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PROVIDER_MODEL_MAP = {
"openai": [
"gpt-4.1-mini",
"gpt-5",
"gpt-5-mini",
"gpt-5-nano",
],
"anthropic": [
"anthropic.claude-haiku-4-5-20251001-v1:0",
"anthropic.claude-sonnet-4-5-20250929-v1:0",
"anthropic.claude-opus-4-20250514-v1:0",
],
"google": [
"gemini-3-pro-preview",
"gemini-2.5-pro",
],
}
MODEL_TO_PROVIDER = {
model: provider
for provider, models in PROVIDER_MODEL_MAP.items()
for model in models
}
# Model display names with categories
MODEL_DISPLAY_NAMES = {
"gpt-4.1-mini": "OpenAI - GPT-4.1 Mini",
"gpt-5": "OpenAI - GPT-5",
"gpt-5-mini": "OpenAI - GPT-5 Mini",
"gpt-5-nano": "OpenAI - GPT-5 Nano",
"anthropic.claude-haiku-4-5-20251001-v1:0": "AWS Bedrock - Anthropic - Claude Haiku 4.5",
"anthropic.claude-sonnet-4-5-20250929-v1:0": "AWS Bedrock - Anthropic - Claude Sonnet 4.5",
"anthropic.claude-opus-4-20250514-v1:0": "AWS Bedrock - Anthropic - Claude Opus 4",
"gemini-3-pro-preview": "Google - Gemini 3 Pro (Preview)",
"gemini-2.5-pro": "Google - Gemini 2.5 Pro",
}
MODEL_CHOICES = [model for models in PROVIDER_MODEL_MAP.values() for model in models]
# Inference profile ARNs for provisioned throughput models
# Read from environment variables (Hugging Face secrets)
import os
MODEL_TO_INFERENCE_PROFILE_ARN = {}
# Claude Haiku 4.5
haiku_arn = os.getenv("BEDROCK_HAIKU_4_5_ARN", "").strip()
if haiku_arn:
MODEL_TO_INFERENCE_PROFILE_ARN["anthropic.claude-haiku-4-5-20251001-v1:0"] = haiku_arn
# Claude Sonnet 4.5
sonnet_arn = os.getenv("BEDROCK_SONNET_4_5_ARN", "").strip()
if sonnet_arn:
MODEL_TO_INFERENCE_PROFILE_ARN["anthropic.claude-sonnet-4-5-20250929-v1:0"] = sonnet_arn
# Claude Opus 4
opus_arn = os.getenv("BEDROCK_OPUS_4_ARN", "").strip()
if opus_arn:
MODEL_TO_INFERENCE_PROFILE_ARN["anthropic.claude-opus-4-20250514-v1:0"] = opus_arn
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