Initial commit: Leopost Full — merge di Leopost, Post Generator e Autopilot OS
- Backend FastAPI con multi-LLM (Claude/OpenAI/Gemini) - Publishing su Facebook, Instagram, YouTube, TikTok - Calendario editoriale con awareness levels (PAS, AIDA, BAB...) - Design system Editorial Fresh (Fraunces + DM Sans) - Scheduler automatico, gestione commenti AI, affiliate links Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Michele
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backend/app/services/llm.py
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194
backend/app/services/llm.py
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"""
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Multi-LLM abstraction layer.
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Supports Claude (Anthropic), OpenAI, and Gemini via direct HTTP calls using httpx.
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Each provider implements the same interface for text generation.
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"""
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from abc import ABC, abstractmethod
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import httpx
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# Default models per provider
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DEFAULT_MODELS = {
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"claude": "claude-sonnet-4-20250514",
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"openai": "gpt-4o-mini",
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"gemini": "gemini-2.0-flash",
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}
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TIMEOUT = 60.0
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class LLMProvider(ABC):
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"""Abstract base class for LLM providers."""
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def __init__(self, api_key: str, model: str | None = None):
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self.api_key = api_key
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self.model = model
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@abstractmethod
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def generate(self, prompt: str, system: str = "") -> str:
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"""Generate text from a prompt.
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Args:
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prompt: The user prompt / message.
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system: Optional system prompt for context and behavior.
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Returns:
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Generated text string.
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"""
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...
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class ClaudeProvider(LLMProvider):
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"""Anthropic Claude provider via Messages API."""
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API_URL = "https://api.anthropic.com/v1/messages"
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def __init__(self, api_key: str, model: str | None = None):
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super().__init__(api_key, model or DEFAULT_MODELS["claude"])
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def generate(self, prompt: str, system: str = "") -> str:
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headers = {
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"x-api-key": self.api_key,
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"anthropic-version": "2023-06-01",
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"content-type": "application/json",
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}
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payload: dict = {
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"model": self.model,
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"max_tokens": 2048,
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"messages": [{"role": "user", "content": prompt}],
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}
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if system:
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payload["system"] = system
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try:
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with httpx.Client(timeout=TIMEOUT) as client:
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response = client.post(self.API_URL, headers=headers, json=payload)
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response.raise_for_status()
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data = response.json()
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# Claude returns content as a list of content blocks
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content_blocks = data.get("content", [])
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return "".join(
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block.get("text", "") for block in content_blocks if block.get("type") == "text"
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)
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except httpx.HTTPStatusError as e:
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raise RuntimeError(
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f"Claude API error {e.response.status_code}: {e.response.text}"
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) from e
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except httpx.RequestError as e:
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raise RuntimeError(f"Claude API request failed: {e}") from e
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class OpenAIProvider(LLMProvider):
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"""OpenAI provider via Chat Completions API."""
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API_URL = "https://api.openai.com/v1/chat/completions"
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def __init__(self, api_key: str, model: str | None = None):
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super().__init__(api_key, model or DEFAULT_MODELS["openai"])
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def generate(self, prompt: str, system: str = "") -> str:
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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}
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messages: list[dict] = []
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if system:
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messages.append({"role": "system", "content": system})
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messages.append({"role": "user", "content": prompt})
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payload = {
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"model": self.model,
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"messages": messages,
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"max_tokens": 2048,
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}
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try:
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with httpx.Client(timeout=TIMEOUT) as client:
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response = client.post(self.API_URL, headers=headers, json=payload)
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response.raise_for_status()
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data = response.json()
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return data["choices"][0]["message"]["content"]
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except httpx.HTTPStatusError as e:
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raise RuntimeError(
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f"OpenAI API error {e.response.status_code}: {e.response.text}"
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) from e
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except httpx.RequestError as e:
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raise RuntimeError(f"OpenAI API request failed: {e}") from e
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class GeminiProvider(LLMProvider):
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"""Google Gemini provider via Generative Language API."""
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API_BASE = "https://generativelanguage.googleapis.com/v1beta/models"
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def __init__(self, api_key: str, model: str | None = None):
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super().__init__(api_key, model or DEFAULT_MODELS["gemini"])
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def generate(self, prompt: str, system: str = "") -> str:
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url = f"{self.API_BASE}/{self.model}:generateContent"
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params = {"key": self.api_key}
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headers = {"Content-Type": "application/json"}
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# Build contents; Gemini uses a parts-based structure
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parts: list[dict] = []
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if system:
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parts.append({"text": f"{system}\n\n{prompt}"})
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else:
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parts.append({"text": prompt})
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payload = {
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"contents": [{"parts": parts}],
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"generationConfig": {
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"maxOutputTokens": 2048,
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},
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}
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try:
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with httpx.Client(timeout=TIMEOUT) as client:
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response = client.post(url, params=params, headers=headers, json=payload)
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response.raise_for_status()
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data = response.json()
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candidates = data.get("candidates", [])
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if not candidates:
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return ""
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content = candidates[0].get("content", {})
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parts_out = content.get("parts", [])
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return "".join(part.get("text", "") for part in parts_out)
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except httpx.HTTPStatusError as e:
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raise RuntimeError(
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f"Gemini API error {e.response.status_code}: {e.response.text}"
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) from e
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except httpx.RequestError as e:
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raise RuntimeError(f"Gemini API request failed: {e}") from e
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def get_llm_provider(
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provider_name: str, api_key: str, model: str | None = None
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) -> LLMProvider:
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"""Factory function to get an LLM provider instance.
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Args:
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provider_name: One of 'claude', 'openai', 'gemini'.
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api_key: API key for the provider.
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model: Optional model override. Uses default if not specified.
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Returns:
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An LLMProvider instance.
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Raises:
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ValueError: If provider_name is not supported.
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"""
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providers = {
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"claude": ClaudeProvider,
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"openai": OpenAIProvider,
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"gemini": GeminiProvider,
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}
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provider_cls = providers.get(provider_name.lower())
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if provider_cls is None:
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supported = ", ".join(providers.keys())
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raise ValueError(
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f"Unknown LLM provider '{provider_name}'. Supported: {supported}"
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)
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return provider_cls(api_key=api_key, model=model)
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