Fixed level issues

app/services/impl/exam/level/__init__.py

@@ -1,6 +1,12 @@
+from asyncio import gather
 from typing import Dict, Optional
+from uuid import uuid4
+
 from fastapi import UploadFile
 
+import random
+
+from app.configs.constants import EducationalContent
 from app.dtos.level import LevelExercisesDTO
 from app.repositories.abc import IDocumentStore
 from app.services.abc import (
@@ -41,48 +47,61 @@ class LevelService(ILevelService):
         )
 
 
-    async def upload_level(self, upload: UploadFile) -> Dict:
-        return await self._upload_module.generate_level_from_file(upload)
+    async def upload_level(self, upload: UploadFile, solutions: Optional[UploadFile] = None) -> Dict:
+        return await self._upload_module.generate_level_from_file(upload, solutions)
+
+    async def _generate_exercise(self, req_exercise, start_id):
+        if req_exercise.type == "mcBlank":
+            questions = await self._mc.gen_multiple_choice("blank_space", req_exercise.quantity, start_id)
+            questions["variant"] = "mcBlank"
+            questions["type"] = "multipleChoice"
+            questions["prompt"] = "Choose the correct word or group of words that completes the sentences."
+            return questions
+
+        elif req_exercise.type == "mcUnderline":
+            questions = await self._mc.gen_multiple_choice("underline", req_exercise.quantity, start_id)
+            questions["variant"] = "mcUnderline"
+            questions["type"] = "multipleChoice"
+            questions["prompt"] = "Choose the underlined word or group of words that is not correct."
+            return questions
+
+        elif req_exercise.type == "passageUtas":
+            topic = req_exercise.topic if req_exercise.topic else random.choice(EducationalContent.TOPICS)
+            exercise = await self._passage_utas.gen_reading_passage_utas(
+                start_id,
+                req_exercise.quantity,
+                topic,
+                req_exercise.text_size
+            )
+            exercise["prompt"] = "Read the text and answer the questions below."
+
+            return exercise
+
+        elif req_exercise.type == "fillBlanksMC":
+            exercise = await self._fill_blanks.gen_fill_blanks(
+                start_id,
+                req_exercise.quantity,
+                req_exercise.text_size,
+                req_exercise.topic
+            )
+            exercise["prompt"] = "Read the text below and choose the correct word for each space."
+            return exercise
 
     async def generate_exercises(self, dto: LevelExercisesDTO):
-        exercises = []
-        start_id = 1
-
+        start_ids = []
+        current_id = 1
         for req_exercise in dto.exercises:
-            if req_exercise.type == "multipleChoice":
-                questions = await self._mc.gen_multiple_choice("normal", req_exercise.quantity, start_id)
-                exercises.append(questions)
+            start_ids.append(current_id)
+            current_id += req_exercise.quantity
 
-            elif req_exercise.type == "mcBlank":
-                questions = await self._mc.gen_multiple_choice("blank_space", req_exercise.quantity, start_id)
-                questions["variant"] = "mc"
-                exercises.append(questions)
+        tasks = [
+            self._generate_exercise(req_exercise, start_id)
+            for req_exercise, start_id in zip(dto.exercises, start_ids)
+        ]
+        questions = await gather(*tasks)
+        questions = [{'id': str(uuid4()), **exercise} for exercise in questions]
 
-            elif req_exercise.type == "mcUnderline":
-                questions = await self._mc.gen_multiple_choice("underline", req_exercise.quantity, start_id)
-                exercises.append(questions)
-
-            elif req_exercise.type == "blankSpaceText":
-                questions = await self._blank_space.gen_blank_space_text_utas(
-                    req_exercise.quantity, start_id, req_exercise.text_size, req_exercise.topic
-                )
-                exercises.append(questions)
-
-            elif req_exercise.type == "passageUtas":
-                questions = await self._passage_utas.gen_reading_passage_utas(
-                    start_id, req_exercise.mc_qty, req_exercise.text_size
-                )
-                exercises.append(questions)
-
-            elif req_exercise.type == "fillBlanksMC":
-                questions = await self._passage_utas.gen_reading_passage_utas(
-                    start_id, req_exercise.mc_qty, req_exercise.text_size
-                )
-                exercises.append(questions)
-
-            start_id = start_id + req_exercise.quantity
-
-        return exercises
+        return {"exercises": questions}
 
     # Just here to support other modules that I don't know if they are supposed to still be used
     async def gen_multiple_choice(self, mc_variant: str, quantity: int, start_id: int = 1):

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

@@ -11,11 +11,12 @@ class FillBlanks:
 
 
     async def gen_fill_blanks(
-            self, quantity: int, start_id: int, size: int, topic=None
+            self, start_id: int, quantity: int, size: int = 300, topic=None
     ):
         if not topic:
             topic = random.choice(EducationalContent.MTI_TOPICS)
-
+        print(quantity)
+        print(start_id)
         messages = [
             {
                 "role": "system",
@@ -28,19 +29,18 @@ class FillBlanks:
             {
                 "role": "user",
                 "content": (
-                        f'From the generated text choose {quantity} words (cannot be sequential words) to replace '
-                        'once with {{id}} where id starts on ' + str(start_id) + ' and is incremented for each word. '
-                        'The ids must be ordered throughout the text and the words must be replaced only once. '
-                        'For each removed word you will place it in the solutions array and assign a letter from A to D,'
-                        ' then you will place that removed word and the chosen letter on the words array along with '
-                        ' other 3 other words for the remaining letter. This is a fill blanks question for an english '
-                        'exam, so don\'t choose words completely at random.'
+                    f'From the generated text choose exactly {quantity} words (cannot be sequential words) replace '
+                    'each with {{id}} (starting from ' + str(start_id) + ' and incrementing), then generate a '
+                    'JSON object containing: the modified text, a solutions array with each word\'s correct '
+                    'letter (A-D), and a words array containing each id with four options where one is '
+                    'the original word (matching the solution) and three are plausible but incorrect '
+                    'alternatives that maintain grammatical consistency. '
+                    'You cannot use repeated words!' #TODO: Solve this after
                 )
             }
         ]
-
         question = await self._llm.prediction(
-            GPTModels.GPT_4_O, messages,  ["question"], TemperatureSettings.GEN_QUESTION_TEMPERATURE
+            GPTModels.GPT_4_O, messages,  [], TemperatureSettings.GEN_QUESTION_TEMPERATURE
         )
         return {
             **question,
@@ -56,7 +56,7 @@ class FillBlanks:
             "solutions": [
                 {
                     "id": "",
-                    "solution": ""
+                    "solution": "<A,B,C or D>"
                 }
             ],
             "words": [

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

@@ -13,15 +13,12 @@ class PassageUtas:
         self._mc_variants = mc_variants
 
     async def gen_reading_passage_utas(
-            self, start_id, mc_quantity: int, topic: Optional[str] # sa_quantity: int,
+            self, start_id, mc_quantity: int, topic: Optional[str], word_size: Optional[int] # sa_quantity: int,
     ):
 
-        passage = await self._reading_service.generate_reading_passage(1, topic)
+        passage = await self._reading_service.generate_reading_passage(1, topic, word_size)
         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)
-
+        mc_exercises["type"] = "multipleChoice"
         """
             exercises: {
                 "shortAnswer": short_answer,
@@ -29,11 +26,12 @@ class PassageUtas:
             },
         """
         return {
-            "exercises": mc_exercises,
-            "text": {
+            **mc_exercises,
+            "passage": {
                 "content": passage["text"],
                 "title": passage["title"]
-            }
+            },
+            "mcVariant": "passageUtas"
         }
 
     async def _gen_short_answer_utas(self, text: str, start_id: int, sa_quantity: int):

app/services/impl/exam/level/upload.py

@@ -2,7 +2,7 @@ import aiofiles
 import os
 from logging import getLogger
 
-from typing import Dict, Any, Coroutine
+from typing import Dict, Any, Coroutine, Optional
 
 import pdfplumber
 from fastapi import UploadFile
@@ -21,7 +21,7 @@ class UploadLevelModule:
         self._logger = getLogger(__name__)
         self._llm = openai
 
-    async def generate_level_from_file(self, file: UploadFile) -> Dict[str, Any] | None:
+    async def generate_level_from_file(self, file: UploadFile, solutions: Optional[UploadFile]) -> Dict[str, Any] | None:
         ext, path_id = await FileHelper.save_upload(file)
         FileHelper.convert_file_to_pdf(
             f'./tmp/{path_id}/upload.{ext}', f'./tmp/{path_id}/exercises.pdf'

app/services/impl/third_parties/heygen/__init__.py

@@ -57,7 +57,7 @@ class Heygen(IVideoGeneratorService):
         )
 
 
-    async def pool_status(self, video_id: str) -> Task:
+    async def poll_status(self, video_id: str) -> Task:
         response = await self._http_client.get(self._GET_VIDEO_URL, headers=self._get_header, params={
             'video_id': video_id
         })
@@ -65,7 +65,6 @@ class Heygen(IVideoGeneratorService):
 
         status = response_data["data"]["status"]
         error = response_data["data"]["error"]
-
         if status != "completed" and error is None:
             self._logger.info(f"Status: {status}")
             return Task(

app/services/impl/third_parties/openai.py

@@ -73,7 +73,7 @@ class OpenAI(ILLMService):
             return await self._prediction(
                 model, messages, token_count, fields_to_check, temperature, (try_count + 1), check_blacklisted
             )
-
+        print(result)
         return json.loads(result)
 
     async def prediction_override(self, **kwargs):