"""
Pre-download Hugging Face models during Docker image build.

This script loads the models to trigger download and caching.
"""
import os
import sys

print("🔽 Downloading Hugging Face models during build...")

# Model configurations from settings.yaml
EMBEDDING_MODEL = "BAAI/bge-m3"
RERANKER_MODEL = "BAAI/bge-reranker-v2-m3"

try:
    print(f"📦 Downloading embedding model: {EMBEDDING_MODEL}")
    from langchain_community.embeddings import HuggingFaceEmbeddings
    
    # Load embedding model (will download if not cached)
    embeddings = HuggingFaceEmbeddings(
        model_name=EMBEDDING_MODEL,
        model_kwargs={"device": "cpu"},  # Use CPU during build
        encode_kwargs={"normalize_embeddings": True},
        show_progress=True,
    )
    
    # Trigger actual download by encoding a small text
    test_text = "test"
    _ = embeddings.embed_query(test_text)
    print(f"✅ Embedding model downloaded: {EMBEDDING_MODEL}")

except Exception as e:
    print(f"⚠️ Warning: Could not download embedding model: {e}")
    # Don't exit on error - allow build to continue (model will download at runtime)
    pass

try:
    print(f"📦 Downloading reranker model: {RERANKER_MODEL}")
    from sentence_transformers import CrossEncoder
    
    # Load reranker model (will download if not cached)
    reranker = CrossEncoder(RERANKER_MODEL)
    
    # Trigger actual download by running inference
    test_pairs = [("test query", "test document")]
    _ = reranker.predict(test_pairs)
    print(f"✅ Reranker model downloaded: {RERANKER_MODEL}")

except Exception as e:
    print(f"⚠️ Warning: Could not download reranker model: {e}")
    # Don't exit on error - allow build to continue (model will download at runtime)
    pass

print("✅ All models downloaded and cached successfully!")