EnCoach Odoo 19 custom modules

Full backend implementation with custom Odoo modules:
- encoach_api: Core API, user management, JWT auth
- encoach_exam: Exam generation (reading, writing, listening, speaking)
- encoach_evaluate: AI-powered evaluation (writing, speaking)
- encoach_training: Training tips and walkthrough
- encoach_storage: File storage management
- encoach_payment: Stripe, PayPal, Paymob integration
- encoach_mail: Email notifications

Made-with: Cursor

encoach_ai/services/__init__.py

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+from . import openai_service

encoach_ai/services/openai_service.py

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+import json
+import logging
+
+import tiktoken
+from openai import OpenAI
+from tenacity import retry, stop_after_attempt, wait_exponential
+
+from ..models.constants import BLACKLISTED_WORDS
+
+_logger = logging.getLogger(__name__)
+
+TOKEN_RESERVE = 300
+MODEL_TOKEN_LIMIT = 4097
+
+
+class EncoachOpenAIService:
+
+    def __init__(self, env):
+        self.env = env
+        api_key = (
+            env["ir.config_parameter"]
+            .sudo()
+            .get_param("encoach.openai_api_key", "")
+        )
+        if not api_key:
+            _logger.warning("OpenAI API key not configured (encoach.openai_api_key)")
+        self.client = OpenAI(api_key=api_key)
+
+    def prediction(
+        self,
+        model,
+        messages,
+        temperature=0.7,
+        response_format=None,
+        fields_to_check=None,
+        check_blacklisted=True,
+        max_retries=2,
+    ):
+        """Call OpenAI chat completion with validation and blacklist filtering.
+
+        Returns parsed JSON dict on success or None on failure.
+        """
+        if response_format is None:
+            response_format = {"type": "json_object"}
+
+        input_tokens = self._count_tokens(
+            " ".join(m.get("content", "") for m in messages if isinstance(m.get("content"), str)),
+            model,
+        )
+        max_tokens = max(MODEL_TOKEN_LIMIT - input_tokens - TOKEN_RESERVE, 256)
+
+        attempt = 0
+        while attempt <= max_retries:
+            try:
+                resp = self.client.chat.completions.create(
+                    model=model,
+                    messages=messages,
+                    temperature=temperature,
+                    max_tokens=max_tokens,
+                    response_format=response_format,
+                )
+                content = resp.choices[0].message.content
+                data = json.loads(content)
+
+                if check_blacklisted:
+                    text_to_check = content
+                    if fields_to_check and isinstance(data, dict):
+                        text_to_check = " ".join(
+                            str(data.get(f, "")) for f in fields_to_check
+                        )
+                    if self._check_blacklisted(text_to_check):
+                        _logger.info(
+                            "Blacklisted content detected (attempt %d/%d)",
+                            attempt + 1,
+                            max_retries + 1,
+                        )
+                        attempt += 1
+                        continue
+
+                return data
+
+            except json.JSONDecodeError:
+                _logger.warning("Invalid JSON from OpenAI (attempt %d)", attempt + 1)
+                attempt += 1
+            except Exception:
+                _logger.exception("OpenAI API error (attempt %d)", attempt + 1)
+                attempt += 1
+
+        _logger.error("OpenAI prediction failed after %d attempts", max_retries + 1)
+        return None
+
+    @staticmethod
+    def _check_blacklisted(text):
+        lower = text.lower()
+        return any(word in lower for word in BLACKLISTED_WORDS)
+
+    @staticmethod
+    def _count_tokens(text, model="gpt-4o"):
+        try:
+            encoding = tiktoken.encoding_for_model(model)
+        except KeyError:
+            encoding = tiktoken.get_encoding("cl100k_base")
+        return len(encoding.encode(text))