Primera prueba de código de microservicios

.gitea/workflows/build.yml

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+name: build
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: [k8s, kaniko]
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Login a Nexus
+        env:
+          NEXUS_USER: ${{ secrets.NEXUS_USER }}
+          NEXUS_PASSWORD: ${{ secrets.NEXUS_PASSWORD }}
+        run: |
+          echo "$NEXUS_PASSWORD" | docker login nexus.rancherk3.duckdns.org -u "$NEXUS_USER" --password-stdin
+
+      - name: Build & Push image with Kaniko
+        image: gcr.io/kaniko-project/executor:latest
+        command:
+          - /kaniko/executor
+        args:
+          - --context=.
+          - --dockerfile=./Dockerfile
+          - --destination=nexus.rancherk3.duckdns.org/tfm/microserviciospython:latest

.gitea/workflows/test.yml

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+name: test
+on: [push]
+
+jobs:
+  echo:
+    runs-on: self-hosted
+    steps:
+      - run: echo "Gitea Actions funcionando!"

Dockerfile

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+# =========================
+# Etapa de Build
+# =========================
+FROM python:3.12-slim AS build
+
+# Instalar dependencias de build mínimas
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential \
+        python3-venv \
+        pip \
+ && apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
+
+# Crear un entorno virtual Python para build
+RUN python -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# Actualizar pip y herramientas de build
+RUN pip install --upgrade pip setuptools wheel build pipenv
+
+# Copiar código y preparar build
+ADD src /app
+WORKDIR /app
+
+# Instalar dependencias y construir la rueda
+RUN pipenv install --deploy --system || true
+RUN python -m build -w
+
+# =========================
+# Etapa de Runtime
+# =========================
+FROM python:3.12-slim
+
+ARG BUILD_DATE
+LABEL org.label-schema.maintainer="Diego Fernández Carvajal (diego.fdezcarvajal@emtmadrid.es)" \
+      org.label-schema.build-dockerfile="11/12/2025" \
+      org.label-schema.build=$BUILD_DATE
+
+ENV DEBIAN_FRONTEND=noninteractive
+ENV TZ=Europe/Madrid
+ENV PYTHONUNBUFFERED=1
+ENV PATH="/opt/venv/bin:$PATH"
+
+WORKDIR /app
+
+# Instalar dependencias mínimas de runtime
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        python3-venv \
+        supervisor \
+        tzdata \
+        locales \
+ && ln -fs /usr/share/zoneinfo/$TZ /etc/localtime \
+ && echo $TZ > /etc/timezone \
+ && echo "es_ES.UTF-8 UTF-8" > /etc/locale.gen \
+ && echo "en_US.UTF-8 UTF-8" >> /etc/locale.gen \
+ && locale-gen \
+ && apt-get clean && apt-get autoremove -y \
+ && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
+
+# Copiar supervisord
+COPY /man/supervisord.conf /etc/supervisor/conf.d/supervisord.conf
+
+# Copiar ruedas desde build
+COPY --from=build /app/dist/*.whl /app/
+
+# Crear entorno virtual para runtime
+RUN python -m venv /opt/venv
+
+# Instalar ruedas en el entorno virtual
+RUN pip install --upgrade pip setuptools wheel
+RUN pip install /app/*.whl -t /app
+
+CMD ["/usr/bin/supervisord","-c","/etc/supervisor/conf.d/supervisord.conf"]

man/supervisord.conf

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+[supervisord]
+logfile=/tmp/supervisord.log                    ; supervisord log file
+logfile_maxbytes=10MB                           ; maximum size of logfile before rotation
+logfile_backups=2                               ; number of backed up logfiles
+loglevel=error                                  ; info, debug, warn, trace
+pidfile=/var/run/supervisord.pid                ; pidfile location
+nodaemon=true                                   ; run supervisord as a daemon
+minfds=1024                                     ; number of startup file descriptors
+minprocs=200                                    ; number of process descriptors
+user=root                                       ; default user
+
+[unix_http_server]
+file = /tmp/supervisor.sock                     ; supervisor unix http server sock file
+
+[program:microservicios]
+command=python3 flask.py
+directory=/app
+stdout_logfile=/dev/fd/1
+stdout_logfile_maxbytes=0
+stderr_logfile=/dev/fd/2
+stderr_logfile_maxbytes=0
+autostart=true
+autorestart=true

src/flask.py

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+from flask import Flask, jsonify, request
+import os
+import psycopg2
+import pandas as pd
+import xgboost as xgb
+import joblib
+
+app = Flask(__name__)
+
+# Configuración de la base de datos
+DB_CONFIG = {
+    'dbname': os.getenv('DB_NAME', 'postgres'),
+    'user': os.getenv('DB_USER', 'postgres'),
+    'password': os.getenv('DB_PASSWORD', 'tfmuocdfcarvajal'),
+    'host': os.getenv('DB_HOST', '10.10.5.32'),
+    'port': os.getenv('DB_PORT', '5432')
+}
+
+# Ruta para entrenar o cargar el modelo
+MODEL_PATH = 'modelo_xgb.joblib'
+
+def get_db_connection():
+    conn = psycopg2.connect(**DB_CONFIG)
+    return conn
+
+def fetch_data():
+    conn = get_db_connection()
+    query = """
+        SELECT h3, hour, dow, total_events, total_nulos, nulo_rate
+        FROM demanda_h3_hour
+    """
+    df = pd.read_sql(query, conn)
+    conn.close()
+    return df
+
+def train_model(df):
+    # Variables predictoras
+    X = df[['hour', 'dow', 'total_events']]
+    # Variable objetivo: nulo_rate > 0 -> 1, else 0
+    y = (df['nulo_rate'] > 0).astype(int)
+    
+    model = xgb.XGBClassifier(
+        max_depth=4, n_estimators=100, learning_rate=0.1, use_label_encoder=False, eval_metric='logloss'
+    )
+    model.fit(X, y)
+    joblib.dump(model, MODEL_PATH)
+    return model
+
+def load_model():
+    try:
+        model = joblib.load(MODEL_PATH)
+        return model
+    except:
+        df = fetch_data()
+        return train_model(df)
+
+model = load_model()
+
+@app.route('/predict', methods=['GET'])
+def predict():
+    # Parámetros de la consulta
+    hour = int(request.args.get('hour'))
+    dow = int(request.args.get('dow'))
+    total_events = int(request.args.get('total_events', 1))  # valor por defecto si no se pasa
+    
+    X_pred = pd.DataFrame([[hour, dow, total_events]], columns=['hour', 'dow', 'total_events'])
+    prob = model.predict_proba(X_pred)[0][1]  # Probabilidad de nulo
+    return jsonify({
+        'hour': hour,
+        'dow': dow,
+        'total_events': total_events,
+        'predicted_nulo_prob': float(prob)
+    })
+
+@app.route('/demand', methods=['GET'])
+def demand():
+    df = fetch_data()
+    X_pred = df[['hour', 'dow', 'total_events']]
+    df['predicted_nulo_prob'] = model.predict_proba(X_pred)[:,1].astype(float)
+    # Convertimos a JSON
+    result = df.to_dict(orient='records')
+    return jsonify(result)
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)

src/pyproject.toml

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+[build-system]
+requires = ["setuptools >= 61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "microservicios"
+version = "1.0.1"
+requires-python = ">= 3.8"
+description = "Microservicios para Estimaciones"
+license={text = "EULA"}
+
+authors = [
+  {name = "Diego Fernández", email = "carvajal.diego@gmail.com"}
+]
+
+dependencies = [
+   'flask',
+   'psycopg2-binary',
+   'pandas',
+   'xgboost',
+   'scikit-learn',
+   'joblib'
+]
+
+[tool.setuptools]
+include-package-data = true
+py-modules = ["flask"]
+
+[tool.setuptools.packages]
+find = {}
+
+[project.urls]
+    Documentation='https://https://gitea.tfmuocdfcarvajal.duckdns.org/TFM/microservicios_python'