config.py

"""
Configuration module for Universal MCP Client
Enhanced with HuggingFace Inference Provider support
"""
import os
from dataclasses import dataclass
from typing import Optional, Dict, List
import logging

# Set up enhanced logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

@dataclass
class MCPServerConfig:
    """Configuration for an MCP server connection"""
    name: str
    url: str
    description: str
    space_id: Optional[str] = None

class AppConfig:
    """Application configuration settings"""
    
    # API Configuration
    ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")
    HF_TOKEN = os.getenv("HF_TOKEN")
    
    # Model Configuration
    CLAUDE_MODEL = "claude-sonnet-4-20250514"
    MAX_TOKENS = 2048
    
    # MCP Configuration
    MCP_BETA_VERSION = "mcp-client-2025-04-04"
    MCP_TIMEOUT_SECONDS = 20.0
    
    # UI Configuration
    GRADIO_THEME = "citrus"
    DEBUG_MODE = True
    
    # File Support
    SUPPORTED_IMAGE_EXTENSIONS = ['.png', '.jpg', '.jpeg', '.gif', '.webp']
    SUPPORTED_AUDIO_EXTENSIONS = ['.mp3', '.wav', '.ogg', '.m4a', '.flac']
    SUPPORTED_VIDEO_EXTENSIONS = ['.mp4', '.avi', '.mov']
    SUPPORTED_DOCUMENT_EXTENSIONS = ['.pdf', '.txt', '.docx']
    
    # Inference Providers Configuration
    INFERENCE_PROVIDERS = {
        "sambanova": {
            "name": "SambaNova",
            "description": "Ultra-fast inference on optimized hardware",
            "supports_tools": True,
            "models": [
                "meta-llama/Llama-3.3-70B-Instruct",
                "deepseek-ai/DeepSeek-R1-0528", 
                "meta-llama/Llama-4-Maverick-17B-128E-Instruct",
                "intfloat/e5-mistral-7b-instruct"
            ]
        },
        "together": {
            "name": "Together AI",
            "description": "High-performance inference for open models",
            "supports_tools": True,
            "models": [
                "deepseek-ai/DeepSeek-V3-0324",
                "Qwen/Qwen2.5-72B-Instruct",
                "meta-llama/Llama-3.1-8B-Instruct",
                "black-forest-labs/FLUX.1-dev"
            ]
        },
        "replicate": {
            "name": "Replicate",
            "description": "Run AI models in the cloud",
            "supports_tools": True,
            "models": [
                "meta/llama-2-70b-chat",
                "mistralai/mixtral-8x7b-instruct-v0.1",
                "black-forest-labs/flux-schnell"
            ]
        },
        "groq": {
            "name": "Groq",
            "description": "Ultra-low latency LPU inference",
            "supports_tools": True,
            "models": [
                "meta-llama/Llama-4-Scout-17B-16E-Instruct",
                "llama-3.1-70b-versatile",
                "mixtral-8x7b-32768"
            ]
        },
        "fal-ai": {
            "name": "fal.ai",
            "description": "Fast AI model inference",
            "supports_tools": True,
            "models": [
                "meta-llama/Llama-3.1-8B-Instruct",
                "black-forest-labs/flux-pro"
            ]
        },
        "fireworks-ai": {
            "name": "Fireworks AI",
            "description": "Production-ready inference platform",
            "supports_tools": True,
            "models": [
                "accounts/fireworks/models/llama-v3p1-70b-instruct",
                "accounts/fireworks/models/mixtral-8x7b-instruct"
            ]
        },
        "cohere": {
            "name": "Cohere",
            "description": "Enterprise-grade language AI",
            "supports_tools": True,
            "models": [
                "command-r-plus",
                "command-r",
                "command"
            ]
        },
        "hf-inference": {
            "name": "HF Inference",
            "description": "Hugging Face serverless inference",
            "supports_tools": True,
            "models": [
                "meta-llama/Llama-3.2-11B-Vision-Instruct",
                "microsoft/DialoGPT-medium",
                "intfloat/multilingual-e5-large"
            ]
        }
    }
    
    @classmethod
    def get_all_media_extensions(cls):
        """Get all supported media file extensions"""
        return (cls.SUPPORTED_IMAGE_EXTENSIONS + 
                cls.SUPPORTED_AUDIO_EXTENSIONS + 
                cls.SUPPORTED_VIDEO_EXTENSIONS)
    
    @classmethod
    def is_image_file(cls, file_path: str) -> bool:
        """Check if file is an image"""
        return any(ext in file_path.lower() for ext in cls.SUPPORTED_IMAGE_EXTENSIONS)
    
    @classmethod
    def is_audio_file(cls, file_path: str) -> bool:
        """Check if file is an audio file"""
        return any(ext in file_path.lower() for ext in cls.SUPPORTED_AUDIO_EXTENSIONS)
    
    @classmethod
    def is_video_file(cls, file_path: str) -> bool:
        """Check if file is a video file"""
        return any(ext in file_path.lower() for ext in cls.SUPPORTED_VIDEO_EXTENSIONS)
    
    @classmethod
    def is_media_file(cls, file_path: str) -> bool:
        """Check if file is any supported media type"""
        return any(ext in file_path.lower() for ext in cls.get_all_media_extensions())

    @classmethod
    def get_provider_models(cls, provider: str) -> List[str]:
        """Get available models for a specific provider"""
        return cls.INFERENCE_PROVIDERS.get(provider, {}).get("models", [])
    
    @classmethod
    def get_all_providers(cls) -> Dict[str, Dict]:
        """Get all available inference providers"""
        return cls.INFERENCE_PROVIDERS

# Check for dependencies
try:
    import httpx
    HTTPX_AVAILABLE = True
except ImportError:
    HTTPX_AVAILABLE = False
    logger.warning("httpx not available - file upload functionality limited")

try:
    from huggingface_hub import InferenceClient
    HF_INFERENCE_AVAILABLE = True
except ImportError:
    HF_INFERENCE_AVAILABLE = False
    logger.warning("huggingface_hub not available - inference provider functionality limited")

# CSS Configuration
CUSTOM_CSS = """
/* Hide Gradio footer */
footer {
    display: none !important;
}
/* Make chatbot expand to fill available space */
.gradio-container {
    height: 100vh !important;
}
/* Ensure proper flex layout */
.main-content {
    display: flex;
    flex-direction: column;
    height: 100%;
}
/* Input area stays at bottom with minimal padding */
.input-area {
    margin-top: auto;
    padding-top: 0.25rem !important;
    padding-bottom: 0 !important;
    margin-bottom: 0 !important;
}
/* Reduce padding around chatbot */
.chatbot {
    margin-bottom: 0 !important;
    padding-bottom: 0 !important;
}
/* Provider selection styling */
.provider-selection {
    border: 1px solid #e0e0e0;
    border-radius: 8px;
    padding: 10px;
    margin: 5px 0;
}
.anthropic-config {
    background-color: #f8f9fa;
    border-left: 4px solid #28a745;
}
.hf-config {
    background-color: #fff8e1;
    border-left: 4px solid #ff9800;
}
"""