💡 Smart India Hackathon (SIH) 2026 Problem Statement Research Explorer & Dataset

Interactive exploration portal, multi-tag search, automated data cleaning pipeline, and comprehensive JSON/CSV dataset for all 226 Problem Statements released for the Smart India Hackathon 2026 (SIH 2026).

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🌐 Live Research Portal

Access the live interactive application at: 👉 https://sih26ps.vercel.app

• Decoupled Search Bar: Instantly filter across keywords, title, description, ministry, or specific problem statement IDs (e.g. SIH26001).
• Multi-Select Technology Tags: Combine tags like AI / ML, Computer Vision, NLP / LLM, GIS / Satellite, IoT / Sensors, Blockchain, Robotics & UAV, Cybersecurity, and more.
• Spec-Compliant Markdown: Formatted via marked.js and DOMPurify with proper sub-bullet nesting and typography.
• 1-Click Markdown Copy: Instant clipboard export of structured problem statements in GitHub-Flavored Markdown.
• Clean Dataset Export: Export filtered or selected problem statements in clean JSON and CSV without scraper bloat.

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📊 Dataset Overview & Statistics

• Source URL: https://sih.gov.in/sih2026PS
• Total Problem Statements: 226 (SIH26001 – SIH26226)
• Primary Category Breakdown:
  • Software: 172
  • Hardware: 54
• Top Themes:
  • Miscellaneous (38)
  • Smart Automation (31)
  • Disaster Management (29)
  • Blockchain & Cybersecurity (22)
  • MedTech / BioTech / HealthTech (14)
  • Smart Education (13)
  • Agriculture, FoodTech & Rural Development (12)
  • Space Technology (11)
  • Robotics and Drones (10)
  • Transportation & Logistics (8)
• Top Ministries / Organizations:
  • AICTE (34)
  • Ministry of Earth Sciences / MoES (30)
  • National Technical Research Organisation / NTRO (23)
  • ISRO (11)
  • Ministry of Home Affairs (11)
  • Ministry of Rural Development (10)
  • Ministry of Consumer Affairs, Food & Public Distribution (10)
  • Government of Maharashtra (9)
  • Ministry of Social Justice and Empowerment / MoSJE (8)
  • DRDO (7)

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📁 Repository Structure

├── index.html                        # Modern Flat Design web portal (HTML5 + JSON-LD Schema)
├── style.css                         # High-contrast CSS system tokens & responsive layouts
├── app.js                            # Search, multi-tag filter engine, and modal controller
├── sih2026_problem_statements.json   # Cleaned & normalized JSON dataset (226 statements)
├── sih2026_problem_statements.csv    # Cleaned CSV export for data analysis & Excel
├── scrape_sih.py                     # Canonical scraper & data normalizer
├── logo.png                          # Official SIH logo
├── favicon.ico / favicon-*.png       # Multi-resolution favicons
├── package.json                      # Project metadata
└── README.md                         # Documentation

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🚀 Quick Start

1. Launch the Web Explorer Locally

Serve the repository with any local HTTP server:

# Python 3
python3 -m http.server 8080

# Or with Node.js
npx serve .

Open http://localhost:8080 in your browser.

2. Run the Data Pipeline & Cleaner

# Clean & normalize the local dataset
python3 scrape_sih.py --clean-only

# Or fetch and parse directly from the official portal
python3 scrape_sih.py

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🔍 JSON Schema Format

Each problem statement entry in sih2026_problem_statements.json:

{
  "id": "SIH26001",
  "numeric_id": 26001,
  "serial_no": 1,
  "title": "AI-Based early warning and landslide Risk Monitoring System in NER",
  "organization": "Ministry of Development of North Eastern Region (MDoNER)",
  "department": "Ministry of Development of North Eastern Region (MDoNER)",
  "category": "Software",
  "theme": "Disaster Management",
  "submitted_ideas": {
    "count": 0,
    "capacity": 500,
    "raw": "0/500"
  },
  "deadline": "20 September 2026",
  "youtube_link": null,
  "dataset_info": null,
  "contact_info": null,
  "external_links": [],
  "sections": {
    "background": {
      "title": "Background",
      "content": "The North Eastern Region (NER) frequently faces landslides..."
    },
    "description": {
      "title": "Description",
      "content": "This problem statement proposes the development of..."
    },
    "expected_solution": {
      "title": "Expected Solution",
      "content": "A scalable AI-based software platform with..."
    }
  },
  "description": "**Background:**\n\nThe North Eastern Region (NER) frequently faces landslides...",
  "modal_id": "ViewProblemStatement26001",
  "web_url": "https://sih.gov.in/sih2026PS#ViewProblemStatement26001"
}

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📦 Clean Export Schema

When exporting selected or filtered statements via the Download / Export menu, the JSON format produces a clean schema without scraper bloat:

{
  "id": "SIH26158",
  "title": "Single-Pass Drone Video to Accurate 3D Model Generation System",
  "category": "Software",
  "theme": "Robotics and Drones",
  "organization": "National Technical Research Organisation (NTRO)",
  "department": "National Technical Research Organisation (NTRO)",
  "submissions": "0/500",
  "deadline": "20 September 2026",
  "official_url": "https://sih.gov.in/sih2026PS#ViewProblemStatement26158",
  "description": "...",
  "dataset_info": "...",
  "external_links": [],
  "sections": { ... }
}

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🐍 Loading the Dataset in Python

import json
import pandas as pd

# Load JSON
with open('sih2026_problem_statements.json', 'r', encoding='utf-8') as f:
    data = json.load(f)

problem_statements = data['problem_statements']
print(f"Loaded {len(problem_statements)} problem statements")

# Convert to Pandas DataFrame
df = pd.DataFrame(problem_statements)

# Filter for AI/ML related statements
ai_statements = [
    p for p in problem_statements 
    if 'AI' in p['title'] or 'machine learning' in p['description'].lower()
]
print(f"Found {len(ai_statements)} AI/ML statements")

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🎨 Web Explorer Features

• Flat Design System: High-contrast, card-based interface with geometric typography (Outfit sans-serif & JetBrains Mono).
• Mobile Responsive: Slide-out filter drawer, sticky action bar, and responsive modal actions.
• Search Scopes: Filter across Full Text, Title Only, Description & Solution, Organization, or Problem ID.
• 12 Tech Tag Pills: AI/ML, Computer Vision, NLP/LLM, GIS/Satellite, IoT, Blockchain, Robotics, Mobile Apps, Cloud, HealthTech, AgriTech, Cybersecurity.
• 1-Click Copy as MD: Copies the problem statement as a formatted GitHub markdown document with metadata table and sections.
• Clean Export Engine: Export filtered or selected subsets to JSON and CSV with sanitized fields.
• Problem Detail Modal: Tabbed view featuring Full Description, Structured Sections, and Datasets & References.