notebooks/README.md

SafeVixAI — Research Notebooks 📓

These notebooks are the research and training layer of SafeVixAI. Each one processes raw data from the Hub and produces a model, index, or processed dataset used by the live application.

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⚡ One-Click Colab Setup

Run this at the top of any notebook to mount the full dataset:

# Step 1 — Clone the Hub (only run once per session)
!git clone https://huggingface.co/datasets/SafeVixAI/SafeVixAI-Dataset-Hub /content/hub

# Step 2 — Install dependencies
!pip install -q pdfplumber chromadb sentence-transformers ultralytics onnx

# Step 3 — Confirm data is accessible
import os
print("Data folders:", os.listdir("/content/hub"))

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📓 Notebook Index

#	Notebook	What it Produces	Input Data
1	YOLOv8_Pothole_Detector_Training	ONNX road damage model	pothole_training/road_damage_2025/
2	ChromaDB_RAG_Vectorstore_Build	ChromaDB index for legal RAG	legal/, medical/ PDFs
3	Accident_EDA_&_Hotspot_Generator	Blackspot seed CSV + heatmap	accidents/kaggle_india_accidents.csv
4	Roads_Data_Processing	Sampled PMGSY GeoJSON	roads/pmgsy_roads.geojson
5	Risk_Model_ONNX_Training	Risk scoring ONNX model	accidents/ + roads/

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📁 Data Paths Used by These Notebooks

All notebooks expect data at these paths after cloning the Hub:

/content/hub/
├── chatbot_service/data/
│   ├── accidents/              ← Notebooks 3, 5
│   ├── legal/                  ← Notebook 2
│   ├── medical/                ← Notebook 2
│   ├── pothole_training/       ← Notebook 1
│   └── roads/                  ← Notebook 4
├── backend/datasets/           ← Notebook 4, 5
└── frontend/public/models/     ← Output for Notebooks 1, 5

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🏃 Running Order

For a full pipeline run, execute notebooks in this order:

1 → Train pothole detector → produces ONNX model
2 → Build RAG vectorstore  → produces ChromaDB index
3 → Accident EDA           → produces blackspot_seed.csv
4 → Roads processing       → produces sampled GeoJSON
5 → Risk model             → combines accidents + roads → risk ONNX

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💾 Saving Outputs Back

After training, outputs land in /content/hub/. To persist them:

# Commit outputs back to Hub (requires HF token)
import os
os.environ["HUGGING_FACE_TOKEN"] = "your_token_here"

!cd /content/hub && git config user.email "you@example.com"
!cd /content/hub && git config user.name "SafeVixAI"
!cd /content/hub && git add . && git commit -m "chore: update trained model outputs"
!cd /content/hub && git push https://your_username:$HUGGING_FACE_TOKEN@huggingface.co/datasets/SafeVixAI/SafeVixAI-Dataset-Hub main

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Part of the SafeVixAI — IIT Madras Road Safety Hackathon 2026