```python
from typing import Optional, Dict, Any
from huggingface_hub import InferenceClient

class ModelsRegistry:
    def __init__(self):
        # Initialize with default models
        self.models = {
            "vision_feature_extractor": "google/vit-base-patch16-224",
            "ocr": "microsoft/trocr-base-stage1",
            "sentiment": "yiyanghkust/finbert-tone",
            "timeseries": "huggingface/time-series-transformer-tiny",
            "general_llm": "mistralai/Mistral-7B-v0.1"
        }
        self.client = InferenceClient()

    def get_model(self, model_name: str) -> Optional[Dict[str, Any]]:
        return self.models.get(model_name)

    async def analyze_sentiment(self, text: str) -> Dict[str, float]:
        """Get sentiment scores using HF model"""
        try:
            result = await self.client.post(
                f"https://api-inference.huggingface.co/models/{self.models['sentiment']}",
                json={"inputs": text}
            )
            return {item['label']: item['score'] for item in result}
        except Exception:
            return {"neutral": 0.5, "positive": 0.25, "negative": 0.25}

    async def analyze_image(self, image_data: bytes) -> Dict[str, Any]:
        """Get image features using HF model"""
        try:
            result = await self.client.post(
                f"https://api-inference.huggingface.co/models/{self.models['vision_feature_extractor']}",
                data=image_data
            )
            return {"features": result}
        except Exception:
            return {"features": []}

    async def generate_text(self, prompt: str) -> str:
        """Get text generation from HF model"""
        try:
            result = await self.client.post(
                f"https://api-inference.huggingface.co/models/{self.models['general_llm']}",
                json={"inputs": prompt}
            )
            return result[0]['generated_text']
        except Exception:
            return "Model unavailable - using fallback response"
```