import os
import logging
from typing import List, Optional, Union, Dict
import base64
import asyncio

# Assuming an external client for Gemini Multimodal Embedding is available
# This is a PoC wrapper that simulates its behavior.

logger = logging.getLogger(__name__)

class MultimodalEmbedder:
    """
    Client for generating multimodal embeddings using Gemini's capabilities.
    This is a Proof-of-Concept wrapper, simulating the Gemini Multimodal Embedding behavior.
    """
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.vector_size = 1408 # Example size for Gemini Multimodal Embedding
        if not self.api_key or self.api_key == "YOUR_GEMINI_API_KEY":
            logger.warning("GEMINI_API_KEY not set for MultimodalEmbedder. Running in simulated mode.")
        logger.info(f"MultimodalEmbedder initialized (PoC mode), vector_size: {self.vector_size}")

    async def generate_embedding(
        self,
        text_input: Optional[str] = None,
        image_input: Optional[Union[bytes, str]] = None # bytes for raw, str for base64
    ) -> List[float]:
        """
        Generates a multimodal embedding from text and/or image input.
        """
        if not text_input and not image_input:
            raise ValueError("At least one of text_input or image_input must be provided.")

        # Simulate API call latency
        await asyncio.sleep(0.05)

        # For PoC, generate a dummy embedding. In real scenario, would call Gemini API.
        # The dummy embedding will be based on text length for some determinism
        seed = 0
        if text_input:
            seed += len(text_input)
        if image_input:
            if isinstance(image_input, bytes):
                seed += len(image_input)
            else:
                seed += len(image_input) # Base64 string length

        # Create a deterministic, but dummy, vector
        embedding = [(float(i + seed % 100) / 1000.0) % 1.0 for i in range(self.vector_size)]
        
        logger.debug(f"Simulated multimodal embedding generated. Text length: {len(text_input) if text_input else 0}, Image present: {image_input is not None}")
        return embedding

    def get_vector_size(self) -> int:
        return self.vector_size

# Example usage (for testing)
async def main():
    api_key = os.environ.get("GEMINI_API_KEY", "YOUR_GEMINI_API_KEY_HERE")
    embedder = MultimodalEmbedder(api_key=api_key)

    text_embedding = await embedder.generate_embedding(text_input="This is a test sentence.")
    print(f"Text embedding size: {len(text_embedding)}")

    image_data = b"fake_image_bytes"
    image_embedding = await embedder.generate_embedding(image_input=image_data)
    print(f"Image embedding size: {len(image_embedding)}")

    multimodal_embedding = await embedder.generate_embedding(
        text_input="Describe the image.",
        image_input=image_data
    )
    print(f"Multimodal embedding size: {len(multimodal_embedding)}")

if __name__ == "__main__":
    import os
    asyncio.run(main())