# Configuration settings for the Hugging Face Model Deployer

import os
from typing import Dict, Any

# App Configuration
APP_CONFIG = {
    "title": "Hugging Face Model Deployer",
    "description": "Complete guide to deploy your models with real TTS/STT integration",
    "version": "1.0.0",
    "author": "Hugging Face Deployer Team"
}

# Hugging Face API Configuration
HF_CONFIG = {
    "base_url": "https://huggingface.co",
    "api_url": "/static-proxy?url=https%3A%2F%2Fapi-inference.huggingface.co",
    "inference_endpoint_url": "https://api.endpoints.huggingface.cloud"
}

# Default Model Settings
DEFAULT_MODELS = {
    "text-generation": [
        "microsoft/DialoGPT-medium",
        "facebook/opt-125m",
        "EleutherAI/gpt-neo-125m"
    ],
    "text2text-generation": [
        "google/flan-t5-small",
        "facebook/bart-large-cnn"
    ],
    "text-to-speech": [
        "microsoft/speecht5_tts",
        "facebook/tts_transformer-en-ljspeech"
    ],
    "automatic-speech-recognition": [
        "openai/whisper-tiny",
        "facebook/wav2vec2-base-960h"
    ]
}

# Deployment Settings
DEPLOYMENT_CONFIG = {
    "default_hardware": "cpu",
    "default_scale": 1,
    "default_task": "text-generation",
    "max_retries": 3,
    "timeout": 300,  # 5 minutes
    "polling_interval": 10  # 10 seconds
}

# UI Settings
UI_CONFIG = {
    "theme": "soft",
    "css_file": "styles.css",
    "max_file_size": "200MB",
    "show_tips": True,
    "auto_save": True
}

# Tutorial Content
TUTORIAL_STEPS = [
    {
        "title": "Authentication",
        "description": "Connect to your Hugging Face account",
        "code": """
from huggingface_hub import login

# Login with your token
login(token="your_token_here")
        """
    },
    {
        "title": "Model Selection",
        "description": "Choose or create a model to deploy",
        "code": """
from huggingface_hub import HfApi

api = HfApi()
models = list(api.list_models(author="your_username"))
        """
    },
    {
        "title": "Deployment",
        "description": "Deploy your model to production",
        "code": """
endpoint = api.create_inference_endpoint(
    name="my-endpoint",
    model="your-username/your-model",
    task="text-generation"
)
        """
    },
    {
        "title": "Testing",
        "description": "Test your deployed model",
        "code": """
import requests

response = requests.post(
    endpoint_url,
    headers={"Authorization": f"Bearer {token}"},
    json={"inputs": "Hello!"}
)
        """
    },
    {
        "title": "TTS/STT Integration",
        "description": "Add voice capabilities to your model",
        "code": """
from transformers import pipeline

tts = pipeline("text-to-speech", model="microsoft/speecht5_tts")
stt = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")
        """
    }
]

# Error Messages
ERROR_MESSAGES = {
    "auth_failed": "Authentication failed. Please check your token.",
    "model_not_found": "Model not found. Please check the model ID.",
    "deployment_failed": "Deployment failed. Please try again.",
    "endpoint_error": "Endpoint error. Please check the URL and try again.",
    "network_error": "Network error. Please check your connection.",
    "permission_denied": "Permission denied. Please check your token permissions."
}

# Success Messages
SUCCESS_MESSAGES = {
    "auth_success": "Successfully authenticated!",
    "deployment_success": "Model deployed successfully!",
    "model_created": "Model repository created!",
    "upload_success": "Files uploaded successfully!",
    "endpoint_ready": "Endpoint is ready for use!"
}

# Environment Variables
ENV_VARS = [
    "HF_TOKEN",
    "HF_USERNAME",
    "SPACE_ID",
    "GRADIO_SERVER_NAME",
    "GRADIO_SERVER_PORT"
]

# Feature Flags
FEATURES = {
    "gpu_support": True,
    "model_upload": True,
    "voice_chat": True,
    "monitoring": True,
    "advanced_settings": True
}