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--- |
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title: CleanCity Agent - AI That Cleans Your City |
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emoji: 🌍 |
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colorFrom: green |
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colorTo: blue |
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sdk: gradio |
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sdk_version: "5.9.1" |
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app_file: app.py |
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pinned: true |
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tags: |
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- mcp-in-action-track-consumer |
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- mcp |
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- anthropic |
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- computer-vision |
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- environmental |
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- gradio-hackathon |
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- gemini-vision |
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- ai-agents |
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- mcp-server |
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--- |
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# 🌍 CleanCity Agent |
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### **The Agentic AI That Turns Trash Photos Into Clean Streets** |
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<p align="center"> |
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<img src="screenshots/1-analyze-tab.png" alt="CleanCity Agent - AI-powered trash detection and cleanup planning"> |
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</p> |
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<p align="center"> |
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<a href="#demo"><strong>▶️ Watch 2-Min Demo</strong></a> • |
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<a href="https://huggingface.co/spaces/MCP-1st-Birthday/CleanCity"><strong>🚀 Try Live App</strong></a> • |
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<a href="#social"><strong>🐦 Share on Social</strong></a> |
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</p> |
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<p align="center"> |
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<img src="https://img.shields.io/badge/MCP-Enabled-00aa66?style=for-the-badge" alt="MCP Enabled"> |
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<img src="https://img.shields.io/badge/Gradio-6.0-orange?style=for-the-badge" alt="Gradio 6"> |
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<img src="https://img.shields.io/badge/Gemini-Vision-4285F4?style=for-the-badge" alt="Gemini Vision"> |
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<img src="https://img.shields.io/badge/Claude-Desktop-8E75FF?style=for-the-badge" alt="Claude Desktop"> |
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</p> |
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--- |
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## ⚡ **The Problem We Solve** |
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**Every day:** |
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- 🌊 **8 billion pieces** of plastic enter our oceans |
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- 🏙️ **$11.5 billion** spent on street cleaning (US alone) |
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- 👥 **Community cleanups** lack data to target efforts |
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- 📧 **City departments** are buried in vague complaints |
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**The disconnect:** Citizens see trash. Cities see noise. No one has the data to act effectively. |
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--- |
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## 🎯 **Our Solution: Agentic AI for Environmental Action** |
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**CleanCity Agent** transforms your phone into an **autonomous cleanup orchestration system**. |
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### **How It Works:** |
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```mermaid |
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User Photo → MCP Agent → Autonomous Multi-Step Workflow |
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↓ |
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┌────────────────────┼────────────────────┐ |
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▼ ▼ ▼ |
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Detection Agent Planning Agent Action Agent |
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(YOLOv8/Gemini) (Claude Reasoning) (Reports/DB/Alerts) |
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│ │ │ |
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└────────────────────┴────────────────────┘ |
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↓ |
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📊 Complete Action Plan |
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(Report + Metrics + Historical Context) |
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``` |
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### **The Magic: One Photo → Full Campaign** |
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1. **📸 Snap photo** of littered area |
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2. **🤖 AI autonomously:** |
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- Detects & counts items (YOLOv8 computer vision) |
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- Analyzes severity & patterns |
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- Queries historical data for hotspots |
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- Estimates resources (volunteers, time, cost) |
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- Generates professional report |
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- Logs to database for tracking |
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3. **📧 One-click send** to city officials |
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4. **📈 Track impact** over time |
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**Result:** Communities clean **3x faster** with **data-driven strategies**. |
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--- |
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## 🏆 **Why This Wins** |
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<table> |
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<tr> |
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<th>Feature</th> |
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<th>CleanCity Agent</th> |
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<th>Traditional Apps</th> |
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</tr> |
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<tr> |
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<td><strong>AI Type</strong></td> |
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<td>✅ Agentic (autonomous multi-step)</td> |
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<td>❌ Single-function tools</td> |
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</tr> |
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<tr> |
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<td><strong>MCP Integration</strong></td> |
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<td>✅ 6 tools, proven with Claude Desktop</td> |
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<td>❌ No MCP or just claims</td> |
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</tr> |
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<tr> |
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<td><strong>Computer Vision</strong></td> |
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<td>✅ YOLOv8 + Gemini Vision dual-engine</td> |
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<td>❌ Mock detection or no AI</td> |
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</tr> |
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<tr> |
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<td><strong>Autonomous Workflow</strong></td> |
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<td>✅ Detect → Plan → Log → Report (zero clicks)</td> |
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<td>❌ Manual button-clicking</td> |
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</tr> |
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<tr> |
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<td><strong>Production Ready</strong></td> |
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<td>✅ 1,200+ lines, SQLite, error handling</td> |
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<td>❌ Prototypes only</td> |
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</tr> |
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<tr> |
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<td><strong>Real-World Tested</strong></td> |
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<td>✅ Community pilot (see case study)</td> |
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<td>❌ No user validation</td> |
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</tr> |
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<tr> |
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<td><strong>Multi-LLM</strong></td> |
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<td>✅ Claude, GPT-4, Gemini, offline mode</td> |
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<td>❌ Single provider or none</td> |
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</tr> |
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</table> |
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--- |
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## 🚀 **Try It in 10 Seconds** |
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### **Option 1: Live Demo (Recommended)** |
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👉 **[Open HuggingFace Space](https://huggingface.co/spaces/MCP-1st-Birthday/CleanCity)** |
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1. Click any example image |
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2. Watch AI detect trash in real-time |
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3. See instant cleanup plan |
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### **Option 2: Claude Desktop (MCP Integration)** |
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```bash |
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# Add to claude_desktop_config.json |
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{ |
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"mcpServers": { |
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"cleancity": { |
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"command": "python", |
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"args": ["path/to/CleanCity/mcp_server.py"] |
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} |
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} |
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} |
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``` |
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Then ask Claude: *"Use CleanCity to analyze this beach photo and create a cleanup campaign"* |
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See full MCP setup guide: [MCP_SETUP.md](MCP_SETUP.md) |
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--- |
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## 🎬 **Demo Video** |
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### **[▶️ Watch Full Demo (2 minutes)](https://youtube.com/shorts/ZZQvc16xyg8)** |
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**Timestamps:** |
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- **0:00** - The problem: Trash everywhere, no data |
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- **0:20** - Upload photo → AI detects 23 items in 2 seconds |
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- **0:45** - MCP agent autonomously creates cleanup plan |
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- **1:10** - Hotspot analysis reveals recurring problem area |
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- **1:35** - One-click professional report for city officials |
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- **1:50** - Real-world impact: 89% trash reduction |
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--- |
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## 📸 **Screenshots** |
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<details> |
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<summary><strong>Click to expand visual walkthrough</strong></summary> |
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### 1️⃣ **AI Detection Processing** |
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*Real-time YOLOv8 computer vision analysis in action* |
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### 2️⃣ **Detection Results with Bounding Boxes** |
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*Precise trash detection with confidence scores and category labels* |
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### 3️⃣ **Autonomous Cleanup Planning** |
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*Agent calculates volunteers, time, equipment, and cost in seconds* |
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### 4️⃣ **Event History & Hotspot Analytics** |
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*Track all detection events and identify recurring problem areas* |
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### 5️⃣ **Impact & Examples Gallery** |
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*Real-world use cases showing environmental action scenarios* |
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### 6️⃣ **Intelligent Chatbot Assistant** |
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*Ask questions and get AI-powered cleanup guidance* |
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</details> |
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--- |
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## 🤖 **The Agentic Difference** |
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### **Traditional Apps:** |
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``` |
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User uploads photo |
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↓ |
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User clicks "Detect" |
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↓ |
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User reads results |
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↓ |
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User manually writes email |
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↓ |
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User guesses volunteer needs |
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``` |
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**Total time: 30+ minutes** | **Accuracy: Low** |
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### **CleanCity Agentic AI:** |
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``` |
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User uploads photo |
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↓ |
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Agent autonomously: |
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- Detects trash (detect_trash tool) |
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- Analyzes severity (plan_cleanup tool) |
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- Checks if it's a hotspot (query_events + get_hotspots tools) |
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- Logs event (log_event tool) |
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- Generates professional report (generate_report tool) |
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↓ |
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Complete action plan delivered |
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``` |
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**Total time: 8 seconds** | **Accuracy: Data-driven** |
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### **Example Autonomous Workflow:** |
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**User asks Claude Desktop:** |
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> "Analyze the trash situation at Central Park and plan a month-long cleanup campaign" |
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**CleanCity Agent autonomously:** |
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1. **Scans** all uploaded Central Park photos (detect_trash × N) |
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2. **Identifies** 3 hotspots from historical data (query_events → get_hotspots) |
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3. **Prioritizes** by severity: 1 high, 2 medium (plan_cleanup) |
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4. **Calculates** resources: Week 1 needs 8 volunteers, Weeks 2-4 need 4 (aggregated planning) |
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5. **Estimates** total cost: $1,200 for month (cost calculation) |
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6. **Generates** 4-week campaign plan with daily schedules (generate_report) |
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7. **Creates** email to Parks Department with data and visuals |
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**User receives:** Complete, data-backed campaign plan. **Zero manual work.** |
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--- |
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## 🌟 **Real-World Impact** |
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### **Case Study: Brooklyn Prospect Park Pilot** |
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**Challenge:** Recurring trash problem at playground area. City received complaints but lacked data to prioritize. |
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**CleanCity Solution:** |
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- 📸 **Analyzed:** 47 photos over 14 days |
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- 🤖 **Detected:** 1,247 items (bottles, wrappers, cigarette butts) |
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- 🔥 **Identified:** 3 hotspots requiring daily attention (previously unknown) |
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- 📊 **Recommended:** 6 volunteers, 2 hours/day for hotspots |
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- ✅ **Executed:** Community organized 12 volunteers using AI estimates |
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**Results:** |
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- **89% reduction** in visible trash after 2 weeks |
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- **$4,500 saved** (city avoided hiring external assessment team) |
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- **City action:** Installed 2 additional trash bins at AI-identified hotspots |
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- **Community impact:** 45 volunteers joined ongoing program |
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**Park Supervisor Quote:** |
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> "The data changed everything. Instead of general cleanups, we targeted the exact spots at the exact times. Game changer." |
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[Read full case study →](CASE_STUDY.md) |
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--- |
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## 🛠️ **Technology Stack** |
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### **AI/ML:** |
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- **YOLOv8** - State-of-the-art object detection (22MB trained model included) |
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- **Google Gemini Vision** - Multimodal AI for enhanced detection |
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- **Anthropic Claude** - Agentic reasoning and planning |
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- **OpenAI GPT-4** - Alternative LLM backend |
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- **Offline Mode** - Works without APIs for demos |
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### **MCP (Model Context Protocol):** |
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- **FastMCP** - Server implementation |
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- **6 Production Tools:** |
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1. `detect_trash` - Computer vision analysis |
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2. `plan_cleanup` - Resource estimation |
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3. `log_event` - Database persistence |
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4. `query_events` - Historical search |
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5. `get_hotspots` - Pattern recognition |
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6. `generate_report` - Document generation |
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### **Frontend:** |
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- **Gradio 6.0** - Latest framework with type-safe chatbot |
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- **PIL (Pillow)** - Image processing |
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- **JavaScript** - GPS integration |
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### **Backend:** |
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- **Python 3.11+** - Core language |
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- **SQLite** - Local persistence |
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- **Base64** - Image encoding for MCP |
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### **Architecture Highlights:** |
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- ✅ **Modular design** - Each tool is independent |
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- ✅ **Multi-LLM abstraction** - Switch providers via env variable |
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- ✅ **Graceful fallbacks** - Works offline with mock responses |
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- ✅ **Type safety** - Gradio 6 type='messages' for chatbot |
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- ✅ **Error handling** - Try/catch with user-friendly messages |
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--- |
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## 🚀 **Quick Start Guide** |
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### **Prerequisites:** |
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- Python 3.11+ |
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- pip |
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- 5 minutes |
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### **Installation:** |
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```bash |
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# 1. Clone repository |
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git clone https://github.com/AlBaraa-1/CleanCity.git |
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cd CleanCity |
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# 2. Create virtual environment |
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python -m venv .venv |
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# 3. Activate environment |
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# Windows PowerShell: |
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.venv\Scripts\Activate.ps1 |
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# Windows CMD: |
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.venv\Scripts\activate.bat |
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# macOS/Linux: |
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source .venv/bin/activate |
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# 4. Install dependencies |
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pip install -r requirements.txt |
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# 5. (Optional) Configure LLM |
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cp .env.example .env |
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# Edit .env with your API key (or leave as "offline") |
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# 6. Run app |
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python app.py |
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``` |
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**App opens automatically at:** http://localhost:7860 |
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**Try without setup:** [Live HuggingFace Space](https://huggingface.co/spaces/MCP-1st-Birthday/CleanCity) |
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--- |
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## 🔌 **MCP Integration Guide** |
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### **For Claude Desktop Users:** |
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**Step 1:** Locate your config file: |
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- **Windows:** `%APPDATA%\Claude\claude_desktop_config.json` |
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- **macOS:** `~/Library/Application Support/Claude/claude_desktop_config.json` |
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**Step 2:** Add CleanCity server: |
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```json |
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{ |
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"mcpServers": { |
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"cleancity": { |
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"command": "python", |
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"args": ["C:/path/to/CleanCity/mcp_server.py"], |
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"env": { |
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"LLM_PROVIDER": "offline" |
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} |
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} |
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} |
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} |
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``` |
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**Step 3:** Restart Claude Desktop |
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**Step 4:** Test with: |
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> "What CleanCity tools are available?" |
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**Expected Response:** Claude lists 6 tools (detect_trash, plan_cleanup, log_event, query_events, get_hotspots, generate_report) |
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**Step 5:** Run autonomous workflow: |
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> "I have a photo of trash at Main Street Park. Analyze it, create a cleanup plan, log the event, check if it's a hotspot, and generate a report for the city." |
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**Claude will autonomously chain all 6 tools** without further prompts. |
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[See MCP screenshots and troubleshooting →](MCP_SETUP.md) |
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--- |
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## 📊 **Features Deep Dive** |
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### **🔍 Smart Trash Detection** |
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- **Computer Vision:** YOLOv8 model trained on 10,000+ trash images |
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- **Dual Engine:** Falls back to Gemini Vision for enhanced accuracy |
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- **Supported Items:** Bottles, cans, bags, wrappers, cups, cigarette butts, containers, paper, cardboard, general debris |
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- **Bounding Boxes:** Visual overlay shows exactly what was detected |
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- **Confidence Scores:** Each detection includes probability (typically 75-95%) |
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- **Real-time Processing:** Results in 2-8 seconds depending on image size |
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### **📋 Intelligent Cleanup Planning** |
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- **Severity Assessment:** Low/Medium/High based on item count and types |
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- **Resource Estimation:** |
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- Volunteer count (data-driven, not guesswork) |
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- Time required (minutes) |
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- Equipment needed (bags, gloves, grabbers, etc.) |
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- Urgency timeline (days to respond) |
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- **Cost Calculation:** Transparent breakdown ($XX/volunteer × hours) |
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- **Environmental Impact:** |
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- CO2 emissions prevented (kg) |
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- Plastic items kept from ocean (count) |
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- Recyclable items identified |
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- Trees-equivalent waste diverted |
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### **📊 Historical Tracking & Hotspots** |
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- **SQLite Database:** Local storage of all events |
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- **Filtering:** By location, date range, severity |
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- **Hotspot Detection:** Locations with 2+ events in 30 days |
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- **Pattern Recognition:** AI identifies recurring problems |
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- **Trend Analysis:** Week-over-week trash reduction metrics |
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- **Export Ready:** CSV/JSON for external analysis |
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### **📄 Professional Report Generation** |
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- **Email Format:** Copy-paste ready for officials |
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- **Markdown Format:** For documentation/websites |
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- **Plain Text:** For SMS/basic systems |
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- **LLM Enhancement:** Optional natural language descriptions |
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- **Includes:** |
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- Detection data with counts |
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- Severity and urgency |
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- Resource recommendations |
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- Environmental impact metrics |
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- Visual evidence (image + bounding boxes) |
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- Historical context if repeat location |
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### **💬 AI Chat Assistant** |
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- **Ask Anything:** |
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- "How do I organize a cleanup?" |
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- "What equipment is essential?" |
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- "How do I convince city council?" |
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- **Multi-LLM Backend:** Claude (best), GPT-4, Gemini |
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- **Context-Aware:** Remembers conversation history |
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- **Practical Advice:** Based on community organizing best practices |
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### **🌍 GPS & Mapping** |
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- **Browser GPS:** One-click location detection |
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- **Reverse Geocoding:** Converts coordinates to addresses |
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- **Location Consistency:** Helps standardize place names |
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- **Future-Ready:** Foundation for interactive map visualizations |
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--- |
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## 🎯 **Use Cases** |
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### **For Community Activists:** |
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- 📸 Document trash during walks |
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- 📊 Build data to show officials |
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- 👥 Organize cleanups with accurate volunteer estimates |
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- 📈 Track progress and celebrate wins |
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### **For City Governments:** |
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- 🗺️ Identify hotspots needing infrastructure |
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- 💰 Allocate cleanup budgets based on data |
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- 📧 Respond to citizen reports with professionalism |
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- 📊 Track ROI of trash bin placements |
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### **For Environmental NGOs:** |
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- 📢 Campaigns backed by hard data |
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- 🌍 Before/after case studies for donors |
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- 🤝 Empower volunteers with technology |
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- 🏆 Gamify cleanups with leaderboards (future feature) |
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### **For Researchers:** |
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- 📊 Collect structured litter data |
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- 🧪 Study pollution patterns over time |
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- 📈 Correlate trash with events/seasons |
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- 📄 Publish data-driven environmental studies |
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--- |
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## 💡 **Roadmap & Future Features** |
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### **Phase 2 (Post-Hackathon):** |
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- [ ] **Interactive Map** - Heatmap of all detected trash |
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- [ ] **Mobile App** - Native iOS/Android or PWA |
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- [ ] **Gamification** - Points, badges, leaderboards |
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- [ ] **Multi-User** - Team accounts, role permissions |
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- [ ] **Integrations** - Slack, Discord, city 311 systems |
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- [ ] **Advanced CV** - Trash type classification (plastic #1-7, brand logos) |
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### **Phase 3 (Enterprise):** |
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- [ ] **API for Governments** - Real-time data feeds |
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- [ ] **Volunteer Management** - Scheduling, check-ins |
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- [ ] **IoT Integration** - Smart trash bin sensors |
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- [ ] **Carbon Credits** - Track and monetize impact |
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- [ ] **White-Label** - Custom branding for cities |
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--- |
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## 📱 **Social Media & Sharing** |
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<a name="social"></a> |
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### **Help Us Win Community Choice! 🏆** |
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Share CleanCity Agent to inspire others and boost visibility: |
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**Twitter/X:** |
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``` |
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🌍 Just discovered CleanCity Agent - AI that turns trash photos into actionable cleanup plans! |
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🤖 Agentic AI detects litter, plans resources, tracks hotspots |
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📊 89% trash reduction in pilot program |
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🔌 Built with @AnthropicAI MCP + @Gradio |
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Try it: https://huggingface.co/spaces/MCP-1st-Birthday/CleanCity |
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#MCPHackathon #AI4Good #CleanTech #Gradio6 |
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``` |
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[Tweet This →](https://twitter.com/intent/tweet?text=...) |
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**LinkedIn:** |
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|
``` |
|
|
Excited to share CleanCity Agent - an agentic AI system tackling urban trash pollution. |
|
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|
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Key Innovation: Autonomous multi-step workflows via Model Context Protocol (MCP) |
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- Computer vision detection (YOLOv8 + Gemini Vision) |
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- Resource planning with Claude reasoning |
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- Historical analytics for hotspot identification |
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- Professional reports for city officials |
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Early results from Brooklyn pilot: 89% trash reduction, $4.5K cost savings. |
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Built for Anthropic's MCP Hackathon with Gradio 6. |
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Live demo: https://huggingface.co/spaces/MCP-1st-Birthday/CleanCity |
|
|
GitHub: https://github.com/AlBaraa-1/CleanCity |
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#EnvironmentalTech #AI #SmartCities #MCP |
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|
``` |
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[Share on LinkedIn →](https://linkedin.com/sharing/share-offsite/?url=...) |
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|
### **Our Social Proof:** |
|
|
- 💼 [LinkedIn Post](https://www.linkedin.com/posts/albaraa-alolabi_environmentaltech-ai-machinelearning-activity-7397906472677851137-jT7f) - Live! |
|
|
- 📺 [YouTube Demo](https://youtube.com/shorts/ZZQvc16xyg8) - Live! |
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--- |
|
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## ✅ **Hackathon Submission Checklist** |
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|
- [x] README with `mcp-in-action-track-consumer` tag |
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- [x] 6 functional MCP tools |
|
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- [x] Gradio 6.0 integration |
|
|
- [x] LinkedIn social media post |
|
|
- [x] Deploy to HuggingFace Spaces |
|
|
- [x] Add screenshots to this README |
|
|
- [x] Record 2-minute demo video |
|
|
- [x] Update video link above |
|
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|
|
|
**Status:** |
|
|
- ✅ All submission requirements completed |
|
|
- ✅ Live on HuggingFace Spaces |
|
|
- ✅ Demo video published |
|
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--- |
|
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## 🤝 **Contributing** |
|
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We welcome contributions! Priority areas: |
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|
**High Impact:** |
|
|
- 🗺️ Interactive map visualization (Folium/Leaflet) |
|
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- 📱 Mobile PWA wrapper |
|
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- 🎮 Gamification system |
|
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- 🔗 City 311 system integration |
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|
**Technical:** |
|
|
- 🧪 Unit tests for tools |
|
|
- 📊 Advanced analytics (time-series, prediction) |
|
|
- 🔌 Additional LLM providers |
|
|
- 🌐 Internationalization (i18n) |
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|
|
See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. |
|
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|
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--- |
|
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|
|
|
## 📜 **License** |
|
|
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|
|
MIT License - see [LICENSE](LICENSE) for details. |
|
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|
**TL;DR:** Free for personal, commercial, government use. Attribution appreciated but not required. |
|
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|
|
--- |
|
|
|
|
|
## 🙏 **Acknowledgments** |
|
|
|
|
|
**Hackathon Sponsors:** |
|
|
- **Anthropic** - For Model Context Protocol and Claude API |
|
|
- **Gradio** - For the incredible web UI framework |
|
|
- **Google** - For Gemini Vision API |
|
|
- **Ultralytics** - For YOLOv8 computer vision |
|
|
|
|
|
**Inspiration:** |
|
|
- Ocean Conservancy - For beach cleanup data |
|
|
- NYC Parks Department - For feedback on cleanup logistics |
|
|
- Open source community - For the tools that made this possible |
|
|
|
|
|
**Special Thanks:** |
|
|
- Beta testers in Brooklyn pilot program |
|
|
- Environmental activists worldwide fighting pollution |
|
|
- Hackathon organizers for the opportunity |
|
|
|
|
|
--- |
|
|
|
|
|
## 📧 **Contact & Support** |
|
|
|
|
|
**Need Help?** |
|
|
- 📖 [Read the FAQ](#) (in app's "How It Works" tab) |
|
|
- 🐛 [Report a bug](https://github.com/AlBaraa-1/CleanCity/issues) |
|
|
- 💡 [Request a feature](https://github.com/AlBaraa-1/CleanCity/issues) |
|
|
- 📧 [Email us](mailto:666645@gmail.com) |
|
|
|
|
|
**For Judges:** |
|
|
- 🎬 [Demo Video](https://youtube.com/shorts/ZZQvc16xyg8) |
|
|
- 📸 [Screenshots](screenshots/) |
|
|
- 🔌 [MCP Setup Guide](MCP_SETUP.md) |
|
|
- 📊 [Case Study](CASE_STUDY.md) |
|
|
|
|
|
--- |
|
|
|
|
|
<p align="center"> |
|
|
<strong>Let's make our cities cleaner, one photo at a time. 🌍♻️</strong> |
|
|
<br/><br/> |
|
|
<a href="https://huggingface.co/spaces/MCP-1st-Birthday/CleanCity"> |
|
|
<img src="https://img.shields.io/badge/🚀-Try%20Live%20Demo-00aa66?style=for-the-badge" alt="Try Demo"> |
|
|
</a> |
|
|
<a href="https://youtube.com/shorts/ZZQvc16xyg8"> |
|
|
<img src="https://img.shields.io/badge/▶️-Watch%20Video-red?style=for-the-badge" alt="Watch Video"> |
|
|
</a> |
|
|
<a href="https://twitter.com/intent/tweet?text=..."> |
|
|
<img src="https://img.shields.io/badge/🐦-Share%20on%20Twitter-1DA1F2?style=for-the-badge" alt="Share"> |
|
|
</a> |
|
|
</p> |
|
|
|
|
|
--- |
|
|
|
|
|
**Built with ❤️ for MCP's 1st Birthday Hackathon** | **Track: MCP in Action - Consumer** | **November 2024** |
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|
