archive/docs/HUGGINGFACE_DEPLOYMENT_PROMPT.md

# 🚀 Crypto-DT-Source: Complete HuggingFace Deployment Prompt

**Purpose:** Complete guide to activate ALL features in the Crypto-DT-Source project for production deployment on HuggingFace Spaces
**Target Environment:** HuggingFace Spaces + Python 3.11+
**Deployment Season:** Q4 2025
**Status:** Ready for Implementation

---

## 📋 Executive Summary

This prompt provides a **complete roadmap** to transform Crypto-DT-Source from a monitoring platform into a **fully-functional cryptocurrency data aggregation service**. All 50+ endpoints will be connected to real data sources, database persistence will be integrated, AI models will be loaded, and the system will be optimized for HuggingFace Spaces deployment.

**Expected Outcome:**
- ✅ Real crypto market data (live prices, OHLCV, trending coins)
- ✅ Historical data storage in SQLite
- ✅ AI-powered sentiment analysis using HuggingFace transformers
- ✅ Authentication + rate limiting on all endpoints
- ✅ WebSocket real-time streaming
- ✅ Provider health monitoring with intelligent failover
- ✅ Automatic provider discovery
- ✅ Full diagnostic and monitoring capabilities
- ✅ Production-ready Docker deployment to HF Spaces

---

## 🎯 Implementation Priorities (Phase 1-4)

### **Phase 1: Core Data Integration (CRITICAL)**
*Goal: Replace all mock data with real API calls*

#### 1.1 Market Data Endpoints
**Files to modify:**
- `api/endpoints.py` - `/api/market`, `/api/prices`
- `collectors/market_data_extended.py` - Real price fetching
- `api_server_extended.py` - FastAPI endpoints

**Requirements:**
- Remove all hardcoded mock data from endpoints
- Implement real API calls to CoinGecko, CoinCap, Binance
- Use async/await pattern for non-blocking calls
- Implement caching layer (5-minute TTL for prices)
- Add error handling with provider fallback

**Implementation Steps:**
```python

# Example: Replace mock market data with real provider data

GET /api/market

├── Call ProviderManager.get_best_provider('market_data')

├── Execute async request to provider

├── Cache response (5 min TTL)

├── Return real BTC/ETH prices instead of mock

└── Fallback to secondary provider on failure



GET /api/prices?symbols=BTC,ETH,SOL

├── Parse symbol list

├── Call ProviderManager for each symbol

├── Aggregate responses

├── Return real-time price data



GET /api/trending

├── Call CoinGecko trending endpoint

├── Store in database

└── Return top 7 trending coins



GET /api/ohlcv?symbol=BTCUSDT&interval=1h&limit=100

├── Call Binance OHLCV endpoint

├── Validate symbol format

├── Apply caching (15-min TTL)

└── Return historical OHLCV data

```

**Success Criteria:**
- [ ] All endpoints return real data from providers
- [ ] Caching implemented with configurable TTL
- [ ] Provider failover working (when primary fails)
- [ ] Response times < 2 seconds
- [ ] No hardcoded mock data in endpoint responses

---

#### 1.2 DeFi Data Endpoints
**Files to modify:**
- `api_server_extended.py` - `/api/defi` endpoint
- `collectors/` - Add DeFi collector

**Requirements:**
- Fetch TVL data from DeFi Llama API
- Track top DeFi protocols
- Cache for 1 hour (DeFi data updates less frequently)

**Implementation:**
```python

GET /api/defi

├── Call DeFi Llama: GET /protocols

├── Filter top 20 by TVL

├── Parse response (name, TVL, chain, symbol)

├── Store in database (defi_protocols table)

└── Return with timestamp



GET /api/defi/tvl-chart

├── Query historical TVL from database

├── Aggregate by date

└── Return 30-day TVL trend

```

---

#### 1.3 News & Sentiment Integration
**Files to modify:**
- `collectors/sentiment_extended.py`
- `api/endpoints.py` - `/api/sentiment` endpoint

**Requirements:**
- Fetch news from RSS feeds (CoinDesk, Cointelegraph, etc.)
- Implement real HuggingFace sentiment analysis (NOT keyword matching)
- Store sentiment scores in database
- Track Fear & Greed Index

**Implementation:**
```python

GET /api/sentiment

├── Query recent news from database

├── Load HuggingFace model: distilbert-base-uncased-finetuned-sst-2-english

├── Analyze each headline/article

├── Calculate aggregate sentiment score

├── Return: {overall_sentiment, fear_greed_index, top_sentiments}



GET /api/news

├── Fetch from RSS feeds (configurable)

├── Run through sentiment analyzer

├── Store in database (news table with sentiment)

├── Return paginated results



POST /api/analyze/text

├── Accept raw text input

├── Run HuggingFace sentiment model

├── Return: {text, sentiment, confidence, label}

```

---

### **Phase 2: Database Integration (HIGH PRIORITY)**
*Goal: Full persistent storage of all data*

#### 2.1 Database Schema Activation
**Files:**
- `database/models.py` - Define all tables
- `database/migrations.py` - Schema setup
- `database/db_manager.py` - Connection management

**Tables to Activate:**
```sql

-- Core tables

prices (id, symbol, price, timestamp, provider)

ohlcv (id, symbol, open, high, low, close, volume, timestamp)

news (id, title, content, sentiment, source, timestamp)

defi_protocols (id, name, tvl, chain, timestamp)

market_snapshots (id, btc_price, eth_price, market_cap, timestamp)



-- Metadata tables

providers (id, name, status, health_score, last_check)

pools (id, name, strategy, created_at)

api_calls (id, endpoint, provider, response_time, status)

user_requests (id, ip_address, endpoint, timestamp)

```

**Implementation:**
```python

# In api_server_extended.py startup:



@app.on_event("startup")

async def startup_event():

    # Initialize database

    db_manager = DBManager()

    await db_manager.initialize()



    # Run migrations

    await db_manager.run_migrations()



    # Create tables if not exist

    await db_manager.create_all_tables()



    # Verify connectivity

    health = await db_manager.health_check()

    logger.info(f"Database initialized: {health}")

```

#### 2.2 API Endpoints ↔ Database Integration
**Pattern to implement:**

```python

# Write pattern: After fetching real data, store it

async def store_market_snapshot():

    # Fetch real data

    prices = await provider_manager.get_market_data()



    # Store in database

    async with db.session() as session:

        snapshot = MarketSnapshot(

            btc_price=prices['BTC'],

            eth_price=prices['ETH'],

            market_cap=prices['market_cap'],

            timestamp=datetime.now()

        )

        session.add(snapshot)

        await session.commit()



    return prices



# Read pattern: Query historical data

@app.get("/api/prices/history/{symbol}")

async def get_price_history(symbol: str, days: int = 30):

    async with db.session() as session:

        history = await session.query(Price).filter(

            Price.symbol == symbol,

            Price.timestamp >= datetime.now() - timedelta(days=days)

        ).all()



    return [{"price": p.price, "timestamp": p.timestamp} for p in history]

```

**Success Criteria:**
- [ ] All real-time data is persisted to database
- [ ] Historical queries return > 30 days of data
- [ ] Database is queried for price history endpoints
- [ ] Migrations run automatically on startup
- [ ] No data loss on server restart

---

### **Phase 3: AI & Sentiment Analysis (MEDIUM PRIORITY)**
*Goal: Real ML-powered sentiment analysis*

#### 3.1 Load HuggingFace Models
**Files:**
- `ai_models.py` - Model loading and inference
- Update `requirements.txt` with torch, transformers

**Models to Load:**
```python

# Sentiment Analysis

SENTIMENT_MODELS = [

    "distilbert-base-uncased-finetuned-sst-2-english",  # Fast, accurate

    "cardiffnlp/twitter-roberta-base-sentiment-latest",  # Social media optimized

    "ProsusAI/finBERT",  # Financial sentiment

]



# Crypto-specific models

CRYPTO_MODELS = [

    "EleutherAI/gpt-neo-125M",  # General purpose (lightweight)

    "facebook/opt-125m",  # Instruction following

]



# Zero-shot classification for custom sentiment

"facebook/bart-large-mnli"  # Multi-class sentiment (bullish/bearish/neutral)

```

**Implementation:**
```python

# ai_models.py



class AIModelManager:

    def __init__(self):

        self.models = {}

        self.device = "cuda" if torch.cuda.is_available() else "cpu"



    async def initialize(self):

        """Load all models on startup"""

        logger.info("Loading HuggingFace models...")



        # Sentiment analysis

        self.models['sentiment'] = pipeline(

            "sentiment-analysis",

            model="distilbert-base-uncased-finetuned-sst-2-english",

            device=0 if self.device == "cuda" else -1

        )



        # Zero-shot for crypto sentiment

        self.models['zeroshot'] = pipeline(

            "zero-shot-classification",

            model="facebook/bart-large-mnli",

            device=0 if self.device == "cuda" else -1

        )



        logger.info("Models loaded successfully")



    async def analyze_sentiment(self, text: str) -> dict:

        """Analyze sentiment of text"""

        if not self.models.get('sentiment'):

            return {"error": "Model not loaded", "sentiment": "unknown"}



        result = self.models['sentiment'](text)[0]



        return {

            "text": text[:100],

            "label": result['label'],

            "score": result['score'],

            "timestamp": datetime.now().isoformat()

        }



    async def analyze_crypto_sentiment(self, text: str) -> dict:

        """Crypto-specific sentiment (bullish/bearish/neutral)"""

        candidate_labels = ["bullish", "bearish", "neutral"]

        result = self.models['zeroshot'](text, candidate_labels)



        return {

            "text": text[:100],

            "sentiment": result['labels'][0],

            "scores": dict(zip(result['labels'], result['scores'])),

            "timestamp": datetime.now().isoformat()

        }



# In api_server_extended.py

ai_manager = AIModelManager()



@app.on_event("startup")

async def startup():

    await ai_manager.initialize()



@app.post("/api/sentiment/analyze")

async def analyze_sentiment(request: AnalyzeRequest):

    """Real sentiment analysis endpoint"""

    result = await ai_manager.analyze_sentiment(request.text)

    return result



@app.post("/api/sentiment/crypto-analysis")

async def crypto_sentiment(request: AnalyzeRequest):

    """Crypto-specific sentiment analysis"""

    result = await ai_manager.analyze_crypto_sentiment(request.text)

    return result

```

#### 3.2 News Sentiment Pipeline
**Implementation:**

```python

# Background task: Analyze news sentiment continuously



async def analyze_news_sentiment():

    """Run every 30 minutes: fetch news and analyze sentiment"""

    while True:

        try:

            # 1. Fetch recent news from feeds

            news_items = await fetch_rss_feeds()



            # 2. Store news items

            for item in news_items:

                # 3. Analyze sentiment

                sentiment = await ai_manager.analyze_sentiment(item['title'])



                # 4. Store in database

                async with db.session() as session:

                    news = News(

                        title=item['title'],

                        content=item['content'],

                        source=item['source'],

                        sentiment=sentiment['label'],

                        confidence=sentiment['score'],

                        timestamp=datetime.now()

                    )

                    session.add(news)



            await session.commit()

            logger.info(f"Analyzed {len(news_items)} news items")



        except Exception as e:

            logger.error(f"News sentiment pipeline error: {e}")



        # Wait 30 minutes

        await asyncio.sleep(1800)



# Start in background on app startup

@app.on_event("startup")

async def startup():

    asyncio.create_task(analyze_news_sentiment())

```

---

### **Phase 4: Security & Production Setup (HIGH PRIORITY)**
*Goal: Production-ready authentication, rate limiting, and monitoring*

#### 4.1 Authentication Implementation
**Files:**
- `utils/auth.py` - JWT token handling
- `api/security.py` - New file for security middleware

**Implementation:**

```python

# utils/auth.py



from fastapi import Depends, HTTPException, status

from fastapi.security import HTTPBearer, HTTPAuthCredentials

import jwt

from datetime import datetime, timedelta



SECRET_KEY = os.getenv("JWT_SECRET_KEY", "your-secret-key-change-in-production")

ALGORITHM = "HS256"



class AuthManager:

    @staticmethod

    def create_token(user_id: str, hours: int = 24) -> str:

        """Create JWT token"""

        payload = {

            "user_id": user_id,

            "exp": datetime.utcnow() + timedelta(hours=hours),

            "iat": datetime.utcnow()

        }

        return jwt.encode(payload, SECRET_KEY, algorithm=ALGORITHM)



    @staticmethod

    def verify_token(token: str) -> str:

        """Verify JWT token"""

        try:

            payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])

            return payload.get("user_id")

        except jwt.ExpiredSignatureError:

            raise HTTPException(status_code=401, detail="Token expired")

        except jwt.InvalidTokenError:

            raise HTTPException(status_code=401, detail="Invalid token")



security = HTTPBearer()

auth_manager = AuthManager()



async def get_current_user(credentials: HTTPAuthCredentials = Depends(security)):

    """Dependency for protected endpoints"""

    return auth_manager.verify_token(credentials.credentials)



# In api_server_extended.py

@app.post("/api/auth/token")

async def get_token(api_key: str):

    """Issue JWT token for API key"""

    # Validate API key against database

    user = await verify_api_key(api_key)

    if not user:

        raise HTTPException(status_code=401, detail="Invalid API key")



    token = auth_manager.create_token(user.id)

    return {"access_token": token, "token_type": "bearer"}



# Protected endpoint example

@app.get("/api/protected-data")

async def protected_endpoint(current_user: str = Depends(get_current_user)):

    """This endpoint requires authentication"""

    return {"user_id": current_user, "data": "sensitive"}

```

#### 4.2 Rate Limiting
**Files:**
- `utils/rate_limiter_enhanced.py` - Enhanced rate limiter

**Implementation:**

```python

# In api_server_extended.py

from slowapi import Limiter

from slowapi.util import get_remote_address

from slowapi.errors import RateLimitExceeded



limiter = Limiter(key_func=get_remote_address)

app.state.limiter = limiter



# Rate limit configuration

FREE_TIER = "30/minute"  # 30 requests per minute

PRO_TIER = "300/minute"   # 300 requests per minute

ADMIN_TIER = None          # Unlimited



@app.exception_handler(RateLimitExceeded)

async def rate_limit_handler(request, exc):

    return JSONResponse(

        status_code=429,

        content={"error": "Rate limit exceeded", "retry_after": 60}

    )



# Apply to endpoints

@app.get("/api/prices")

@limiter.limit(FREE_TIER)

async def get_prices(request: Request):

    return await prices_handler()



@app.get("/api/sentiment")

@limiter.limit(FREE_TIER)

async def get_sentiment(request: Request):

    return await sentiment_handler()



# Premium endpoints

@app.get("/api/historical-data")

@limiter.limit(PRO_TIER)

async def get_historical_data(request: Request, current_user: str = Depends(get_current_user)):

    return await historical_handler()

```

**Tier Configuration:**
```python

RATE_LIMIT_TIERS = {

    "free": {

        "requests_per_minute": 30,

        "requests_per_day": 1000,

        "max_symbols": 5,

        "data_retention_days": 7

    },

    "pro": {

        "requests_per_minute": 300,

        "requests_per_day": 50000,

        "max_symbols": 100,

        "data_retention_days": 90

    },

    "enterprise": {

        "requests_per_minute": None,  # Unlimited

        "requests_per_day": None,

        "max_symbols": None,

        "data_retention_days": None

    }

}

```

---

#### 4.3 Monitoring & Diagnostics
**Files:**
- `api/endpoints.py` - Diagnostic endpoints
- `monitoring/health_monitor.py` - Health checks

**Implementation:**

```python

@app.get("/api/health")

async def health_check():

    """Comprehensive health check"""

    return {

        "status": "healthy",

        "timestamp": datetime.now().isoformat(),

        "components": {

            "database": await check_database(),

            "providers": await check_providers(),

            "models": await check_models(),

            "websocket": await check_websocket(),

            "cache": await check_cache()

        },

        "metrics": {

            "uptime_seconds": get_uptime(),

            "active_connections": active_ws_count(),

            "request_count_1h": get_request_count("1h"),

            "average_response_time_ms": get_avg_response_time()

        }

    }



@app.post("/api/diagnostics/run")

async def run_diagnostics(auto_fix: bool = False):

    """Full system diagnostics"""

    issues = []

    fixes = []



    # Check all components

    checks = [

        check_database_integrity(),

        check_provider_health(),

        check_disk_space(),

        check_memory_usage(),

        check_model_availability(),

        check_config_files(),

        check_required_directories(),

        verify_api_connectivity()

    ]



    results = await asyncio.gather(*checks)



    for check in results:

        if check['status'] != 'ok':

            issues.append(check)

            if auto_fix:

                fix = await apply_fix(check)

                fixes.append(fix)



    return {

        "timestamp": datetime.now().isoformat(),

        "total_checks": len(checks),

        "issues_found": len(issues),

        "issues": issues,

        "fixes_applied": fixes if auto_fix else []

    }



@app.get("/api/metrics")

async def get_metrics():

    """System metrics for monitoring"""

    return {

        "cpu_percent": psutil.cpu_percent(interval=1),

        "memory_percent": psutil.virtual_memory().percent,

        "disk_percent": psutil.disk_usage('/').percent,

        "database_size_mb": get_database_size() / 1024 / 1024,

        "active_requests": active_request_count(),

        "websocket_connections": active_ws_count(),

        "provider_stats": await get_provider_statistics()

    }

```

---

### **Phase 5: Background Tasks & Auto-Discovery**
*Goal: Continuous operation with automatic provider discovery*

#### 5.1 Background Tasks
**Files:**
- `scheduler.py` - Task scheduling
- `monitoring/scheduler_comprehensive.py` - Enhanced scheduler

**Tasks to Activate:**

```python

# In api_server_extended.py



@app.on_event("startup")

async def start_background_tasks():

    """Start all background tasks"""



    tasks = [

        # Data collection tasks

        asyncio.create_task(collect_prices_every_5min()),

        asyncio.create_task(collect_defi_data_every_hour()),

        asyncio.create_task(fetch_news_every_30min()),

        asyncio.create_task(analyze_sentiment_every_hour()),



        # Health & monitoring tasks

        asyncio.create_task(health_check_every_5min()),

        asyncio.create_task(broadcast_stats_every_5min()),

        asyncio.create_task(cleanup_old_logs_daily()),

        asyncio.create_task(backup_database_daily()),

        asyncio.create_task(send_diagnostics_hourly()),



        # Discovery tasks (optional)

        asyncio.create_task(discover_new_providers_daily()),

    ]



    logger.info(f"Started {len(tasks)} background tasks")



# Scheduled tasks with cron-like syntax

TASK_SCHEDULE = {

    "collect_prices": "*/5 * * * *",  # Every 5 minutes

    "collect_defi": "0 * * * *",      # Hourly

    "fetch_news": "*/30 * * * *",     # Every 30 minutes

    "sentiment_analysis": "0 * * * *", # Hourly

    "health_check": "*/5 * * * *",    # Every 5 minutes

    "backup_database": "0 2 * * *",   # Daily at 2 AM

    "cleanup_logs": "0 3 * * *",      # Daily at 3 AM

}

```

#### 5.2 Auto-Discovery Service
**Files:**
- `backend/services/auto_discovery_service.py` - Discovery logic

**Implementation:**

```python

# Enable in environment

ENABLE_AUTO_DISCOVERY=true

AUTO_DISCOVERY_INTERVAL_HOURS=24



class AutoDiscoveryService:

    """Automatically discover new crypto API providers"""



    async def discover_providers(self) -> List[Provider]:

        """Scan for new providers"""

        discovered = []



        sources = [

            self.scan_github_repositories,

            self.scan_api_directories,

            self.scan_rss_feeds,

            self.query_existing_apis,

        ]



        for source in sources:

            try:

                providers = await source()

                discovered.extend(providers)

                logger.info(f"Discovered {len(providers)} from {source.__name__}")

            except Exception as e:

                logger.error(f"Discovery error in {source.__name__}: {e}")



        # Validate and store

        valid = []

        for provider in discovered:

            if await self.validate_provider(provider):

                await self.store_provider(provider)

                valid.append(provider)



        return valid



    async def scan_github_repositories(self):

        """Search GitHub for crypto API projects"""

        # Query GitHub API for relevant repos

        # Extract API endpoints

        # Return as Provider objects

        pass



    async def validate_provider(self, provider: Provider) -> bool:

        """Test if provider is actually available"""

        try:

            async with aiohttp.ClientSession() as session:

                async with session.get(

                    provider.base_url,

                    timeout=aiohttp.ClientTimeout(total=5)

                ) as resp:

                    return resp.status < 500

        except:

            return False



# Start discovery on demand

@app.post("/api/discovery/run")

async def trigger_discovery(background: bool = True):

    """Trigger provider discovery"""

    discovery_service = AutoDiscoveryService()



    if background:

        asyncio.create_task(discovery_service.discover_providers())

        return {"status": "Discovery started in background"}

    else:

        providers = await discovery_service.discover_providers()

        return {"discovered": len(providers), "providers": providers}

```

---

## 🐳 HuggingFace Spaces Deployment

### Configuration for HF Spaces

**`spaces/app.py` (Entry point):**
```python

import os

import sys



# Set environment for HF Spaces

os.environ['HF_SPACE'] = 'true'

os.environ['PORT'] = '7860'  # HF Spaces default port



# Import and start the main FastAPI app

from api_server_extended import app



if __name__ == "__main__":

    import uvicorn



    uvicorn.run(

        app,

        host="0.0.0.0",

        port=7860,

        log_level="info"

    )

```

**`spaces/requirements.txt`:**
```

fastapi==0.109.0

uvicorn[standard]==0.27.0

aiohttp==3.9.1

pydantic==2.5.3

websockets==12.0

sqlalchemy==2.0.23

torch==2.1.1

transformers==4.35.2

huggingface-hub==0.19.1

slowapi==0.1.9

python-jose==3.3.0

psutil==5.9.6

aiofiles==23.2.1

```

**`spaces/README.md`:**
```markdown

# Crypto-DT-Source on HuggingFace Spaces



Real-time cryptocurrency data aggregation service with 200+ providers.



## Features

- Real-time price data

- AI sentiment analysis

- 50+ REST endpoints

- WebSocket streaming

- Provider health monitoring

- Historical data storage



## API Documentation

- Swagger UI: https://[your-space-url]/docs

- ReDoc: https://[your-space-url]/redoc



## Quick Start

```bash

curl https://[your-space-url]/api/health

curl https://[your-space-url]/api/prices?symbols=BTC,ETH

curl https://[your-space-url]/api/sentiment

```

## WebSocket Connection
```javascript

const ws = new WebSocket('wss://[your-space-url]/ws');

ws.onmessage = (event) => console.log(JSON.parse(event.data));

```
```



---



## ✅ Activation Checklist



### Phase 1: Data Integration

- [ ] Modify `/api/market` to return real CoinGecko data

- [ ] Modify `/api/prices` to fetch real provider data

- [ ] Modify `/api/trending` to return live trending coins

- [ ] Implement `/api/ohlcv` with Binance data

- [ ] Implement `/api/defi` with DeFi Llama data

- [ ] Remove all hardcoded mock data

- [ ] Test all endpoints with real data

- [ ] Add caching layer (5-30 min TTL based on endpoint)



### Phase 2: Database

- [ ] Run database migrations

- [ ] Create all required tables

- [ ] Implement write pattern for real data storage

- [ ] Implement read pattern for historical queries

- [ ] Add database health check

- [ ] Test data persistence across restarts

- [ ] Implement cleanup tasks for old data



### Phase 3: AI & Sentiment

- [ ] Install transformers and torch

- [ ] Load HuggingFace sentiment model

- [ ] Implement sentiment analysis endpoint

- [ ] Implement crypto-specific sentiment classification

- [ ] Create news sentiment pipeline

- [ ] Store sentiment scores in database

- [ ] Test model inference latency



### Phase 4: Security

- [ ] Generate JWT secret key

- [ ] Implement authentication middleware

- [ ] Create API key management system

- [ ] Implement rate limiting on all endpoints

- [ ] Add tier-based rate limits (free/pro/enterprise)

- [ ] Create `/api/auth/token` endpoint

- [ ] Test authentication on protected endpoints

- [ ] Set up HTTPS certificate for CORS



### Phase 5: Background Tasks

- [ ] Activate all scheduled tasks

- [ ] Set up price collection (every 5 min)

- [ ] Set up DeFi data collection (hourly)

- [ ] Set up news fetching (every 30 min)

- [ ] Set up sentiment analysis (hourly)

- [ ] Set up health checks (every 5 min)

- [ ] Set up database backup (daily)

- [ ] Set up log cleanup (daily)



### Phase 6: HF Spaces Deployment

- [ ] Create `spaces/` directory

- [ ] Create `spaces/app.py` entry point

- [ ] Create `spaces/requirements.txt`

- [ ] Create `spaces/README.md`

- [ ] Configure environment variables

- [ ] Test locally with Docker

- [ ] Push to HF Spaces

- [ ] Verify all endpoints accessible

- [ ] Monitor logs and metrics

- [ ] Set up auto-restart on failure



---



## 🔧 Environment Variables



```bash

# Core

PORT=7860

ENVIRONMENT=production

LOG_LEVEL=info



# Database

DATABASE_URL=sqlite:///data/crypto_aggregator.db

DATABASE_POOL_SIZE=20



# Security

JWT_SECRET_KEY=your-secret-key-change-in-production

API_KEY_SALT=your-salt-key



# HuggingFace Spaces

HF_SPACE=true

HF_SPACE_URL=https://huggingface.co/spaces/your-username/crypto-dt-source



# Features

ENABLE_AUTO_DISCOVERY=true

ENABLE_SENTIMENT_ANALYSIS=true

ENABLE_BACKGROUND_TASKS=true



# Rate Limiting

FREE_TIER_LIMIT=30/minute

PRO_TIER_LIMIT=300/minute



# Caching

CACHE_TTL_PRICES=300  # 5 minutes

CACHE_TTL_DEFI=3600   # 1 hour

CACHE_TTL_NEWS=1800   # 30 minutes



# Providers (optional API keys)

ETHERSCAN_API_KEY=

BSCSCAN_API_KEY=

COINGECKO_API_KEY=

```

---

## 📊 Expected Performance

After implementation:

| Metric | Target | Current |
|--------|--------|---------|
| Price endpoint response time | < 500ms | N/A |
| Sentiment analysis latency | < 2s | N/A |
| WebSocket update frequency | Real-time | ✅ Working |
| Database query latency | < 100ms | N/A |
| Provider failover time | < 2s | ✅ Working |
| Authentication overhead | < 50ms | N/A |
| Concurrent connections supported | 1000+ | ✅ Tested |

---

## 🚨 Troubleshooting

### Models not loading on HF Spaces
```bash

# HF Spaces has limited disk space

# Use distilbert models (smaller) instead of full models

# Or cache models in requirements

pip install --no-cache-dir transformers torch

```

### Database file too large
```bash

# Implement cleanup task

# Keep only 90 days of data

# Archive old data to S3

```

### Rate limiting too aggressive
```bash

# Adjust limits in environment

FREE_TIER_LIMIT=100/minute

PRO_TIER_LIMIT=500/minute

```

### WebSocket disconnections
```bash

# Increase heartbeat frequency

WEBSOCKET_HEARTBEAT_INTERVAL=10  # seconds

WEBSOCKET_HEARTBEAT_TIMEOUT=30   # seconds

```

---

## 📚 Next Steps

1. **Review Phase 1-2**: Data integration and database
2. **Review Phase 3-4**: AI and security implementations
3. **Review Phase 5-6**: Background tasks and HF deployment
4. **Execute implementation** following the checklist
5. **Test thoroughly** before production deployment
6. **Monitor metrics** and adjust configurations
7. **Collect user feedback** and iterate

---

## 🎯 Success Criteria

Project is **production-ready** when:

✅ All 50+ endpoints return real data
✅ Database stores 90 days of historical data
✅ Sentiment analysis runs on real ML models
✅ Authentication required on all protected endpoints
✅ Rate limiting enforced across all tiers
✅ Background tasks running without errors
✅ Health check returns all components OK
✅ WebSocket clients can stream real-time data
✅ Auto-discovery discovers new providers
✅ Deployed on HuggingFace Spaces successfully
✅ Average response time < 1 second
✅ Zero downtime during operation

---

**Document Version:** 2.0
**Last Updated:** 2025-11-15
**Maintained by:** Claude Code AI
**Status:** Ready for Implementation