accident EDA & Blackspot Hotspot Generator\n", "\n", "**Part of:** SafeVisionAI · IIT Madras Road Safety Hackathon 2026\n", "**Output:** `accidents_summary.json` + `blackspot_seed.csv` → seeded to the backend database\n", "\n", "This notebook processes the **Kaggle India Road Accidents dataset** (1M+ rows)\n", "to produce two key intelligence artifacts:\n", "\n", "1. **`accidents_summary.json`** — National total + top 10 states by accident count\n", "2. **`blackspot_seed.csv`** — GPS clusters with accident counts for map hotspot visualization\n", "\n", "---\n", "### 📊 Dataset\n", "- **Source:** Kaggle India Road Accidents dataset\n", "- **Size:** ~1,048,575 rows · 30+ columns\n", "- **Acquired via:** `setup_kaggle.ps1` + `scripts/data/seed_blackspots.py`\n", "\n", "### 🔄 Pipeline\n", "```\n", "Raw CSV → Normalize columns → State summary → GPS cluster → blackspot_seed.csv\n", "```"]),
-    md(["## 📂 Step 0 — Upload Accidents Dataset\n", "\n", "Upload `kaggle_india_accidents.csv` from:\n", "```\n", "chatbot_service/data/accidents/kaggle_india_accidents.csv\n", "```\n", "\n", "> ⚠️ This file is ~450MB. The Hub stores it via Git LFS."]),
-    md(["## 📖 Step 1 — Load & Normalize Dataset\n", "\n", "Reads the CSV and normalizes all column names to lowercase snake_case.\n", "Result: **1,048,575 rows** of accident records across Indian states.\n", "\n", "> 💡 The mixed-type DtypeWarning is expected for columns with mixed numeric/string data."]),
-    md(["## 📊 Step 2 — Generate National Summary JSON\n", "\n", "Auto-detects the `state` and `accident` columns using flexible column name matching,\n", "then computes:\n", "- **National total** — sum of all accident counts\n", "- **Top 10 states** — ranked by accident volume\n", "\n", "Exports `accidents_summary.json` — used by the chatbot to answer national stats queries."]),
-    md(["## 📍 Step 3 — Generate GPS Blackspot Clusters\n", "\n", "Groups accident records by rounded GPS coordinates (2 decimal places ≈ ~1km²),\n", "then counts accidents per grid cell.\n", "\n", "Result: **4,134 blackspot clusters** exported as `blackspot_seed.csv`\n", "→ This CSV is loaded by `backend/scripts/app/seed_emergency.py` to populate the PostGIS accident layer.\n", "\n", "| Column | Description |\n", "|--------|-------------|\n", "| `lat_r` | Rounded latitude (±0.01°) |\n", "| `lon_r` | Rounded longitude (±0.01°) |\n", "| `accident_count` | Number of accidents in this 1km² cell |"])
-]
-
-nb4_mds = [
-    md(["# 🛣️ Roads & Toll Plaza Data Processing\n", "\n", "**Part of:** SafeVisionAI · IIT Madras Road Safety Hackathon 2026\n", "**Output:** `toll_plazas_lite.json` → deployed to `backend/data/roads/`\n", "\n", "This notebook processes the **NHAI Toll Plaza dataset** to produce a lightweight JSON\n", "suitable for the SafeVisionAI backend API and offline PWA map layer.\n", "\n", "---\n", "### 📊 Dataset\n", "- **Source:** NHAI Open Data / custom toll_plazas.csv\n", "- **Fields:** Name, NH Number, Latitude, Longitude\n", "- **Coverage:** All operational toll plazas on National Highways\n", "\n", "### 🔄 Pipeline\n", "```\n", "toll_plazas.csv → Select key columns → Rename headers → Export toll_plazas_lite.json\n", "```"]),
-    md(["## 📦 Step 1 — Upload & Process Toll Plaza CSV\n", "\n", "Upload `toll_plazas.csv` from:\n", "```\n", "backend/data/roads/toll_plazas.csv\n", "```\n", "\n", "The processing pipeline:\n", "1. Reads the CSV with `pandas`\n", "2. Selects only 4 essential columns: `name, id, lat, lon`\n", "3. Drops rows with missing coordinates\n", "4. Renames to human-readable headers\n", "5. Exports as `toll_plazas_lite.json`\n", "\n", "The resulting JSON is consumed by the backend `/api/roads/tolls` endpoint\n", "and the offline PWA map layer for toll overlay rendering.\n", "\n", "> 📦 Output size: ~65KB (vs 2MB+ raw CSV)"])
-]
-
-nb5_mds = [
-    md(["# ⚡ Road Risk Scoring Model — ONNX Training Pipeline\n", "\n", "**Part of:** SafeVisionAI · IIT Madras Road Safety Hackathon 2026\n", "**Output:** `risk_model.onnx` (~21KB) → deployed to `frontend/public/models/`\n", "\n", "This notebook trains a **GradientBoosting classifier** to predict real-time road risk\n", "and exports it as ONNX for **in-browser inference** — no server call needed.\n", "\n", "---\n", "### 🧠 Model Architecture\n", "| Component | Details |\n", "|-----------|--------|\n", "| Algorithm | GradientBoostingClassifier |\n", "| Input features | 5 (road type, hour, rain, speed limit, prev accidents) |\n", "| Output | Binary: `high_risk` (0 or 1) |\n", "| Export | ONNX via `skl2onnx` |\n", "| Size | ~21KB — loads in milliseconds in browser |\n", "\n", "### 🔄 Pipeline\n", "```\n", "Synthetic data generation → GBM training → ONNX conversion → Download\n", "```\n", "\n", "> 💡 The model runs entirely client-side in the SafeVisionAI PWA using `onnxruntime-web`."]),
-    md(["## 🔧 Step 1 — Install ML Toolkit\n", "\n", "Installs the minimum stack needed for training and ONNX export:\n", "- `scikit-learn` — GradientBoostingClassifier\n", "- `skl2onnx` — converts sklearn models to ONNX format\n", "- `pandas` + `numpy` — data generation and manipulation"]),
-    md(["## 🏗️ Step 2 — Build Synthetic Training Data\n", "\n", "Generates 5,000 synthetic road sensor records matching the live app's data structure:\n", "\n", "| Feature | Values | Description |\n", "|---------|--------|-------------|\n", "| `road_type` | 0-3 | NH=0, SH=1, MDR=2, VR=3 |\n", "| `hour` | 0-23 | Hour of day |\n", "| `is_rain` | 0/1 | Weather condition |\n", "| `speed_limit` | 40/60/80/100 | Posted speed (km/h) |\n", "| `prev_accidents` | Poisson(2) | Historical accident count |\n", "\n", "**Label logic:** `high_risk = 1` when: Night hours (10pm–4am) + National/State Highway + Raining\n", "This reflects real-world patterns from the India accident dataset."]),
-    md(["## 🎯 Step 3 — Train GradientBoosting Classifier\n", "\n", "Trains a GBM with 50 estimators and max depth 4:\n", "- **Fast:** <10 seconds on CPU\n", "- **Accurate:** Handles non-linear risk patterns well\n", "- **Tiny:** Converts to 21KB ONNX — ideal for edge/PWA deployment"]),
-    md(["## 📦 Step 4 — Export to ONNX & Download\n", "\n", "Converts the trained sklearn model to ONNX format using `skl2onnx`:\n", "- **Input:** `FloatTensorType([None, 5])` — batch of 5-feature vectors\n", "- **Output:** Risk probability + binary class label\n", "\n", "Download `risk_model.onnx` and place at:\n", "```\n", "frontend/public/models/risk_model.onnx\n", "```\n", "\n", "The Next.js PWA loads this at startup and runs inference on each map segment click.\n", "\n", "> ✅ Final output: **~21KB** ONNX model — ready for browser deployment"])
-]
-
-# Mapping structure:
-# nb_path: { code_cell_index: list of markdowns to insert BEFORE it }
-config = {
-    'YOLOv8_Pothole_Detector_Training_frontend_public_models_1.ipynb': {
-        0: [nb1_mds[0]], # Title
-        1: [nb1_mds[1]], # Step 1 (pip install)
-        2: [nb1_mds[2]], # Step 2 (import os)
-        3: [nb1_mds[3]], # Step 3 (upload)
-        4: [nb1_mds[4]], # Step 4 (extract)
-        5: [nb1_mds[5]], # Step 5 (master directory)
-        6: [nb1_mds[6]], # Step 6 (merge datasets)
-        7: [nb1_mds[7]], # Step 7 (data.yaml)
-        8: [nb1_mds[8]]  # Step 8 (train & export)
-    },
-    'ChromaDB_RAG_Vectorstore_Build_chatbot_service_data_chroma_db_2.ipynb': {
-        0: [nb2_mds[0]], # Title
-        1: [nb2_mds[1]], # Step 1: Install tools
-        4: [nb2_mds[2]], # Step 2: Upload Files (cell 3.5 in code)
-        5: [nb2_mds[3]], # Step 3: Helper functions chunking (cell 4 in code)
-        6: [nb2_mds[4]], # Step 4: Parse & Embed legal (cell 5 in code)
-        7: [nb2_mds[5]], # Step 5: Build & persist index (cell 6 in code)
-        8: [nb2_mds[6]]  # Step 6: Download Database
-    },
-    'Accident_EDA_&_Hotspot_Generator_chatbot_service_data_accidents_3.ipynb': {
-        0: [nb3_mds[0], nb3_mds[1]], # Title + Step 0: Upload
-        1: [nb3_mds[2]], # Step 1: Load
-        2: [nb3_mds[3]], # Step 2: Summary
-        3: [nb3_mds[4]]  # Step 3: GPS
-    },
-    'Roads_Data_Processing_backend_data_4.ipynb': {
-        0: [nb4_mds[0], nb4_mds[1]] # Title + Step 1
-    },
-    'Risk_Model_ONNX_Training_frontend_public_models_5.ipynb': {
-        0: [nb5_mds[0], nb5_mds[1]], # Title + Install ML
-        1: [nb5_mds[2]], # Step 2 Build synthetic Data
-        2: [nb5_mds[3]], # Step 3 Train GradientBoosting
-        3: [nb5_mds[4]]  # Step 4 Export ONNX
-    }
-}
-
-import os
-def reset_and_inject():
-    # Fix both repositories
-    repos = [
-        r"C:\Hackathons\IITM\SafeVisionAI-Dataset-Hub\notebooks",
-        r"C:\Hackathons\IITM\SafeVisionAI\notebooks"
-    ]
-    
-    for repo in repos:
-        print(f"\nProcessing {repo}")
-        for nb_name, mapping in config.items():
-            path = os.path.join(repo, nb_name)
-            if not os.path.exists(path):
-                print(f"[SKIP] {nb_name}")
-                continue
-            
-            with open(path, 'r', encoding='utf-8') as f:
-                nb = json.load(f)
-            
-            # Start fresh with only code cells (wipe existing markdowns)
-            code_cells = [c for c in nb['cells'] if c['cell_type'] == 'code']
-            
-            new_cells = []
-            for idx, c_cell in enumerate(code_cells):
-                # Before adding the code cell, add any markdowns mapped to this index
-                if idx in mapping:
-                    for md_obj in mapping[idx]:
-                        new_cells.append(md_obj)
-                
-                new_cells.append(c_cell)
-            
-            # Add any remaining markdowns mapped to an index purely at the end (greater than code count)
-            # Not needed for our current dict but safe.
-            
-            nb['cells'] = new_cells
-            with open(path, 'w', encoding='utf-8') as f:
-                json.dump(nb, f, indent=2, ensure_ascii=False)
-            
-            print(f"[OK] {nb_name} -> now has {len(new_cells)} total cells")
-
-if __name__ == "__main__":
-    reset_and_inject()
-    print("\n[DONE] All cells perfectly mapped, zero duplicates.")