Delete core/hf_inference.py

core/hf_inference.py

@@ -1,602 +0,0 @@
-"""
-HuggingFace Inference Client
-Design System Extractor v2
-
-Handles all LLM inference calls using HuggingFace Inference API.
-Supports diverse models from different providers for specialized tasks.
-"""
-
-import os
-from typing import Optional, AsyncGenerator
-from dataclasses import dataclass
-from huggingface_hub import InferenceClient, AsyncInferenceClient
-
-from config.settings import get_settings
-
-
-@dataclass
-class ModelInfo:
-    """Information about a model."""
-    model_id: str
-    provider: str
-    context_length: int
-    strengths: list[str]
-    best_for: str
-    tier: str  # "free", "pro", "pro+"
-
-
-# =============================================================================
-# COMPREHENSIVE MODEL REGISTRY — Organized by Provider
-# =============================================================================
-
-AVAILABLE_MODELS = {
-    # =========================================================================
-    # META — Llama Family (Best for reasoning)
-    # =========================================================================
-    "meta-llama/Llama-3.1-405B-Instruct": ModelInfo(
-        model_id="meta-llama/Llama-3.1-405B-Instruct",
-        provider="Meta",
-        context_length=128000,
-        strengths=["Best reasoning", "Massive knowledge", "Complex analysis"],
-        best_for="Agent 3 (Advisor) — PREMIUM CHOICE",
-        tier="pro+"
-    ),
-    "meta-llama/Llama-3.1-70B-Instruct": ModelInfo(
-        model_id="meta-llama/Llama-3.1-70B-Instruct",
-        provider="Meta",
-        context_length=128000,
-        strengths=["Excellent reasoning", "Long context", "Design knowledge"],
-        best_for="Agent 3 (Advisor) — RECOMMENDED",
-        tier="pro"
-    ),
-    "meta-llama/Llama-3.1-8B-Instruct": ModelInfo(
-        model_id="meta-llama/Llama-3.1-8B-Instruct",
-        provider="Meta",
-        context_length=128000,
-        strengths=["Fast", "Good reasoning for size", "Long context"],
-        best_for="Budget Agent 3 fallback",
-        tier="free"
-    ),
-    
-    # =========================================================================
-    # MISTRAL — European Excellence
-    # =========================================================================
-    "mistralai/Mixtral-8x22B-Instruct-v0.1": ModelInfo(
-        model_id="mistralai/Mixtral-8x22B-Instruct-v0.1",
-        provider="Mistral",
-        context_length=65536,
-        strengths=["Large MoE", "Strong reasoning", "Efficient"],
-        best_for="Agent 3 (Advisor) — Pro alternative",
-        tier="pro"
-    ),
-    "mistralai/Mixtral-8x7B-Instruct-v0.1": ModelInfo(
-        model_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
-        provider="Mistral",
-        context_length=32768,
-        strengths=["Good MoE efficiency", "Solid reasoning"],
-        best_for="Agent 3 (Advisor) — Free tier option",
-        tier="free"
-    ),
-    "mistralai/Mistral-7B-Instruct-v0.3": ModelInfo(
-        model_id="mistralai/Mistral-7B-Instruct-v0.3",
-        provider="Mistral",
-        context_length=32768,
-        strengths=["Fast", "Good instruction following"],
-        best_for="General fallback",
-        tier="free"
-    ),
-    "mistralai/Codestral-22B-v0.1": ModelInfo(
-        model_id="mistralai/Codestral-22B-v0.1",
-        provider="Mistral",
-        context_length=32768,
-        strengths=["Code specialist", "JSON generation", "Structured output"],
-        best_for="Agent 4 (Generator) — RECOMMENDED",
-        tier="pro"
-    ),
-    
-    # =========================================================================
-    # COHERE — Command R Family (Analysis & Retrieval)
-    # =========================================================================
-    "CohereForAI/c4ai-command-r-plus": ModelInfo(
-        model_id="CohereForAI/c4ai-command-r-plus",
-        provider="Cohere",
-        context_length=128000,
-        strengths=["Excellent analysis", "RAG optimized", "Long context"],
-        best_for="Agent 3 (Advisor) — Great for research tasks",
-        tier="pro"
-    ),
-    "CohereForAI/c4ai-command-r-v01": ModelInfo(
-        model_id="CohereForAI/c4ai-command-r-v01",
-        provider="Cohere",
-        context_length=128000,
-        strengths=["Good analysis", "Efficient"],
-        best_for="Agent 3 budget option",
-        tier="free"
-    ),
-    
-    # =========================================================================
-    # GOOGLE — Gemma Family
-    # =========================================================================
-    "google/gemma-2-27b-it": ModelInfo(
-        model_id="google/gemma-2-27b-it",
-        provider="Google",
-        context_length=8192,
-        strengths=["Strong instruction following", "Good balance"],
-        best_for="Agent 2 (Normalizer) — Quality option",
-        tier="pro"
-    ),
-    "google/gemma-2-9b-it": ModelInfo(
-        model_id="google/gemma-2-9b-it",
-        provider="Google",
-        context_length=8192,
-        strengths=["Fast", "Good instruction following"],
-        best_for="Agent 2 (Normalizer) — Balanced",
-        tier="free"
-    ),
-    
-    # =========================================================================
-    # MICROSOFT — Phi Family (Small but Mighty)
-    # =========================================================================
-    "microsoft/Phi-3.5-mini-instruct": ModelInfo(
-        model_id="microsoft/Phi-3.5-mini-instruct",
-        provider="Microsoft",
-        context_length=128000,
-        strengths=["Very fast", "Great structured output", "Long context"],
-        best_for="Agent 2 (Normalizer) — RECOMMENDED",
-        tier="free"
-    ),
-    "microsoft/Phi-3-medium-4k-instruct": ModelInfo(
-        model_id="microsoft/Phi-3-medium-4k-instruct",
-        provider="Microsoft",
-        context_length=4096,
-        strengths=["Fast", "Good for simple tasks"],
-        best_for="Simple naming tasks",
-        tier="free"
-    ),
-    
-    # =========================================================================
-    # QWEN — Alibaba Family
-    # =========================================================================
-    "Qwen/Qwen2.5-72B-Instruct": ModelInfo(
-        model_id="Qwen/Qwen2.5-72B-Instruct",
-        provider="Alibaba",
-        context_length=32768,
-        strengths=["Strong reasoning", "Multilingual", "Good design knowledge"],
-        best_for="Agent 3 (Advisor) — Alternative",
-        tier="pro"
-    ),
-    "Qwen/Qwen2.5-32B-Instruct": ModelInfo(
-        model_id="Qwen/Qwen2.5-32B-Instruct",
-        provider="Alibaba",
-        context_length=32768,
-        strengths=["Good balance", "Multilingual"],
-        best_for="Medium-tier option",
-        tier="pro"
-    ),
-    "Qwen/Qwen2.5-Coder-32B-Instruct": ModelInfo(
-        model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
-        provider="Alibaba",
-        context_length=32768,
-        strengths=["Code specialist", "JSON/structured output"],
-        best_for="Agent 4 (Generator) — Alternative",
-        tier="pro"
-    ),
-    "Qwen/Qwen2.5-7B-Instruct": ModelInfo(
-        model_id="Qwen/Qwen2.5-7B-Instruct",
-        provider="Alibaba",
-        context_length=32768,
-        strengths=["Fast", "Good all-rounder"],
-        best_for="General fallback",
-        tier="free"
-    ),
-    
-    # =========================================================================
-    # DEEPSEEK — Code Specialists
-    # =========================================================================
-    "deepseek-ai/deepseek-coder-33b-instruct": ModelInfo(
-        model_id="deepseek-ai/deepseek-coder-33b-instruct",
-        provider="DeepSeek",
-        context_length=16384,
-        strengths=["Excellent code generation", "JSON specialist"],
-        best_for="Agent 4 (Generator) — Code focused",
-        tier="pro"
-    ),
-    "deepseek-ai/DeepSeek-V2.5": ModelInfo(
-        model_id="deepseek-ai/DeepSeek-V2.5",
-        provider="DeepSeek",
-        context_length=32768,
-        strengths=["Strong reasoning", "Good code"],
-        best_for="Multi-purpose",
-        tier="pro"
-    ),
-    
-    # =========================================================================
-    # BIGCODE — StarCoder Family
-    # =========================================================================
-    "bigcode/starcoder2-15b-instruct-v0.1": ModelInfo(
-        model_id="bigcode/starcoder2-15b-instruct-v0.1",
-        provider="BigCode",
-        context_length=16384,
-        strengths=["Code generation", "Multiple languages"],
-        best_for="Agent 4 (Generator) — Open source code model",
-        tier="free"
-    ),
-}
-
-
-# =============================================================================
-# RECOMMENDED CONFIGURATIONS BY TIER
-# =============================================================================
-
-MODEL_PRESETS = {
-    "budget": {
-        "name": "Budget (Free Tier)",
-        "description": "Best free models for each task",
-        "agent2": "microsoft/Phi-3.5-mini-instruct",
-        "agent3": "mistralai/Mixtral-8x7B-Instruct-v0.1",
-        "agent4": "bigcode/starcoder2-15b-instruct-v0.1",
-        "fallback": "mistralai/Mistral-7B-Instruct-v0.3",
-    },
-    "balanced": {
-        "name": "Balanced (Pro Tier)",
-        "description": "Good quality/cost balance",
-        "agent2": "google/gemma-2-9b-it",
-        "agent3": "meta-llama/Llama-3.1-70B-Instruct",
-        "agent4": "mistralai/Codestral-22B-v0.1",
-        "fallback": "Qwen/Qwen2.5-7B-Instruct",
-    },
-    "quality": {
-        "name": "Maximum Quality (Pro+)",
-        "description": "Best models regardless of cost",
-        "agent2": "google/gemma-2-27b-it",
-        "agent3": "meta-llama/Llama-3.1-405B-Instruct",
-        "agent4": "deepseek-ai/deepseek-coder-33b-instruct",
-        "fallback": "meta-llama/Llama-3.1-8B-Instruct",
-    },
-    "diverse": {
-        "name": "Diverse Providers",
-        "description": "One model from each major provider",
-        "agent2": "microsoft/Phi-3.5-mini-instruct",  # Microsoft
-        "agent3": "CohereForAI/c4ai-command-r-plus",  # Cohere
-        "agent4": "mistralai/Codestral-22B-v0.1",     # Mistral
-        "fallback": "meta-llama/Llama-3.1-8B-Instruct",  # Meta
-    },
-}
-
-
-# =============================================================================
-# AGENT-SPECIFIC RECOMMENDATIONS
-# =============================================================================
-
-AGENT_MODEL_RECOMMENDATIONS = {
-    "crawler": {
-        "requires_llm": False,
-        "notes": "Pure rule-based extraction using Playwright + CSS parsing"
-    },
-    "extractor": {
-        "requires_llm": False,
-        "notes": "Pure rule-based extraction using Playwright + CSS parsing"
-    },
-    "normalizer": {
-        "requires_llm": True,
-        "task": "Token naming, duplicate detection, pattern inference",
-        "needs": ["Fast inference", "Good instruction following", "Structured output"],
-        "recommended": [
-            ("microsoft/Phi-3.5-mini-instruct", "BEST — Fast, great structured output"),
-            ("google/gemma-2-9b-it", "Good balance of speed and quality"),
-            ("Qwen/Qwen2.5-7B-Instruct", "Reliable all-rounder"),
-        ],
-        "temperature": 0.2,
-    },
-    "advisor": {
-        "requires_llm": True,
-        "task": "Design system analysis, best practice recommendations",
-        "needs": ["Strong reasoning", "Design knowledge", "Creative suggestions"],
-        "recommended": [
-            ("meta-llama/Llama-3.1-70B-Instruct", "BEST — Excellent reasoning"),
-            ("CohereForAI/c4ai-command-r-plus", "Great for analysis tasks"),
-            ("Qwen/Qwen2.5-72B-Instruct", "Strong alternative"),
-            ("mistralai/Mixtral-8x7B-Instruct-v0.1", "Best free option"),
-        ],
-        "temperature": 0.4,
-    },
-    "generator": {
-        "requires_llm": True,
-        "task": "Generate JSON tokens, CSS variables, structured output",
-        "needs": ["Code generation", "JSON formatting", "Schema adherence"],
-        "recommended": [
-            ("mistralai/Codestral-22B-v0.1", "BEST — Mistral's code model"),
-            ("deepseek-ai/deepseek-coder-33b-instruct", "Excellent code specialist"),
-            ("Qwen/Qwen2.5-Coder-32B-Instruct", "Strong code model"),
-            ("bigcode/starcoder2-15b-instruct-v0.1", "Best free option"),
-        ],
-        "temperature": 0.1,
-    },
-}
-
-
-# =============================================================================
-# INFERENCE CLIENT
-# =============================================================================
-
-class HFInferenceClient:
-    """
-    Wrapper around HuggingFace Inference API.
-    
-    Handles model selection, retries, and fallbacks.
-    """
-    
-    def __init__(self):
-        self.settings = get_settings()
-        # Read token fresh from env — the Settings singleton may have been
-        # created before the user entered their token via the Gradio UI.
-        self.token = os.getenv("HF_TOKEN", "") or self.settings.hf.hf_token
-
-        if not self.token:
-            raise ValueError("HF_TOKEN is required for inference")
-
-        # Let huggingface_hub route to the best available provider automatically.
-        # Do NOT set base_url (overrides per-model routing) or
-        # provider="hf-inference" (that provider no longer hosts most models).
-        # The default provider="auto" picks the first available third-party
-        # provider (novita, together, cerebras, etc.) for each model.
-        self.sync_client = InferenceClient(token=self.token)
-        self.async_client = AsyncInferenceClient(token=self.token)
-    
-    def get_model_for_agent(self, agent_name: str) -> str:
-        """Get the appropriate model for an agent."""
-        return self.settings.get_model_for_agent(agent_name)
-    
-    def get_temperature_for_agent(self, agent_name: str) -> float:
-        """Get recommended temperature for an agent."""
-        temps = {
-            "normalizer": 0.2,  # Consistent naming
-            "advisor": 0.4,    # Creative recommendations
-            "generator": 0.1,  # Precise formatting
-        }
-        return temps.get(agent_name, 0.3)
-    
-    def _build_messages(
-        self,
-        system_prompt: str,
-        user_message: str,
-        examples: list[dict] = None
-    ) -> list[dict]:
-        """Build message list for chat completion."""
-        messages = []
-        
-        if system_prompt:
-            messages.append({"role": "system", "content": system_prompt})
-        
-        if examples:
-            for example in examples:
-                messages.append({"role": "user", "content": example["user"]})
-                messages.append({"role": "assistant", "content": example["assistant"]})
-        
-        messages.append({"role": "user", "content": user_message})
-        
-        return messages
-    
-    def complete(
-        self,
-        agent_name: str,
-        system_prompt: str,
-        user_message: str,
-        examples: list[dict] = None,
-        max_tokens: int = None,
-        temperature: float = None,
-        json_mode: bool = False,
-    ) -> str:
-        """
-        Synchronous completion.
-        
-        Args:
-            agent_name: Which agent is making the call (for model selection)
-            system_prompt: System instructions
-            user_message: User input
-            examples: Optional few-shot examples
-            max_tokens: Max tokens to generate
-            temperature: Sampling temperature (uses agent default if not specified)
-            json_mode: If True, instruct model to output JSON
-        
-        Returns:
-            Generated text
-        """
-        model = self.get_model_for_agent(agent_name)
-        max_tokens = max_tokens or self.settings.hf.max_new_tokens
-        temperature = temperature or self.get_temperature_for_agent(agent_name)
-        
-        # Build messages
-        if json_mode:
-            system_prompt = f"{system_prompt}\n\nYou must respond with valid JSON only. No markdown, no explanation, just JSON."
-        
-        messages = self._build_messages(system_prompt, user_message, examples)
-        
-        try:
-            response = self.sync_client.chat_completion(
-                model=model,
-                messages=messages,
-                max_tokens=max_tokens,
-                temperature=temperature,
-            )
-            return response.choices[0].message.content
-
-        except Exception as e:
-            error_msg = str(e)
-            print(f"[HF] Primary model {model} failed: {error_msg[:120]}")
-            fallback = self.settings.models.fallback_model
-            if fallback and fallback != model:
-                print(f"[HF] Trying fallback: {fallback}")
-                try:
-                    response = self.sync_client.chat_completion(
-                        model=fallback,
-                        messages=messages,
-                        max_tokens=max_tokens,
-                        temperature=temperature,
-                    )
-                    return response.choices[0].message.content
-                except Exception as fallback_err:
-                    print(f"[HF] Fallback {fallback} also failed: {str(fallback_err)[:120]}")
-                    raise fallback_err
-            raise e
-    
-    async def complete_async(
-        self,
-        agent_name: str,
-        system_prompt: str,
-        user_message: str,
-        examples: list[dict] = None,
-        max_tokens: int = None,
-        temperature: float = None,
-        json_mode: bool = False,
-    ) -> str:
-        """
-        Asynchronous completion.
-        
-        Same parameters as complete().
-        """
-        model = self.get_model_for_agent(agent_name)
-        max_tokens = max_tokens or self.settings.hf.max_new_tokens
-        temperature = temperature or self.get_temperature_for_agent(agent_name)
-        
-        if json_mode:
-            system_prompt = f"{system_prompt}\n\nYou must respond with valid JSON only. No markdown, no explanation, just JSON."
-        
-        messages = self._build_messages(system_prompt, user_message, examples)
-        
-        try:
-            response = await self.async_client.chat_completion(
-                model=model,
-                messages=messages,
-                max_tokens=max_tokens,
-                temperature=temperature,
-            )
-            return response.choices[0].message.content
-
-        except Exception as e:
-            error_msg = str(e)
-            print(f"[HF] Primary model {model} failed: {error_msg[:120]}")
-            fallback = self.settings.models.fallback_model
-            if fallback and fallback != model:
-                print(f"[HF] Trying fallback: {fallback}")
-                try:
-                    response = await self.async_client.chat_completion(
-                        model=fallback,
-                        messages=messages,
-                        max_tokens=max_tokens,
-                        temperature=temperature,
-                    )
-                    return response.choices[0].message.content
-                except Exception as fallback_err:
-                    print(f"[HF] Fallback {fallback} also failed: {str(fallback_err)[:120]}")
-                    raise fallback_err
-            raise e
-    
-    async def stream_async(
-        self,
-        agent_name: str,
-        system_prompt: str,
-        user_message: str,
-        max_tokens: int = None,
-        temperature: float = None,
-    ) -> AsyncGenerator[str, None]:
-        """
-        Async streaming completion.
-        
-        Yields tokens as they are generated.
-        """
-        model = self.get_model_for_agent(agent_name)
-        max_tokens = max_tokens or self.settings.hf.max_new_tokens
-        temperature = temperature or self.get_temperature_for_agent(agent_name)
-        
-        messages = self._build_messages(system_prompt, user_message)
-        
-        async for chunk in await self.async_client.chat_completion(
-            model=model,
-            messages=messages,
-            max_tokens=max_tokens,
-            temperature=temperature,
-            stream=True,
-        ):
-            if chunk.choices[0].delta.content:
-                yield chunk.choices[0].delta.content
-
-
-# =============================================================================
-# SINGLETON & CONVENIENCE FUNCTIONS
-# =============================================================================
-
-_client: Optional[HFInferenceClient] = None
-
-
-def get_inference_client() -> HFInferenceClient:
-    """Get or create the inference client singleton.
-
-    Re-creates the client if the token has changed (e.g. user entered it
-    via the Gradio UI after initial startup).
-    """
-    global _client
-    current_token = os.getenv("HF_TOKEN", "")
-    if _client is None or (_client.token != current_token and current_token):
-        _client = HFInferenceClient()
-    return _client
-
-
-def complete(
-    agent_name: str,
-    system_prompt: str,
-    user_message: str,
-    **kwargs
-) -> str:
-    """Convenience function for sync completion."""
-    client = get_inference_client()
-    return client.complete(agent_name, system_prompt, user_message, **kwargs)
-
-
-async def complete_async(
-    agent_name: str,
-    system_prompt: str,
-    user_message: str,
-    **kwargs
-) -> str:
-    """Convenience function for async completion."""
-    client = get_inference_client()
-    return await client.complete_async(agent_name, system_prompt, user_message, **kwargs)
-
-
-def get_model_info(model_id: str) -> dict:
-    """Get information about a specific model."""
-    if model_id in AVAILABLE_MODELS:
-        info = AVAILABLE_MODELS[model_id]
-        return {
-            "model_id": info.model_id,
-            "provider": info.provider,
-            "context_length": info.context_length,
-            "strengths": info.strengths,
-            "best_for": info.best_for,
-            "tier": info.tier,
-        }
-    return {"model_id": model_id, "provider": "unknown"}
-
-
-def get_models_by_provider() -> dict[str, list[str]]:
-    """Get all models grouped by provider."""
-    by_provider = {}
-    for model_id, info in AVAILABLE_MODELS.items():
-        if info.provider not in by_provider:
-            by_provider[info.provider] = []
-        by_provider[info.provider].append(model_id)
-    return by_provider
-
-
-def get_models_by_tier(tier: str) -> list[str]:
-    """Get all models for a specific tier (free, pro, pro+)."""
-    return [
-        model_id for model_id, info in AVAILABLE_MODELS.items()
-        if info.tier == tier
-    ]
-
-
-def get_preset_config(preset_name: str) -> dict:
-    """Get a preset model configuration."""
-    return MODEL_PRESETS.get(preset_name, MODEL_PRESETS["balanced"])