import os
from pathlib import Path


client_key = os.getenv("OPENAI_API_KEY", "")
base_url = os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
api_key = client_key
openai_api_key = api_key
topk = int(os.getenv("CHARTPIPELINE_TOPK", "5"))

resource_path = os.getenv("CHARTPIPELINE_RESOURCE_PATH", str(Path("resources").resolve()))
data_resource_dirs = [
    path
    for path in os.getenv("CHARTPIPELINE_DATA_RESOURCE_DIRS", str(Path("examples").resolve())).split(os.pathsep)
    if path
]
data_resource_path = data_resource_dirs[0] if data_resource_dirs else str(Path("examples").resolve())

color_resource_path = os.path.join(resource_path, "color")
image_resource_path = os.path.join(resource_path, "image")
text_resource_path = os.path.join(resource_path, "text")
result_resource_path = os.path.join(resource_path, "result")

text_json_path = os.path.join(text_resource_path, "training_data.json")
text_index_path = os.path.join(text_resource_path, "faiss_infographics.index")
text_data_path = os.path.join(text_resource_path, "infographics_data.npy")
color_index_path = os.path.join(color_resource_path, "color_palette.index")
color_data_path = os.path.join(color_resource_path, "color_palette.json")
image_index_path = os.path.join(image_resource_path, "image_recommendation.index")
image_data_path = os.path.join(image_resource_path, "image_recommendation.json")
image_list_path = os.path.join(image_resource_path, "result_map.txt")

model_resource_path = os.path.join(resource_path, "models")
embed_model_path = os.getenv(
    "CHARTPIPELINE_EMBED_MODEL_PATH",
    os.path.join(
        model_resource_path,
        "models--sentence-transformers--all-MiniLM-L6-v2",
        "snapshots",
        "fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9",
    ),
)
sentence_transformer_path = embed_model_path

RENDER_LONGEST_SIDE = int(os.getenv("CHARTPIPELINE_RENDER_LONGEST_SIDE", "3860"))