1118 lines
45 KiB
Python
1118 lines
45 KiB
Python
# api.py — OCI GenAI + OpenAI v1 Compatibility (files + images + multimodal)
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# -----------------------------------------------------------------------------
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# Requisitos:
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# pip install flask oci requests pillow
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# # (recomendado p/ CORS em produção)
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# pip install flask-cors
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# Execução:
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# export API_KEY="minha-chave"
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# export GENAI_BUCKET="lohmann-ai-br"
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# export GENAI_UPLOAD_PREFIX="genai-uploads/"
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# # opcional: onde está o JSON dos modelos
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# export LLM_CONFIG_PATH="/home/app/llm_models.json"
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# # opcional: debug da autenticação
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# export DEBUG_AUTH=true
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# python api.py # porta 8000
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# -----------------------------------------------------------------------------
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from flask import Flask, request, jsonify, abort, Response, stream_with_context, send_file
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import oci
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import requests
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import os
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import io
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import json
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import uuid
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import base64
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import time
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import mimetypes
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import hmac
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, Optional, Generator
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app = Flask(__name__)
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# ==========================
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# CORS (habilita para OpenWebUI e browsers)
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# ==========================
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try:
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from flask_cors import CORS
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CORS(
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app,
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resources={r"/*": {"origins": "*"}},
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supports_credentials=False,
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allow_headers=["Content-Type", "Authorization", "X-API-Key", "X-Channel", "X-Cuid"],
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expose_headers=["Content-Type", "Authorization", "X-API-Key"],
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methods=["GET", "POST", "OPTIONS"]
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)
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except Exception as _e:
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print("AVISO: flask-cors não instalado; CORS mínimo será aplicado via after_request.")
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@app.after_request
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def add_cors_headers(resp):
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resp.headers.setdefault("Access-Control-Allow-Origin", "*")
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resp.headers.setdefault("Access-Control-Allow-Methods", "GET, POST, OPTIONS")
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resp.headers.setdefault("Access-Control-Allow-Headers", "Content-Type, Authorization, X-API-Key, X-Channel, X-Cuid")
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return resp
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# ==========================
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# Configuração e Autenticação OCI
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# ==========================
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def load_config(config_file="/home/app/credentials.conf"):
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config = {}
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try:
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with open(config_file, 'r') as f:
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for line in f:
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line = line.strip()
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if line and not line.startswith('#'):
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key, value = line.split('=', 1)
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config[key.strip()] = value.strip()
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return config
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except FileNotFoundError:
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raise FileNotFoundError(f"Arquivo de configuração '{config_file}' não encontrado")
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except Exception as e:
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raise Exception(f"Erro ao carregar configuração: {str(e)}")
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config = load_config()
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TEST_MODE = config.get("test_mode", "false").lower() == "true"
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signer = None
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if not TEST_MODE:
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try:
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signer = oci.signer.Signer(
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tenancy=config.get("tenancy"),
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user=config.get("user"),
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fingerprint=config.get("fingerprint"),
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private_key_file_location=config.get("key_file"),
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pass_phrase=config.get("pass_phrase"),
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private_key_content=config.get("key_content"),
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)
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except Exception as e:
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print(f"Erro ao inicializar signer OCI: {e}")
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print("Executando em modo de teste...")
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TEST_MODE = True
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# ==========================
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# Segurança API
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# ==========================
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DEBUG_AUTH = os.environ.get("DEBUG_AUTH", "false").lower() == "true"
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def _safe_equals(a: str, b: str) -> bool:
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if a is None or b is None:
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return False
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return hmac.compare_digest(a, b)
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def _parse_bearer_token(auth_header: str) -> str:
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# Suporta "Bearer <token>" (case-insensitive no prefixo). Opcionalmente aceita "Token <token>".
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if not auth_header:
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return ""
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parts = auth_header.strip().split()
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if len(parts) == 2 and parts[0].lower() in ("bearer", "token"):
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return parts[1]
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return ""
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def check_api_key():
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expected_key = os.environ.get("API_KEY")
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if not expected_key:
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print("AVISO: API_KEY não configurada nas variáveis de ambiente.")
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return
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provided_key = request.headers.get("X-API-Key")
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auth_header = request.headers.get("Authorization")
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bearer_token = _parse_bearer_token(auth_header)
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if DEBUG_AUTH:
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print(f"[auth] method={request.method} path={request.path} "
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f"X-API-Key={'<set>' if provided_key else '<none>'} "
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f"Authorization={'<set>' if auth_header else '<none>'}")
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if _safe_equals(provided_key, expected_key) or _safe_equals(bearer_token, expected_key):
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return
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abort(401, description="Credenciais inválidas ou ausentes. Use X-API-Key ou Authorization: Bearer.")
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@app.before_request
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def before_all_requests():
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# Permitir preflight CORS (OPTIONS) sem autenticação
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if request.method == "OPTIONS":
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return "", 204
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check_api_key()
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# ==========================
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# Variáveis de Bucket / Uploads
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# ==========================
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BUCKET_NAME = os.environ.get("GENAI_BUCKET", "lohmann-ai-br")
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UPLOAD_PREFIX = os.environ.get("GENAI_UPLOAD_PREFIX", "genai-uploads/")
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if UPLOAD_PREFIX and not UPLOAD_PREFIX.endswith("/"):
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UPLOAD_PREFIX += "/"
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object_client = None
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namespace = None
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region = config.get("region") or os.environ.get("OCI_REGION", "us-chicago-1")
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if not TEST_MODE:
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try:
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object_client = oci.object_storage.ObjectStorageClient(config)
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namespace = object_client.get_namespace().data
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except Exception as e:
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print(f"Erro ao inicializar ObjectStorageClient: {e}")
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TEST_MODE = True
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# Session store para mapear file_id -> object_name (para fallback de download)
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FILE_INDEX: Dict[str, str] = {}
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# ==========================
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# Helpers: Signed URL (PAR) + Upload
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# ==========================
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def guess_mime(filename: str, default: str = "application/octet-stream") -> str:
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mt, _ = mimetypes.guess_type(filename)
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return mt or default
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def create_par_for_object(object_name: str, hours_valid: int = 1) -> str:
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"""
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Cria um Pre-Authenticated Request (PAR) para leitura do objeto.
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Retorna a URL completa (https://objectstorage.region.oraclecloud.com{accessUri})
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"""
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if TEST_MODE:
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return f"https://objectstorage.{region}.oraclecloud.com/test/{object_name}"
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expires = datetime.utcnow() + timedelta(hours=hours_valid)
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details = oci.object_storage.models.CreatePreauthenticatedRequestDetails(
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name=f"par-{uuid.uuid4().hex[:8]}",
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access_type="ObjectRead",
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time_expires=expires,
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bucket_listing_action=None,
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object_name=object_name
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)
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par = object_client.create_preauthenticated_request(
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namespace_name=namespace,
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bucket_name=BUCKET_NAME,
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create_preauthenticated_request_details=details
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).data
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base = f"https://objectstorage.{region}.oraclecloud.com"
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return base + par.access_uri
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def upload_file_to_bucket(file_storage, filename: str) -> Dict[str, Any]:
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"""
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Faz upload do arquivo para o bucket e retorna metadata + signed URL (PAR).
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"""
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file_storage.stream.seek(0)
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content = file_storage.read()
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size = len(content)
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if TEST_MODE:
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file_id = f"file-{uuid.uuid4().hex[:12]}"
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url = f"https://objectstorage.{region}.oraclecloud.com/test/{UPLOAD_PREFIX}{file_id}_{filename}"
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FILE_INDEX[file_id] = f"{UPLOAD_PREFIX}{file_id}_{filename}"
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return {"id": file_id, "object": "file", "filename": filename, "bytes": size, "url": url}
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object_name = f"{UPLOAD_PREFIX}{uuid.uuid4().hex}_{filename}"
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object_client.put_object(
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namespace,
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BUCKET_NAME,
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object_name,
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content,
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content_type=guess_mime(filename, "application/octet-stream")
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)
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url = create_par_for_object(object_name, hours_valid=24)
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file_id = f"file-{uuid.uuid4().hex[:12]}"
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FILE_INDEX[file_id] = object_name
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return {
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"id": file_id,
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"object": "file",
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"filename": filename,
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"bytes": size,
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"url": url
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}
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def get_signed_url_from_file_id(file_id: str, hours_valid: int = 24) -> Optional[str]:
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if file_id in FILE_INDEX:
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obj = FILE_INDEX[file_id]
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return create_par_for_object(obj, hours_valid=hours_valid) if not TEST_MODE else f"https://test/{obj}"
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return None
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# ==========================
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# Modelos — defaults e JSON externo (hot-reload)
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# ==========================
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SUPPORTED_MODELS_DEFAULTS: Dict[str, Dict[str, Any]] = {
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"gpt5": { # OpenAI GPT-5
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"id": "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceyasebknceb4ekbiaiisjtu3fj5i7s4io3ignvg4ip2uyma",
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"params": {"max_completion_tokens": 2048, "reasoning_effort": "MEDIUM", "verbosity": "MEDIUM"}
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},
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"grok3mini": { # xAI Grok-3 Mini
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"id": "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceyavwbgai5nlntsd5hngaileroifuoec5qxttmydhq7mykq",
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"params": {"temperature": 1, "top_p": 1, "max_tokens": 600}
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},
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"llama4maverick": { # Meta Llama-4 Maverick
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"id": "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceyayjawvuonfkw2ua4bob4rlnnlhs522pafbglivtwlfzta",
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"params": {"temperature": 1, "top_p": 0.75, "max_tokens": 600, "frequency_penalty": 0, "presence_penalty": 0}
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},
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"grokcode": { # xAI Grok-Code-Fast 1
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"id": "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceyasw26b5macw3kkrm5czk7ziblk5m7axkgnzrtrtp7ytqa",
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"params": {"temperature": 1, "top_p": 1, "top_k": 0, "max_tokens": 600}
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},
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"commandrplus": { # Cohere Command-R-Plus
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"id": "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceyaodm6rdyxmdzlddweh4amobzoo4fatlao2pwnekexmosq",
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"params": {"temperature": 1, "top_p": 0.75, "top_k": 0, "max_tokens": 600, "frequency_penalty": 0}
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},
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"gptoss120": { # OpenAI GPT-OSS 120B
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"id": "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceya3eub3uksacl5q35mrigancv6rbppihlg7ihhjofyc22q",
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"params": {"temperature": 1, "top_p": 1, "top_k": 0, "max_tokens": 2048, "frequency_penalty": 0, "presence_penalty": 0}
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},
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"grok4": { # xAI Grok-4
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"id": "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceya3bsfz4ogiuv3yc7gcnlry7gi3zzx6tnikg6jltqszm2q",
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"params": {"temperature": 1, "top_p": 1, "top_k": 0, "max_tokens": 20000}
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}
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}
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LLM_CONFIG_PATH = os.environ.get("LLM_CONFIG_PATH", "/home/app/llm_models.json")
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def get_supported_models() -> Dict[str, Dict[str, Any]]:
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"""
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Lê SEMPRE o JSON de modelos (hot-reload). Se ausente/ inválido, usa defaults embutidos.
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Estrutura esperada:
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{
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"models": {
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"apelido": { "id": "ocid1....", "params": {...} },
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...
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}
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}
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"""
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try:
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with open(LLM_CONFIG_PATH, "r", encoding="utf-8") as f:
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data = json.load(f)
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models = data.get("models", {})
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valid = {k: v for k, v in models.items() if isinstance(v, dict) and v.get("id")}
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if not valid:
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raise ValueError("Arquivo de modelos não contém 'models' válidos.")
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return valid
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except Exception as e:
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print(f"[warn] Usando SUPPORTED_MODELS_DEFAULTS (motivo: {e})")
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return SUPPORTED_MODELS_DEFAULTS
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# ==========================
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# Session Store (Agente)
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# ==========================
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SESSION_STORE = {}
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SESSION_TTL = timedelta(hours=2)
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def session_controller(region, agent_endpoint_id, channel, cuid):
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"""
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Controla sessões com o agente, reaproveitando se estiver dentro do TTL (2h).
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A cada interação, a sessão é renovada (sliding TTL).
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"""
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session_key = f"{channel}:{cuid}"
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now = datetime.utcnow()
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existing = SESSION_STORE.get(session_key)
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if existing:
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last_used = existing["lastUsedAt"]
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if now - last_used < SESSION_TTL:
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existing["lastUsedAt"] = now
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return {"id": existing["sessionId"], "sessionKey": session_key, "reused": True}
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if TEST_MODE:
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new_session_id = f"test_session_{agent_endpoint_id[:8]}_{int(now.timestamp())}"
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SESSION_STORE[session_key] = {
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"sessionId": new_session_id, "createdAt": now, "lastUsedAt": now, "sessionKey": session_key
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}
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print(f"[agent] nova sessão criada (TEST): key={session_key} id={new_session_id}")
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return {"id": new_session_id, "sessionKey": session_key, "reused": False}
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try:
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session = requests.Session()
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session.auth = signer
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url = (
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f"https://agent-runtime.generativeai.{region}.oci.oraclecloud.com/20240531/"
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f"agentEndpoints/{agent_endpoint_id}/sessions"
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)
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payload = {
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"description": f"Session for {session_key}",
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"displayName": session_key,
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"idleTimeoutInSeconds": str(int(SESSION_TTL.total_seconds()))
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}
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resp = session.post(url, json=payload)
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resp.raise_for_status()
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data = resp.json()
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SESSION_STORE[session_key] = {
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"sessionId": data.get("id"), "createdAt": now, "lastUsedAt": now, "sessionKey": session_key
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}
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print(f"[agent] nova sessão criada: key={session_key} id={data.get('id')}")
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data["sessionKey"] = session_key
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data["reused"] = False
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return data
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except Exception as e:
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return {"error": str(e), "sessionKey": session_key}
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def _invalidate_session(session_key: str):
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try:
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if session_key in SESSION_STORE:
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del SESSION_STORE[session_key]
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print(f"[agent] sessão invalidada: key={session_key}")
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except Exception:
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pass
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# ==========================
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# Funções de interação (Agente + Inference)
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# ==========================
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def ask_agent(region, agent_endpoint_id, session_id, user_message):
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if TEST_MODE:
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return {
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"message": f"Resposta simulada para: {user_message}",
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"sessionId": session_id,
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"timestamp": datetime.utcnow().isoformat() + "Z"
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}
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session = requests.Session()
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session.auth = signer
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base_url = f"https://agent-runtime.generativeai.{region}.oci.oraclecloud.com/20240531"
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chat_url = f"{base_url}/agentEndpoints/{agent_endpoint_id}/actions/chat"
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payload = {"userMessage": user_message, "shouldStream": False, "sessionId": session_id}
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try:
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response = session.post(chat_url, json=payload)
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status = response.status_code
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text_body = None
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try:
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json_body = response.json()
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except Exception:
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json_body = None
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text_body = response.text
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if 200 <= status < 300:
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return json_body if json_body is not None else {"message": text_body or ""}
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else:
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return {
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"_http_status": status,
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"_raw_text": text_body,
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"_raw_json": json_body
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}
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except Exception as e:
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return {
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"_http_status": 0,
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"error": f"Falha de rede ao chamar Agent: {e}"
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}
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def call_inference_model(region, compartment_id, model_id, prompt):
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print(">>> /inference payload recebido:")
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if TEST_MODE:
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return {"response": f"Resposta simulada para o prompt: {prompt}"}
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try:
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endpoint = f"https://inference.generativeai.{region}.oci.oraclecloud.com"
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generative_ai_inference_client = oci.generative_ai_inference.GenerativeAiInferenceClient(
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config=config, service_endpoint=endpoint, retry_strategy=oci.retry.NoneRetryStrategy(), timeout=(10, 240)
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)
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chat_detail = oci.generative_ai_inference.models.ChatDetails()
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content = oci.generative_ai_inference.models.TextContent(); content.text = f"{prompt}"
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message = oci.generative_ai_inference.models.Message(); message.role = "USER"; message.content = [content]
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chat_request = oci.generative_ai_inference.models.GenericChatRequest()
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chat_request.api_format = oci.generative_ai_inference.models.BaseChatRequest.API_FORMAT_GENERIC
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chat_request.messages = [message]
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chat_request.max_tokens = 50000
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chat_request.temperature = 1
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chat_request.top_p = 1
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chat_request.top_k = 0
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chat_detail.serving_mode = oci.generative_ai_inference.models.OnDemandServingMode(model_id=model_id)
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chat_detail.chat_request = chat_request
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chat_detail.compartment_id = compartment_id
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chat_response = generative_ai_inference_client.chat(chat_detail)
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chat_choices = chat_response.data.chat_response.choices
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chat_data = {
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"text": chat_choices[0].message.content[0].text,
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"finish_reason": chat_choices[0].finish_reason
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}
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return {"response": chat_data}
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except Exception as e:
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return {"error": str(e)}
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|
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# ==========================
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|
# Utilitários (OpenAI v1)
|
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# ==========================
|
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|
|
ROLE_MAP = {"system": "SYSTEM", "user": "USER", "assistant": "ASSISTANT"}
|
|
|
|
def ensure_data_url(image_url: str) -> str:
|
|
if not image_url:
|
|
return image_url
|
|
if image_url.startswith("data:"):
|
|
return image_url
|
|
try:
|
|
resp = requests.get(image_url, timeout=30); resp.raise_for_status()
|
|
content = resp.content
|
|
mime = resp.headers.get("Content-Type") or guess_mime(image_url, "image/jpeg")
|
|
b64 = base64.b64encode(content).decode("utf-8")
|
|
return f"data:{mime};base64,{b64}"
|
|
except Exception as e:
|
|
print(f"[warn] Falha ao baixar imagem '{image_url}': {e}")
|
|
return image_url
|
|
|
|
def resolve_model_and_params(body: Dict[str, Any], path_model_id: str) -> Dict[str, Any]:
|
|
"""
|
|
Resolve o OCID do modelo a partir de:
|
|
1) body['model'] se for chave suportada;
|
|
2) body['model'] se for OCID;
|
|
3) path_model_id se for chave suportada ou OCID.
|
|
Mescla defaults + overrides do corpo (OpenAI-like).
|
|
"""
|
|
supported = get_supported_models() # HOT-RELOAD
|
|
user_model = body.get("model")
|
|
model_key = None
|
|
model_ocid = None
|
|
|
|
if isinstance(user_model, str) and user_model in supported:
|
|
model_key = user_model
|
|
model_ocid = supported[user_model]["id"]
|
|
defaults = supported[user_model].get("params", {}).copy()
|
|
elif isinstance(user_model, str) and user_model.startswith("ocid1.generativeaimodel"):
|
|
model_ocid = user_model
|
|
defaults = {}
|
|
else:
|
|
if path_model_id and path_model_id.startswith("ocid1.generativeaimodel"):
|
|
model_ocid = path_model_id
|
|
defaults = {}
|
|
elif path_model_id in supported:
|
|
model_key = path_model_id
|
|
model_ocid = supported[path_model_id]["id"]
|
|
defaults = supported[path_model_id].get("params", {}).copy()
|
|
else:
|
|
raise ValueError("Modelo ausente ou não suportado: use um dos "
|
|
f"{list(supported.keys())} ou forneça um OCID válido.")
|
|
|
|
overrides = {}
|
|
for k in [
|
|
"temperature", "top_p", "top_k", "max_tokens", "frequency_penalty", "presence_penalty",
|
|
"reasoning_effort", "verbosity", "max_completion_tokens"
|
|
]:
|
|
if k in body and k in body and body[k] is not None:
|
|
overrides[k] = body[k]
|
|
|
|
merged = {**defaults, **overrides}
|
|
return {"model_key": model_key, "model_ocid": model_ocid, "params": merged}
|
|
|
|
def to_oci_messages(openai_messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
|
|
oci_msgs: List[Dict[str, Any]] = []
|
|
for m in openai_messages:
|
|
role = ROLE_MAP.get(str(m.get("role", "")).lower(), "USER")
|
|
content = m.get("content", "")
|
|
|
|
parts: List[Dict[str, Any]] = []
|
|
if isinstance(content, list):
|
|
for p in content:
|
|
if isinstance(p, dict) and p.get("type") == "text":
|
|
txt = p.get("text", "")
|
|
if txt: parts.append({"type": "TEXT", "text": txt})
|
|
elif isinstance(p, dict) and p.get("type") == "image_url":
|
|
url = p.get("image_url", {})
|
|
if isinstance(url, dict): url = url.get("url", "")
|
|
if isinstance(url, str) and url:
|
|
data_url = ensure_data_url(url)
|
|
parts.append({"type": "IMAGE_URL", "url": data_url})
|
|
elif isinstance(p, str):
|
|
parts.append({"type": "TEXT", "text": p})
|
|
elif isinstance(content, str):
|
|
parts.append({"type": "TEXT", "text": content})
|
|
else:
|
|
parts.append({"type": "TEXT", "text": json.dumps(content, ensure_ascii=False)})
|
|
|
|
oci_msgs.append({"role": role, "content": parts})
|
|
return oci_msgs
|
|
|
|
def build_oci_chat_payload(messages: List[Dict[str, Any]], params: Dict[str, Any]) -> Dict[str, Any]:
|
|
payload = {"messages": messages}
|
|
if "temperature" in params: payload["temperature"] = params["temperature"]
|
|
if "top_p" in params: payload["top_p"] = params["top_p"]
|
|
if "top_k" in params: payload["top_k"] = params["top_k"]
|
|
if "frequency_penalty" in params: payload["frequency_penalty"] = params["frequency_penalty"]
|
|
if "presence_penalty" in params: payload["presence_penalty"] = params["presence_penalty"]
|
|
if "max_completion_tokens" in params:
|
|
payload["max_completion_tokens"] = params["max_completion_tokens"]
|
|
elif "max_tokens" in params:
|
|
payload["max_completion_tokens"] = params["max_tokens"]
|
|
if "reasoning_effort" in params: payload["reasoning_effort"] = params["reasoning_effort"]
|
|
if "verbosity" in params: payload["verbosity"] = params["verbosity"]
|
|
return payload
|
|
|
|
def oci_chat_invoke(region: str, compartment_id: str, model_ocid: str, oci_payload: Dict[str, Any]) -> Dict[str, Any]:
|
|
print(">>> OCI CHAT REQUEST (payload que será enviado):")
|
|
print(json.dumps(oci_payload, ensure_ascii=False, indent=2))
|
|
|
|
if TEST_MODE:
|
|
return {
|
|
"dry_run": True,
|
|
"note": "TEST_MODE=True — retorno simulado.",
|
|
"payload": oci_payload,
|
|
"output_text": "[dry-run] ambiente de teste — valide o payload impresso no console."
|
|
}
|
|
|
|
try:
|
|
endpoint = f"https://inference.generativeai.{region}.oci.oraclecloud.com"
|
|
client = oci.generative_ai_inference.GenerativeAiInferenceClient(
|
|
config=config, service_endpoint=endpoint, retry_strategy=oci.retry.NoneRetryStrategy(), timeout=(10, 240)
|
|
)
|
|
|
|
chat_detail = oci.generative_ai_inference.models.ChatDetails()
|
|
generic = oci.generative_ai_inference.models.GenericChatRequest()
|
|
generic.api_format = oci.generative_ai_inference.models.BaseChatRequest.API_FORMAT_GENERIC
|
|
|
|
sdk_messages = []
|
|
for m in oci_payload["messages"]:
|
|
sdk_msg = oci.generative_ai_inference.models.Message()
|
|
sdk_msg.role = m["role"]
|
|
parts = []
|
|
for c in m["content"]:
|
|
ctype = c.get("type")
|
|
if ctype == "TEXT":
|
|
tc = oci.generative_ai_inference.models.TextContent(); tc.text = c.get("text", ""); parts.append(tc)
|
|
elif ctype == "IMAGE_URL":
|
|
ic = oci.generative_ai_inference.models.ImageContent()
|
|
iu = oci.generative_ai_inference.models.ImageUrl(); iu.url = c.get("url", "")
|
|
ic.image_url = iu; parts.append(ic)
|
|
sdk_msg.content = parts
|
|
sdk_messages.append(sdk_msg)
|
|
|
|
generic.messages = sdk_messages
|
|
|
|
if "temperature" in oci_payload: generic.temperature = oci_payload["temperature"]
|
|
if "top_p" in oci_payload: generic.top_p = oci_payload["top_p"]
|
|
if "top_k" in oci_payload: generic.top_k = oci_payload["top_k"]
|
|
if "frequency_penalty" in oci_payload: generic.frequency_penalty = oci_payload["frequency_penalty"]
|
|
if "presence_penalty" in oci_payload: generic.presence_penalty = oci_payload["presence_penalty"]
|
|
if "max_completion_tokens" in oci_payload: generic.max_tokens = oci_payload["max_completion_tokens"]
|
|
|
|
chat_detail.serving_mode = oci.generative_ai_inference.models.OnDemandServingMode(model_id=model_ocid)
|
|
chat_detail.chat_request = generic
|
|
chat_detail.compartment_id = compartment_id
|
|
|
|
chat_response = client.chat(chat_detail)
|
|
data = chat_response.data
|
|
|
|
if hasattr(data, "chat_response") and data.chat_response and data.chat_response.choices:
|
|
choice = data.chat_response.choices[0]
|
|
text = None
|
|
if choice.message and choice.message.content:
|
|
for block in choice.message.content:
|
|
if hasattr(block, "text") and block.text:
|
|
text = block.text; break
|
|
return {"output_text": text, "raw": "sdk"}
|
|
return {"output_text": None, "raw": "unknown"}
|
|
except Exception as e:
|
|
return {"error": f"Falha ao chamar OCI: {e}"}
|
|
|
|
def to_openai_chat_response(model_label: str, content_text: str, finish_reason: str = "stop") -> Dict[str, Any]:
|
|
now = int(time.time()); rid = f"chatcmpl-{uuid.uuid4().hex[:24]}"
|
|
return {
|
|
"id": rid, "object": "chat.completion", "created": now, "model": model_label,
|
|
"choices": [{"index": 0, "message": {"role": "assistant", "content": content_text}, "finish_reason": finish_reason}],
|
|
"usage": {"prompt_tokens": None, "completion_tokens": None, "total_tokens": None}
|
|
}
|
|
|
|
def to_openai_text_response(model_label: str, content_text: str, finish_reason: str = "stop") -> Dict[str, Any]:
|
|
now = int(time.time()); rid = f"cmpl-{uuid.uuid4().hex[:24]}"
|
|
return {
|
|
"id": rid, "object": "text_completion", "created": now, "model": model_label,
|
|
"choices": [{"index": 0, "text": content_text, "finish_reason": finish_reason, "logprobs": None}],
|
|
"usage": {"prompt_tokens": None, "completion_tokens": None, "total_tokens": None}
|
|
}
|
|
|
|
def sse_chat_stream(model_label: str, full_text: str) -> Generator[str, None, None]:
|
|
rid = f"chatcmpl-{uuid.uuid4().hex[:24]}"; now = int(time.time())
|
|
first = {"id": rid, "object": "chat.completion.chunk", "created": now, "model": model_label,
|
|
"choices": [{"index": 0, "delta": {"role": "assistant"}, "finish_reason": None}]}
|
|
yield f"data: {json.dumps(first)}\n\n"
|
|
for ch in full_text or "":
|
|
chunk = {"id": rid, "object": "chat.completion.chunk", "created": now, "model": model_label,
|
|
"choices": [{"index": 0, "delta": {"content": ch}, "finish_reason": None}]}
|
|
yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
|
|
endchunk = {"id": rid, "object": "chat.completion.chunk", "created": now, "model": model_label,
|
|
"choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}]}
|
|
yield f"data: {json.dumps(endchunk)}\n\n"
|
|
yield "data: [DONE]\n\n"
|
|
|
|
# ===== Helpers OpenAI-v1 p/ Agents (sem session_id em URL) =====
|
|
|
|
def _messages_to_text(messages: List[Dict[str, Any]]) -> str:
|
|
"""
|
|
Concatena conteúdo textual de mensagens OpenAI-like para enviar a um GenAI Agent.
|
|
Ignora imagens/partes não-textuais. Mantém a ordem.
|
|
"""
|
|
if not isinstance(messages, list):
|
|
return ""
|
|
chunks = []
|
|
for m in messages:
|
|
content = m.get("content", "")
|
|
if isinstance(content, str):
|
|
chunks.append(content)
|
|
elif isinstance(content, list):
|
|
for p in content:
|
|
if isinstance(p, dict) and p.get("type") == "text":
|
|
t = p.get("text", "")
|
|
if t:
|
|
chunks.append(t)
|
|
elif isinstance(p, str):
|
|
chunks.append(p)
|
|
elif content:
|
|
try:
|
|
chunks.append(json.dumps(content, ensure_ascii=False))
|
|
except Exception:
|
|
pass
|
|
return "\n".join(chunks).strip()
|
|
|
|
def _coerce_to_text(val: Any) -> str:
|
|
if val is None:
|
|
return ""
|
|
if isinstance(val, str):
|
|
return val
|
|
try:
|
|
if isinstance(val, dict):
|
|
if isinstance(val.get("text"), str):
|
|
return val["text"]
|
|
if isinstance(val.get("content"), str):
|
|
return val["content"]
|
|
if isinstance(val.get("content"), dict) and isinstance(val["content"].get("text"), str):
|
|
return val["content"]["text"]
|
|
if isinstance(val.get("content"), list):
|
|
for c in val["content"]:
|
|
if isinstance(c, dict) and isinstance(c.get("text"), str):
|
|
return c["text"]
|
|
data = val.get("data")
|
|
if isinstance(data, dict):
|
|
for key in ("message", "output", "text"):
|
|
if isinstance(data.get(key), str):
|
|
return data[key]
|
|
if isinstance(data.get("content"), dict) and isinstance(data["content"].get("text"), str):
|
|
return data["content"]["text"]
|
|
if isinstance(data.get("content"), list):
|
|
for c in data["content"]:
|
|
if isinstance(c, dict) and isinstance(c.get("text"), str):
|
|
return c["text"]
|
|
return json.dumps(val, ensure_ascii=False)
|
|
except Exception:
|
|
return str(val)
|
|
|
|
def _extract_agent_text(agent_payload: Any) -> str:
|
|
"""
|
|
Extrai o texto principal de respostas de GenAI Agent em diferentes formatos,
|
|
incluindo {"role":"AGENT","content":{"text":"..."}}.
|
|
"""
|
|
if agent_payload is None:
|
|
return ""
|
|
if isinstance(agent_payload, str):
|
|
try:
|
|
maybe_json = json.loads(agent_payload)
|
|
return _extract_agent_text(maybe_json)
|
|
except Exception:
|
|
return agent_payload
|
|
|
|
if isinstance(agent_payload, dict):
|
|
candidates = [
|
|
agent_payload.get("message"),
|
|
agent_payload.get("output"),
|
|
agent_payload.get("text"),
|
|
agent_payload.get("content"),
|
|
agent_payload.get("data"),
|
|
agent_payload.get("result"),
|
|
]
|
|
for c in candidates:
|
|
if c is not None:
|
|
txt = _coerce_to_text(c)
|
|
if txt:
|
|
return txt
|
|
return _coerce_to_text(agent_payload)
|
|
|
|
return _coerce_to_text(agent_payload)
|
|
|
|
def _resolve_agent_session(region: str, agent_endpoint_id: str) -> Dict[str, Any]:
|
|
"""
|
|
SEM session_id na URL. Cria/renova sessão automaticamente com base em:
|
|
- X-Channel (opcional, default "openai-v1")
|
|
- X-Cuid (opcional). Se ausente, deriva a partir do header de auth ou gera determinístico.
|
|
"""
|
|
channel = request.headers.get("X-Channel") or "openai-v1"
|
|
cuid = request.headers.get("X-Cuid")
|
|
if not cuid:
|
|
seed = request.headers.get("Authorization") or request.headers.get("X-API-Key") or uuid.uuid4().hex
|
|
cuid = uuid.uuid5(uuid.NAMESPACE_OID, seed).hex
|
|
return session_controller(region, agent_endpoint_id, channel, cuid)
|
|
|
|
def _agent_openai_like_answer(model_label: str, agent_payload: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""
|
|
Mapeia a resposta do Agent para o formato OpenAI chat.completion.
|
|
Garante que 'choices[0].message.content' seja SEMPRE string.
|
|
"""
|
|
text = _extract_agent_text(agent_payload)
|
|
return to_openai_chat_response(model_label, text)
|
|
|
|
# ==========================
|
|
# Endpoints nativos
|
|
# ==========================
|
|
|
|
@app.route("/", methods=["GET"])
|
|
def test():
|
|
return jsonify({"test": "ok"})
|
|
|
|
@app.route("/test/<myvar>/copy", methods=["GET"])
|
|
def var_copy(myvar):
|
|
return jsonify({"myvar": myvar})
|
|
|
|
@app.route("/genai-agent/<region>/<agent_endpoint_id>/session", methods=["POST"])
|
|
def manage_session(region, agent_endpoint_id):
|
|
data = request.get_json() or {}
|
|
print(">>> /genai-agent/.../session payload recebido:")
|
|
channel = data.get("channel"); cuid = data.get("cuid")
|
|
if not all([channel, cuid]):
|
|
return jsonify({"error": "Parâmetros 'channel' e 'cuid' são obrigatórios"}), 400
|
|
response_data = session_controller(region, agent_endpoint_id, channel, cuid)
|
|
return jsonify(response_data)
|
|
|
|
@app.route("/genai-agent/<region>/<agent_endpoint_id>/<session_id>/chat", methods=["POST"])
|
|
def agent_chat(region, agent_endpoint_id, session_id):
|
|
data = request.get_json() or {}
|
|
print(">>> /genai-agent/.../chat payload recebido:")
|
|
user_message = data.get("userMessage")
|
|
if not user_message:
|
|
return jsonify({"error": "userMessage é obrigatório"}), 400
|
|
response_data = ask_agent(region, agent_endpoint_id, session_id, user_message)
|
|
return jsonify({"agentResponse": response_data})
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<model_id>/inference", methods=["POST"])
|
|
def inference(region, compartment_id, model_id):
|
|
data = request.get_json() or {}
|
|
print(">>> /inference request body:")
|
|
prompt = data.get("prompt")
|
|
if not prompt:
|
|
return jsonify({"error": "Campo 'prompt' é obrigatório."}), 400
|
|
response_data = call_inference_model(region, compartment_id, model_id, prompt)
|
|
return jsonify(response_data)
|
|
|
|
# ==========================
|
|
# Endpoints OpenAI v1 compat — CHAT (LLMs)
|
|
# ==========================
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/chat/completions", methods=["POST"])
|
|
def v1_chat_completions(region, compartment_id, path_model_id):
|
|
try:
|
|
body = request.get_json(force=True, silent=False) or {}
|
|
except Exception as e:
|
|
return jsonify({"error": f"JSON inválido: {e}"}), 400
|
|
|
|
print(">>> /v1/chat/completions body recebido:")
|
|
print(body)
|
|
|
|
try:
|
|
resolved = resolve_model_and_params(body, path_model_id)
|
|
except Exception as e:
|
|
return jsonify({"error": str(e)}), 400
|
|
|
|
model_label = body.get("model") or resolved["model_key"] or resolved["model_ocid"]
|
|
msgs = body.get("messages") or []
|
|
if not isinstance(msgs, list) or not msgs:
|
|
return jsonify({"error": "Campo 'messages' é obrigatório e deve ser uma lista."}), 400
|
|
|
|
oci_msgs = to_oci_messages(msgs)
|
|
oci_payload = build_oci_chat_payload(oci_msgs, resolved["params"])
|
|
oci_result = oci_chat_invoke(region, compartment_id, resolved["model_ocid"], oci_payload)
|
|
|
|
if isinstance(oci_result, dict):
|
|
output_text = (
|
|
oci_result.get("output_text")
|
|
or oci_result.get("generated_text")
|
|
or oci_result.get("inference_response", {}).get("output_text")
|
|
or oci_result.get("payload", {}).get("output_text")
|
|
)
|
|
else:
|
|
output_text = None
|
|
|
|
if output_text is None:
|
|
output_text = json.dumps(oci_result, ensure_ascii=False)
|
|
|
|
if body.get("stream") is True:
|
|
return Response(stream_with_context(sse_chat_stream(model_label, output_text)),
|
|
mimetype="text/event-stream")
|
|
|
|
return jsonify(to_openai_chat_response(model_label, output_text))
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/completions", methods=["POST"])
|
|
def v1_text_completions(region, compartment_id, path_model_id):
|
|
try:
|
|
body = request.get_json(force=True, silent=False) or {}
|
|
except Exception as e:
|
|
return jsonify({"error": f"JSON inválido: {e}"}), 400
|
|
|
|
print(">>> /v1/completions body recebido:")
|
|
print(body)
|
|
|
|
try:
|
|
resolved = resolve_model_and_params(body, path_model_id)
|
|
except Exception as e:
|
|
return jsonify({"error": str(e)}), 400
|
|
|
|
model_label = body.get("model") or resolved["model_key"] or resolved["model_ocid"]
|
|
prompt = body.get("prompt")
|
|
if prompt is None:
|
|
return jsonify({"error": "Campo 'prompt' é obrigatório."}), 400
|
|
|
|
prompt_text = "\n".join([str(p) for p in prompt]) if isinstance(prompt, list) else str(prompt)
|
|
msgs = [{"role": "user", "content": prompt_text}]
|
|
oci_msgs = to_oci_messages(msgs)
|
|
oci_payload = build_oci_chat_payload(oci_msgs, resolved["params"])
|
|
oci_result = oci_chat_invoke(region, compartment_id, resolved["model_ocid"], oci_payload)
|
|
|
|
if isinstance(oci_result, dict):
|
|
output_text = (
|
|
oci_result.get("output_text")
|
|
or oci_result.get("generated_text")
|
|
or oci_result.get("inference_response", {}).get("output_text")
|
|
or oci_result.get("payload", {}).get("output_text")
|
|
)
|
|
else:
|
|
output_text = None
|
|
|
|
if output_text is None:
|
|
output_text = json.dumps(oci_result, ensure_ascii=False)
|
|
|
|
if body.get("stream") is True:
|
|
return Response(stream_with_context(sse_chat_stream(model_label, output_text)),
|
|
mimetype="text/event-stream")
|
|
|
|
return jsonify(to_openai_text_response(model_label, output_text))
|
|
|
|
# ==========================
|
|
# OpenAI v1 compatibility — GenAI Agents (auto-sessão com retry 409)
|
|
# ==========================
|
|
|
|
@app.route("/genai-agent/<region>/<agent_endpoint_id>/v1/chat/completions", methods=["POST"])
|
|
def agent_v1_chat_completions_auto(region, agent_endpoint_id):
|
|
"""
|
|
OpenAI v1 compat (SEM session_id na URL): cria/renova uma sessão automaticamente.
|
|
Faz retry 1x em caso de 409 (sessão inválida/expirada).
|
|
"""
|
|
try:
|
|
body = request.get_json(force=True, silent=False) or {}
|
|
except Exception as e:
|
|
return jsonify({"error": f"JSON inválido: {e}"}), 400
|
|
|
|
messages = body.get("messages")
|
|
if not messages or not isinstance(messages, list):
|
|
return jsonify({"error": "Campo 'messages' é obrigatório e deve ser uma lista."}), 400
|
|
|
|
# cria/renova sessão automaticamente (primeira tentativa)
|
|
sess = _resolve_agent_session(region, agent_endpoint_id)
|
|
if "error" in sess:
|
|
return jsonify({"error": f"Falha ao criar sessão: {sess['error']}"}), 500
|
|
session_id = sess.get("id")
|
|
session_key = sess.get("sessionKey") or ""
|
|
|
|
# extrai texto das mensagens OpenAI-like
|
|
user_message = _messages_to_text(messages)
|
|
if not user_message:
|
|
return jsonify({"error": "Não foi possível extrair texto de 'messages'."}), 400
|
|
|
|
# 1ª chamada ao Agent
|
|
agent_resp = ask_agent(region, agent_endpoint_id, session_id, user_message)
|
|
|
|
# Se veio erro 409, invalida e tenta 1x recriar sessão
|
|
if isinstance(agent_resp, dict) and agent_resp.get("_http_status") == 409:
|
|
print(f"[agent] 409 recebido. Invalidando sessão {session_key} e recriando...")
|
|
_invalidate_session(session_key)
|
|
sess2 = _resolve_agent_session(region, agent_endpoint_id)
|
|
if "error" in sess2:
|
|
return jsonify({
|
|
"error": {
|
|
"message": f"Agent chat 409 e falha ao recriar sessão: {sess2['error']}",
|
|
"type": "agent_session_error",
|
|
"code": 409
|
|
}
|
|
}), 502
|
|
session_id = sess2.get("id")
|
|
agent_resp = ask_agent(region, agent_endpoint_id, session_id, user_message)
|
|
|
|
# Se ainda for erro HTTP (qualquer status), retornar erro amigável
|
|
if isinstance(agent_resp, dict) and agent_resp.get("_http_status"):
|
|
status = agent_resp.get("_http_status")
|
|
raw_json = agent_resp.get("_raw_json")
|
|
raw_text = agent_resp.get("_raw_text")
|
|
msg = None
|
|
if isinstance(raw_json, dict):
|
|
msg = raw_json.get("message") or raw_json.get("error") or raw_json.get("title")
|
|
if not msg:
|
|
msg = json.dumps(raw_json, ensure_ascii=False)
|
|
if not msg:
|
|
msg = raw_text or f"HTTP {status} do Agent."
|
|
return jsonify({
|
|
"error": {
|
|
"message": msg,
|
|
"type": "agent_http_error",
|
|
"code": status
|
|
}
|
|
}), 502
|
|
|
|
model_label = body.get("model") or f"agent:{agent_endpoint_id}"
|
|
|
|
# streaming
|
|
if body.get("stream") is True:
|
|
stream_text = _extract_agent_text(agent_resp)
|
|
return Response(stream_with_context(sse_chat_stream(model_label, stream_text)),
|
|
mimetype="text/event-stream")
|
|
|
|
# normal
|
|
resp = _agent_openai_like_answer(model_label, agent_resp)
|
|
resp["_agent"] = {"session_id": session_id, "reused": sess.get("reused", False)}
|
|
return jsonify(resp)
|
|
|
|
@app.route("/genai-agent/<region>/<agent_endpoint_id>/v1/models", methods=["GET"])
|
|
def agent_v1_models(region, agent_endpoint_id):
|
|
"""
|
|
Retorna a lista de modelos suportados para compatibilidade OpenAI/v1 (agents).
|
|
Estrutura idêntica à rota /genai/.../v1/models.
|
|
"""
|
|
supported = get_supported_models() # HOT-RELOAD do JSON externo
|
|
data = []
|
|
for k, v in supported.items():
|
|
data.append({
|
|
"id": k,
|
|
"object": "model",
|
|
"owned_by": "oci.genai.agent",
|
|
"ocid": v.get("id"),
|
|
"params": v.get("params", {}),
|
|
"region": region,
|
|
"agent_endpoint": agent_endpoint_id
|
|
})
|
|
return jsonify({"object": "list", "data": data})
|
|
|
|
# ==========================
|
|
# Endpoints OpenAI v1 — FILES
|
|
# ==========================
|
|
|
|
def _files_upload_handler():
|
|
if "file" not in request.files:
|
|
return jsonify({"error": "Campo 'file' é obrigatório"}), 400
|
|
f = request.files["file"]
|
|
result = upload_file_to_bucket(f, f.filename)
|
|
return jsonify(result)
|
|
|
|
def _files_list_handler():
|
|
if TEST_MODE:
|
|
return jsonify({"data": [
|
|
{"id": fid, "object": "file", "filename": os.path.basename(obj), "bytes": 0}
|
|
for fid, obj in FILE_INDEX.items()
|
|
]})
|
|
resp = object_client.list_objects(namespace, BUCKET_NAME, prefix=UPLOAD_PREFIX)
|
|
files = []
|
|
for obj in resp.data.objects:
|
|
files.append({
|
|
"id": f"file-{uuid.uuid4().hex[:12]}",
|
|
"object": "file",
|
|
"filename": obj.name.replace(UPLOAD_PREFIX, ""),
|
|
"bytes": obj.size
|
|
})
|
|
return jsonify({"data": files})
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/files", methods=["POST"])
|
|
def v1_files_upload(region=None, compartment_id=None, path_model_id=None):
|
|
return _files_upload_handler()
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/files", methods=["GET"])
|
|
def v1_files_list(region=None, compartment_id=None, path_model_id=None):
|
|
return _files_list_handler()
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/files/<file_id>/content", methods=["GET"])
|
|
def v1_files_content(file_id, region=None, compartment_id=None, path_model_id=None):
|
|
if TEST_MODE:
|
|
return jsonify({"note": "TEST_MODE — conteúdo não disponível"}), 200
|
|
obj = FILE_INDEX.get(file_id)
|
|
if not obj:
|
|
return jsonify({"error": "file_id não encontrado neste servidor"}), 404
|
|
obj_resp = object_client.get_object(namespace, BUCKET_NAME, obj)
|
|
data = obj_resp.data.content
|
|
filename = os.path.basename(obj)
|
|
return send_file(
|
|
io.BytesIO(data.read()),
|
|
mimetype=guess_mime(filename, "application/octet-stream"),
|
|
as_attachment=False,
|
|
download_name=filename
|
|
)
|
|
|
|
# ==========================
|
|
# Endpoints OpenAI v1 — IMAGES (gera/edita/varia) com retorno via PAR
|
|
# ==========================
|
|
|
|
def _store_image_bytes_and_return_url(image_bytes: bytes, filename: str) -> str:
|
|
if TEST_MODE:
|
|
return f"https://objectstorage.{region}.oraclecloud.com/test/{UPLOAD_PREFIX}{uuid.uuid4().hex}_{filename}"
|
|
object_name = f"{UPLOAD_PREFIX}{uuid.uuid4().hex}_{filename}"
|
|
object_client.put_object(
|
|
namespace, BUCKET_NAME, object_name, image_bytes, content_type=guess_mime(filename, "image/png")
|
|
)
|
|
return create_par_for_object(object_name, hours_valid=24)
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/images/generations", methods=["POST"])
|
|
def v1_images_generations(region=None, compartment_id=None, path_model_id=None):
|
|
body = request.form or request.get_json(force=True, silent=True) or {}
|
|
prompt = body.get("prompt")
|
|
if not prompt:
|
|
return jsonify({"error": "Campo 'prompt' é obrigatório"}), 400
|
|
png_bytes = base64.b64decode(
|
|
"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHuwKp9w8H2AAAAABJRU5ErkJggg=="
|
|
)
|
|
url = _store_image_bytes_and_return_url(png_bytes, "generation.png")
|
|
return jsonify({"created": int(time.time()), "data": [{"url": url}]})
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/images/edits", methods=["POST"])
|
|
def v1_images_edits(region=None, compartment_id=None, path_model_id=None):
|
|
if "image" not in request.files:
|
|
return jsonify({"error": "Campo 'image' (multipart) é obrigatório"}), 400
|
|
_ = request.form.get("prompt", "")
|
|
base_img = request.files["image"].read()
|
|
url = _store_image_bytes_and_return_url(base_img, "edit.png")
|
|
return jsonify({"created": int(time.time()), "data": [{"url": url, "note": "mock edit"}]})
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/images/variations", methods=["POST"])
|
|
def v1_images_variations(region=None, compartment_id=None, path_model_id=None):
|
|
if "image" not in request.files:
|
|
return jsonify({"error": "Campo 'image' (multipart) é obrigatório"}), 400
|
|
base_img = request.files["image"].read()
|
|
url = _store_image_bytes_and_return_url(base_img, "variation.png")
|
|
return jsonify({"created": int(time.time()), "data": [{"url": url, "note": "mock variation"}]})
|
|
|
|
# ==========================
|
|
# Endpoint OpenAI v1 /models (LLMs)
|
|
# ==========================
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/models", methods=["GET"])
|
|
def v1_models(region, compartment_id, path_model_id):
|
|
"""
|
|
Lista os modelos disponíveis (do JSON hot-reload), em formato OpenAI-like.
|
|
Ignora path_model_id (mantido apenas para compat. com o padrão de URL existente).
|
|
"""
|
|
supported = get_supported_models()
|
|
data = []
|
|
for k, v in supported.items():
|
|
data.append({
|
|
"id": k, # expõe o apelido p/ uso direto em { model: "<apelido>" }
|
|
"object": "model",
|
|
"owned_by": "oci.genai",
|
|
"ocid": v.get("id"),
|
|
"params": v.get("params", {})
|
|
})
|
|
return jsonify({"object": "list", "data": data})
|
|
|
|
# ==========================
|
|
# Main
|
|
# ==========================
|
|
|
|
if __name__ == '__main__':
|
|
# Observação: para produção, use um servidor WSGI (gunicorn/uwsgi) atrás de um proxy.
|
|
app.run(host='0.0.0.0', port=8000)
|