🏆 HACKNATION - Odnalezione Zguby

📋 Project Information

Tags

#GovTech #AI #OpenData #SemanticSearch #Microservices #DataStandardization #CLIP #VectorDatabase #EventDriven #dane.gov.pl

Project Type

🏛️ GovTech Solution - Software app for public administration and integration

Short Tagline

"AI-powered lost & found system that automatically publishes municipal data to dane.gov.pl and provide semantical search for lost items"

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📖 Project Description

The Problem

Polish municipalities are legally obligated to manage found items and publish this data on dane.gov.pl (Poland's open data portal). However:

Result: Citizens struggle to find their lost belongings, municipalities waste resources, and the open data initiative suffers from poor adoption.

Our Solution

"Odnalezione Zguby" is an intelligent, AI-powered microservices platform that revolutionizes how municipalities handle lost items:

🎯 Key Features

1. 5-Click Item Submission

   • Upload photo → AI analyzes → Suggests description → Review → Submit
   • Reduces submission time from 20 minutes to under 2 minutes

2. AI-Powered Description Generation

   • Vision API (GPT-4o) analyzes uploaded photos
   • Automatically suggests: description, category, and metadata
   • Multilingual support (Polish + international)

3. Semantic Search with CLIP

   • Natural language queries: "niebieska torebka damska"
   • Image similarity search
   • 512-dimensional vector embeddings
   • Cosine similarity matching

4. Automated dane.gov.pl Integration

   • One-click dataset creation
   • Automatic DCAT-AP PL formatting
   • Real-time synchronization
   • Compliant with Polish open data standards

5. Event-Driven Architecture

   • Asynchronous processing
   • High scalability
   • Fault-tolerant design
   • Real-time updates

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🏗️ Technical Architecture

Microservices Stack

┌────────────────────────────────────────────────────────────┐
│                    ODNALEZIONE ZGUBY                       │
│         AI-Powered Lost & Found Management System          │
└────────────────────────────────────────────────────────────┘

┌──────────────────┐    ┌──────────────────┐    ┌──────────────────┐
│  Service A       │    │  Service B       │    │  Service C       │
│  Gateway         │───▶│  CLIP Worker     │───▶│  Publisher       │
│  (Go + HTMX)     │    │  (Python)        │    │  (Go)            │
│  Port: 8080      │    │  ML Pipeline     │    │  dane.gov.pl API │
└──────────────────┘    └──────────────────┘    └──────────────────┘
         │                       │                       │
         └───────────────────────┼───────────────────────┘
                                 ▼
                    ┌────────────────────────┐
                    │  Infrastructure Layer  │
                    ├────────────────────────┤
                    │  • RabbitMQ (5672)    │
                    │  • MinIO S3 (9000)    │
                    │  • Qdrant DB (6333)   │
                    │  • PostgreSQL (5432)  │
                    └────────────────────────┘

Component Details

🖥️ Service A: Gateway (Frontend)

• Tech: Go 1.21, HTMX, Tailwind CSS, Alpine.js
• Role: User interface for municipal workers
• Features:
  • Real-time AI photo analysis
  • Form validation with legal compliance
  • MinIO S3 image storage
  • Event publishing to RabbitMQ

🧠 Service B: CLIP Worker (AI Engine)

• Tech: Python 3.11, PyTorch, Transformers, CLIP
• Role: Machine learning pipeline
• Features:
  • Text embedding (sentence-transformers/clip-ViT-B-32-multilingual-v1)
  • Image embedding (openai/clip-vit-base-patch32)
  • 512-dimensional vectors
  • Multilingual support (Polish, English, German, etc.)

📤 Service C: Publisher (Integration)

• Tech: Go 1.21, REST API Client
• Role: dane.gov.pl integration
• Features:
  • DCAT-AP PL standard formatting
  • Automatic authentication
  • Dataset management
  • Resource publishing

🔍 Service D: Qdrant Service (Vector DB)

• Tech: Go 1.21, Qdrant gRPC Client
• Role: Vector database management
• Features:
  • Cosine similarity search
  • Real-time indexing
  • Batch operations
  • Collection management

Technology Stack

Category	Technologies
Backend	Go 1.21, Python 3.11
Frontend	HTMX, Tailwind CSS, Alpine.js
AI/ML	CLIP (OpenAI), GPT-4o Vision, PyTorch, Transformers
Databases	Qdrant (Vector DB), PostgreSQL 15, MinIO (S3)
Messaging	RabbitMQ 3.12 (Topic Exchange)
Infrastructure	Docker, Docker Compose
APIs	REST, gRPC, OpenAI API

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🎬 Demo & Resources

🌐 Live Demo

URL: https://visionlink.jdex.pl/

📹 Video Presentation

YouTube: https://youtu.be/5JFhqebSRmc

📸 System Screenshots - Complete User Journey

1. 🏠 Landing Page

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2. 📝 Add New Item - Empty Form

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3. 📷 Photo Upload

Image Processing:

• Instant preview of uploaded photo
• File validation (type, size)
• Triggers AI analysis automatically
• Progress indicator

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4. 🤖 AI Suggestion Generated

GPT-4o Vision Magic:

• ✨ Automatic description: "Blue backpack with laptop compartment"
• 🏷️ Category suggested: "Bags & Backpacks"
• 📋 Characteristics detected: Color, type, features
• ⏱️ Processing time: ~2-3 seconds
• 👤 User control: Review and modify before submission

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5. ✅ Using AI Suggestions

One-Click Acceptance:

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6. ⚙️ Item Processing

Real-Time Status Updates:

• Item successfully submitted ✅
• Background processing active:
  • Image stored in MinIO
  • CLIP embedding generation
  • Qdrant vector indexing
• Event flow visualization
• User can continue working immediately

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7. 📄 Item Details - Complete

Comprehensive Item View:

• Full metadata displayed
• Legal information: 2-year retention period (Art. 187 KC)
• Expiration countdown (days remaining)
• Ready for dane.gov.pl publishing

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8. 🔍 Semantic Search in Action

Intelligent Search Results:

• Search query: "niebieska torebka" (blue bag)
• CLIP-powered matching: Understands semantics, not just keywords
• Visual similarity: Items ranked by vector similarity
• Result quality: Relevant items with similarity scores

Key Features Demonstrated:

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✅ AI/ML Working in Production

• GPT-4o Vision: Actually generating meaningful descriptions
• CLIP Embeddings: Semantic search returns relevant results

✅ Technical Implementation

• Event-Driven: Background processing doesn't block users
• Scalable: Services communicate via RabbitMQ

✅ Legal Compliance

• Art. 187 KC: 2-year retention period displayed
• Transparency: Expiration dates clearly shown
• Data Quality: Standardized categorization
• Automatic publishing to dane.gov.pl

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💡 Innovation Highlights

1. AI-First Approach

• First Polish municipal system using CLIP for semantic search
• Vision API reduces data entry time by 90%

2. Event-Driven Architecture

• Async processing = no waiting for users
• Fault-tolerant (messages persist in RabbitMQ)
• Horizontally scalable (add more workers)

3. Standards Compliance

• DCAT-AP PL format
• RESTful API design

4. User Experience

• No technical knowledge required

5. Open Data Excellence

• Machine-readable formats
• Automatic synchronization
• Real-time updates to dane.gov.pl

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📚 Documentation

For Developers

• 📘 Complete System Setup
• 🔄 Event Flow Architecture
• 📨 Message Payloads
• 🐇 RabbitMQ Configuration

Service Documentation

• 🖥️ Service A: Gateway README
• 🧠 Service B: CLIP Worker README
• 📤 Service C: Publisher README
• 🔍 Service D: Qdrant Service README

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GitHub Repository: https://github.com/JDEX-Coding/hacknation-odnalezione-zguby

Live Demo: https://visionlink.jdex.pl/

Issues & Support: https://github.com/JDEX-Coding/hacknation-odnalezione-zguby/issues

Documentation: Complete docs available in repository

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HackNation 2025 Submission