```python
from fastapi import APIRouter, UploadFile, File, HTTPException
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from typing import Optional, List
from datetime import datetime
import uuid
from .models_registry import ModelsRegistry
from .signal_engine import SignalEngine

router = APIRouter()

# Initialize models and engine
models = ModelsRegistry()
engine = SignalEngine(models)

class MarketRequest(BaseModel):
    pair: str
    timeframe: str
    exchange: Optional[str] = "binance"

class SignalResponse(BaseModel):
    direction: str  # "LONG", "SHORT", or "NEUTRAL"
    entry_zone: List[float]
    stop_loss: float
    take_profit_levels: List[float]
    timeframe_inferred: str
    confidence: float  # 0-100
    explanation: str
    signal_id: str
    timestamp: datetime
    
class WebhookTestRequest(BaseModel):
    webhook_url: Optional[str]
    signal: SignalResponse

@router.post("/analyze/screenshot", response_model=SignalResponse)
async def analyze_screenshot(file: UploadFile = File(...)):
    try:
        # Generate a unique ID for this signal
        signal_id = str(uuid.uuid4())
        
        # In a real implementation, we would:
        # 1. Process the image with vision models
        # 2. Extract text with OCR if needed
        # 3. Analyze with LLM
        # For now, return a mock response
        return SignalResponse(
            direction="LONG",
            entry_zone=[42000, 42500],
            stop_loss=41500,
            take_profit_levels=[43500, 44500],
            timeframe_inferred="1h",
            confidence=75.5,
            explanation="The chart shows a bullish breakout from a descending wedge pattern with increasing volume. Key support at 41,500 and first target at 43,500.",
            signal_id=signal_id,
            timestamp=datetime.now()
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/analyze/market", response_model=SignalResponse)
async def analyze_market(request: MarketRequest):
    try:
        signal_id = str(uuid.uuid4())
        
        # In a real implementation, we would:
        # 1. Fetch market data
        # 2. Analyze with time-series models
        # 3. Combine with technical indicators
        return SignalResponse(
            direction="SHORT" if "BTC" in request.pair else "NEUTRAL",
            entry_zone=[2850, 2900] if "ETH" in request.pair else [42000, 42500],
            stop_loss=2950 if "ETH" in request.pair else 43500,
            take_profit_levels=[2700, 2600] if "ETH" in request.pair else [40000, 39000],
            timeframe_inferred=request.timeframe,
            confidence=68.2,
            explanation=f"Market shows overbought conditions on {request.timeframe} timeframe with RSI above 70. Expecting a pullback to support levels.",
            signal_id=signal_id,
            timestamp=datetime.now()
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@router.get("/history", response_model=List[SignalResponse])
async def get_history(limit: int = 20):
    # In a real implementation, this would fetch from a database
    return []

@router.post("/signals/webhook-test")
async def test_webhook(request: WebhookTestRequest):
    # In a real implementation, this would send to the webhook URL
    return {"status": "success", "message": "Webhook test successful (mock response)"}
```