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Browse files- Dockerfile +42 -0
- README.md +22 -9
- main.py +49 -0
- requirements.txt +8 -0
Dockerfile
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# ============================
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# Dockerfile – Savant RRF Φ12.0 API
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# ============================
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FROM python:3.11-slim
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# Evitar prompts interactivos
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ENV DEBIAN_FRONTEND=noninteractive \
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PYTHONUNBUFFERED=1
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# Crear directorio de trabajo
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WORKDIR /app
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# Instalar dependencias del sistema mínimas
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RUN apt-get update && apt-get install -y --no-install-recommends \
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build-essential \
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git \
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&& rm -rf /var/lib/apt/lists/*
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# Copiar requirements
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COPY requirements.txt /app/requirements.txt
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# Instalar dependencias Python
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RUN pip install --no-cache-dir -r /app/requirements.txt
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# Copiar código de la API
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# Asumimos que main.py contiene:
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# - carga del encoder
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# - carga del meta-logit desde HF
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# - definición de "app = FastAPI(...)"
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COPY main.py /app/main.py
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# Variables de entorno opcionales
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# HF_TOKEN: token para descargar modelos de Hugging Face
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ENV HF_TOKEN=""
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# Exponer el puerto donde correrá uvicorn
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EXPOSE 8000
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# Comando por defecto: lanzar la API
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
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README.md
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---
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title: APISAvant
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emoji: ⚡
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colorFrom: yellow
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colorTo: gray
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sdk: docker
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pinned: false
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license: other
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---
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-
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# Savant RRF Φ12.0 – Dirac-Resonant Conceptual Quality API
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Savant RRF Φ12.0 es una API de evaluación conceptual resonante para texto generado por modelos de lenguaje (LLMs).
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Dado un `prompt` y una `answer`, la API devuelve:
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- **SRRF** – Resonant Quality Score (probabilidad de “respuesta buena” según meta-logit).
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- **CRRF** – Coherent Resonant Score (SRRF modulado por alineación semántica).
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- **E_phi** – Resonant Health Score (promedio entre SRRF y entropía resonante normalizada).
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- **p_good** – probabilidad directa del meta-logit binario.
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- Features físicos: entropía, energía, chirality de una **shell de Dirac icosaédrica**.
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El encoder principal es `antonypamo/RRFSAVANTMADE` y el meta-logit se carga desde el repo de HF `antonypamo/RRFSavantMetaLogit` (por defecto `logreg_rrf_savant_v2.joblib`).
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---
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## Estructura del repo
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```text
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.
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├── main.py # Código de la API (FastAPI + Savant RRF Φ12.0 core)
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├── requirements.txt # Dependencias Python
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└── Dockerfile # Imagen Docker de la API
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main.py
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from sentence_transformers import SentenceTransformer
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from huggingface_hub import hf_hub_download
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import joblib, os
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from fastapi import FastAPI
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from pydantic import BaseModel
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from typing import Optional, Dict, Any
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import numpy as np
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# + resto de imports (scipy, etc.)
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ENCODER_MODEL_ID = "antonypamo/RRFSAVANTMADE"
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META_LOGIT_REPO = "antonypamo/RRFSavantMetaLogit"
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META_LOGIT_FILENAME = "logreg_rrf_savant_v2.joblib"
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encoder = SentenceTransformer(ENCODER_MODEL_ID)
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meta_logit_path = hf_hub_download(
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repo_id=META_LOGIT_REPO,
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filename=META_LOGIT_FILENAME,
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token=os.environ.get("HF_TOKEN")
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)
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meta_logit = joblib.load(meta_logit_path)
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class EvaluateRequest(BaseModel):
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prompt: str
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answer: str
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model_label: Optional[str] = None
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class EvaluateResponse(BaseModel):
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scores: Dict[str, float]
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features: Dict[str, float]
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sim_summary: Dict[str, Any]
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app = FastAPI(
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title="Savant RRF Φ12.0 API",
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description="Dirac-Resonant conceptual quality layer for LLM-generated text.",
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version="1.0.0",
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)
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@app.post("/evaluate", response_model=EvaluateResponse)
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def evaluate(req: EvaluateRequest):
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scores, feats = compute_scores_srff_crff_ephi(req.prompt, req.answer)
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# opcional: sim_summary con entropía/energía/chirality
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sim_summary = {...}
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return EvaluateResponse(
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scores=scores,
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features=feats,
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sim_summary=sim_summary,
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)
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requirements.txt
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fastapi==0.115.0
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uvicorn[standard]==0.30.6
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sentence-transformers==3.0.1
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huggingface_hub==0.24.6
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joblib==1.4.2
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scipy==1.13.1
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numpy==1.26.4
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