{"repo": "bhiv-engineering-exchange/bhiv-prompt-runner01", "files": {"api.py": {"05a52db1e91b6fb60b836dc08885204e5ffad3c4": {"t": 1785674395.6460004, "n": 5242, "h": "38334d68a5f9a111", "c": "\"\"\"\nPrompt Runner - FastAPI Service\n================================\nConverts any user prompt into a structured JSON instruction using the Groq API.\n\nRequired environment variable:\n    GROQ_API_KEY=gsk_...   (get yours at console.groq.com)\n\nStart:\n    uvicorn api:app --host 0.0.0.0 --port 8001\n\nEndpoints:\n    POST /generate   — prompt  structured JSON instruction\n    GET  /health     — Groq availability check\n    GET  /schema     — JSON Schema for the instruction format\n    GET  /models     — list available Groq models\n\"\"\"\n\nfrom typing import Any, Dict, List, Optional\n\nfrom fastapi import FastAPI, HTTPException\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom pydantic import BaseModel, Field\n\nfrom llm_adapter import LLMAdapter, DEFAULT_MODEL\n\n# ---------------------------------------------------------------------------\n# App\n# ---------------------------------------------------------------------------\n\napp = FastAPI(\n    title=\"Prompt Runner API\",\n    description=\"Converts any user prompt into a deterministic JSON instruction via Groq API.\",\n    version=\"5.0.0\",\n)\n\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=[\"*\"],\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\n# ---------------------------------------------------------------------------\n# JSON Schema (served at /schema)\n# ---------------------------------------------------------------------------\n\nINSTRUCTION_SCHEMA: Dict[str, Any] = {\n    \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n    \"title\": \"Prompt Runner Instruction\",\n    \"type\": \"object\",\n    \"properties\": {\n        \"prompt\":          {\"type\": \"string\"},\n        \"module\":          {\"type\": \"string\"},\n        \"intent\":          {\"type\": \"string\"},\n        \"topic\":           {\"type\": \"string\"},\n        \"tasks\":           {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"minItems\": 1},\n        \"output_format\":   {\"type\": \"string\"},\n        \"product_context\": {\"type\": \"string\", \"enum\": [\"creator_core\"]},\n    },\n    \"required\": [\"prompt\", \"module\", \"intent\", \"topic\", \"tasks\", \"output_format\", \"product_context\"],\n    \"additionalProperties\": False,\n}\n\n# ---------------------------------------------------------------------------\n# Singleton adapter\n# ---------------------------------------------------------------------------\n\n_llm: Optional[LLMAdapter] = None\n\n\ndef _get_llm() -> LLMAdapter:\n    global _llm\n    if _llm is None:\n        _llm = LLMAdapter()   # reads GROQ_API_KEY from environment / .env.local\n    return _llm\n\n\n# ---------------------------------------------------------------------------\n# Pydantic models\n# ---------------------------------------------------------------------------\n\nclass PromptRequest(BaseModel):\n    prompt: str           = Field(..., min_length=1, description=\"User prompt to interpret\")\n    model:  Optional[str] = Field(None, description=\"Groq model override\")\n\n\nclass InstructionResponse(BaseModel):\n    prompt:          str\n    module:          str\n    intent:          str\n    topic:           str\n    tasks:           List[str]\n    output_format:   str\n    product_context: str = \"creator_core\"\n\n\nclass HealthResponse(BaseModel):\n    status:         str\n    groq_available: bool\n    models:         List[str]\n\n\n# ---------------------------------------------------------------------------\n# Endpoints\n# ---------------------------------------------------------------------------\n\n@app.get(\"/health\", response_model=HealthResponse, tags=[\"system\"])\ndef health_check():\n    \"\"\"Check Groq API availability.\"\"\"\n    llm = _get_llm()\n    llm.reset_availability_cache()\n    return HealthResponse(\n        status         = \"healthy\",\n        groq_available = llm.available,\n        models         = llm.client.list_models() if llm.available else [],\n    )\n\n\n@app.post(\"/generate\", response_model=InstructionResponse, tags=[\"core\"])\ndef generate_instruction(request: PromptRequest):\n    \"\"\"\n    Convert a user prompt into a structured JSON instruction via Groq API.\n\n    Output always contains exactly these 6 keys:\n    {\n        \"prompt\": \"...\",\n        \"module\": \"...\",\n        \"intent\": \"...\",\n        \"topic\": \"...\",\n        \"tasks\": [\"...\", \"...\"],\n        \"output_format\": \"...\"\n    }\n    \"\"\"\n    llm = _get_llm()\n\n    if not llm.available:\n        raise HTTPException(\n            status_code=503,\n            detail=\"GROQ_API_KEY is not configured. Set it as an environment variable.\",\n        )\n\n    if request.model:\n        llm.client.model = request.model\n        llm.reset_availability_cache()\n\n    try:\n        instruction = llm.generate_instruction(request.prompt)\n        return InstructionResponse(**instruction)\n    except Exception as exc:\n        raise HTTPException(status_code=500, detail=str(exc))\n\n\n@app.get(\"/schema\", tags=[\"system\"])\ndef get_schema():\n    \"\"\"Return the JSON Schema for the instruction output format.\"\"\"\n    return INSTRUCTION_SCHEMA\n\n\n@app.get(\"/models\", tags=[\"system\"])\ndef list_models():\n    \"\"\"List available Groq models.\"\"\"\n    llm = _get_llm()\n    llm.reset_availability_cache()\n    return {\n        \"groq_available\": llm.available,\n        \"models\":         llm.client.list_models() if llm.available else [],\n        \"default_model\":  DEFAULT_MODEL,\n    }\n    \n    \n    \n    \n    "}}}}