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add to gradio ui
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@@ -163,8 +163,10 @@ MODEL_MAPPINGS = {
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"claude-3-7-sonnet-20250219": "claude-3-7-sonnet-20250219",
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},
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"uitars": {
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# UI-TARS models default to custom endpoint
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"default": "ByteDance-Seed/UI-TARS-1.5-7B",
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# UI-TARS models using MLXVLM provider
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"default": "mlx-community/UI-TARS-1.5-7B-4bit",
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"UI-TARS-1.5-7B-4bit": "mlx-community/UI-TARS-1.5-7B-4bit",
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"UI-TARS-1.5-7B-6bit": "mlx-community/UI-TARS-1.5-7B-6bit"
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},
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"ollama": {
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# For Ollama models, we keep the original name
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@@ -287,8 +289,16 @@ def get_provider_and_model(model_name: str, loop_provider: str) -> tuple:
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model_name_to_use = cleaned_model_name
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# agent_loop remains AgentLoop.OMNI
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elif agent_loop == AgentLoop.UITARS:
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provider = LLMProvider.OAICOMPAT
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model_name_to_use = MODEL_MAPPINGS["uitars"]["default"] # Default
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# For UITARS, use MLXVLM provider for the MLX models, OAICOMPAT for custom
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if model_name == "Custom model...":
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provider = LLMProvider.OAICOMPAT
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model_name_to_use = "tgi"
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else:
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provider = LLMProvider.MLXVLM
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# Get the model name from the mappings or use as-is if not found
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model_name_to_use = MODEL_MAPPINGS["uitars"].get(
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model_name, model_name if model_name else MODEL_MAPPINGS["uitars"]["default"]
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)
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else:
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# Default to OpenAI if unrecognized loop
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provider = LLMProvider.OPENAI
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@@ -558,7 +568,11 @@ def create_gradio_ui(
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"OPENAI": openai_models,
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"ANTHROPIC": anthropic_models,
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"OMNI": omni_models + ["Custom model..."], # Add custom model option
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"UITARS": ["Custom model..."], # UI-TARS options
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"UITARS": [
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"mlx-community/UI-TARS-1.5-7B-4bit",
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"mlx-community/UI-TARS-1.5-7B-6bit",
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"Custom model..."
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], # UI-TARS options with MLX models
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}
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# --- Apply Saved Settings (override defaults if available) ---
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