cursor-instructions/HF_DEPLOYMENT_SUMMARY.md

🚀 Quick Summary - HuggingFace Space Update Request

What We Need

UPDATE our existing HuggingFace Space to become the SINGLE SOURCE OF TRUTH for all data in Dreammaker Crypto Platform.

Why

Currently, data requests are scattered across 60+ files using multiple APIs. We want to centralize everything through one HF Space endpoint.

What Should Be Deployed

30+ API Endpoints Including:

Market Data:

• GET /api/market - Top cryptocurrencies list
• GET /api/price/{symbol} - Current price
• GET /api/ohlcv - Chart data (OHLCV)
• GET /api/ticker/{symbol} - Real-time ticker

News & Sentiment:

• GET /api/news/latest - Latest crypto news
• GET /api/sentiment/global - Fear & Greed Index
• GET /api/sentiment/symbol/{symbol} - Symbol-specific sentiment

Trading:

• GET /api/exchange-info - Trading pairs
• GET /api/orderbook/{symbol} - Order book
• GET /api/trades/{symbol} - Recent trades

AI & Predictions:

• GET /api/ai/signals - AI trading signals
• POST /api/ai/predict - Price predictions
• GET /api/ai/analysis/{symbol} - Comprehensive analysis

Blockchain:

• GET /api/blockchain/transactions/{address} - Transaction history
• GET /api/blockchain/whale-alerts - Large transfers

Statistics:

• GET /api/stats - Global market stats
• GET /api/stats/dominance - Market dominance

WebSocket:

• WS /ws/ticker - Real-time price updates
• WS /ws/trades - Real-time trade stream

Tech Stack Required

- FastAPI (Python 3.9+)
- Redis (caching)
- aiohttp (async HTTP)
- PyTorch + Transformers (AI models)
- ccxt (exchange integration)
- WebSockets (real-time)

Data Sources to Integrate

1. CoinGecko API (market data)
2. Binance API (OHLCV, trades)
3. NewsAPI / CryptoPanic (news)
4. Alternative.me (Fear & Greed Index)
5. AI Models (sentiment, predictions)

Key Features

✅ Automatic Fallbacks - If one source fails, try another
✅ Smart Caching - Different TTL for different data types
✅ Rate Limiting - Respect API limits
✅ Error Handling - Consistent error format
✅ WebSocket - Real-time updates
✅ AI Models - BERT for sentiment, LSTM for predictions

Deployment Files

See complete details in:

• 📄 HUGGINGFACE_SPACE_DEPLOYMENT_REQUEST.md (Full specifications)
• 📄 DATA_ARCHITECTURE_ANALYSIS_REPORT.md (Architecture analysis)

Expected Outcome

Before: 201 files making direct API calls
After: ALL data comes from HF Space (single endpoint)

Result:

• -70% code reduction
• +300% performance improvement
• 100% control over data flow

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Status: 🟡 Awaiting Implementation
Priority: HIGH
Type: UPDATE REQUEST (not new deployment)