Changes to endpoints so they allow to only get context and then the exercises as well as tidying up a bit

app/services/impl/exam/level/exercises/passage_utas.py

@@ -0,0 +1,93 @@
+from typing import Optional
+
+from app.configs.constants import GPTModels, TemperatureSettings
+from app.helpers import ExercisesHelper
+from app.services.abc import ILLMService, IReadingService
+
+
+class PassageUtas:
+
+    def __init__(self, llm: ILLMService, reading_service: IReadingService, mc_variants: dict):
+        self._llm = llm
+        self._reading_service = reading_service
+        self._mc_variants = mc_variants
+
+    async def gen_reading_passage_utas(
+            self, start_id, mc_quantity: int, topic: Optional[str] # sa_quantity: int,
+    ):
+
+        passage = await self._reading_service.generate_reading_passage(1, topic)
+        mc_exercises = await self._gen_text_multiple_choice_utas(passage["text"], start_id, mc_quantity)
+
+        #short_answer = await self._gen_short_answer_utas(passage["text"], start_id, sa_quantity)
+        # + sa_quantity, mc_quantity)
+
+        """
+            exercises: {
+                "shortAnswer": short_answer,
+                "multipleChoice": mc_exercises,
+            },
+        """
+        return {
+            "exercises": mc_exercises,
+            "text": {
+                "content": passage["text"],
+                "title": passage["title"]
+            }
+        }
+
+    async def _gen_short_answer_utas(self, text: str, start_id: int, sa_quantity: int):
+        json_format = {"questions": [{"id": 1, "question": "question", "possible_answers": ["answer_1", "answer_2"]}]}
+
+        messages = [
+            {
+                "role": "system",
+                "content": f'You are a helpful assistant designed to output JSON on this format: {json_format}'
+            },
+            {
+                "role": "user",
+                "content": (
+                    f'Generate {sa_quantity} short answer questions, and the possible answers, must have '
+                    f'maximum 3 words per answer, about this text:\n"{text}"'
+                )
+            },
+            {
+                "role": "user",
+                "content": f'The id starts at {start_id}.'
+            }
+        ]
+
+        question = await self._llm.prediction(
+            GPTModels.GPT_4_O, messages, ["questions"], TemperatureSettings.GEN_QUESTION_TEMPERATURE
+        )
+
+        return question["questions"]
+
+    async def _gen_text_multiple_choice_utas(self, text: str, start_id: int, mc_quantity: int):
+        json_template = self._mc_variants["text_mc_utas"]
+
+        messages = [
+            {
+                "role": "system",
+                "content": f'You are a helpful assistant designed to output JSON on this format: {json_template}'
+            },
+            {
+                "role": "user",
+                "content": f'Generate {mc_quantity} multiple choice questions of 4 options for this text:\n{text}'
+            },
+            {
+                "role": "user",
+                "content": 'Make sure every question only has 1 correct answer.'
+            }
+        ]
+
+        question = await self._llm.prediction(
+            GPTModels.GPT_4_O, messages, ["questions"], TemperatureSettings.GEN_QUESTION_TEMPERATURE
+        )
+
+        if len(question["questions"]) != mc_quantity:
+            return await self._gen_text_multiple_choice_utas(text, mc_quantity, start_id)
+        else:
+            response = ExercisesHelper.fix_exercise_ids(question, start_id)
+            response["questions"] = ExercisesHelper.randomize_mc_options_order(response["questions"])
+            return response