Refactor grading criterion handling: remove structured grading criter…

…ion model, update imports

athena/athena/app.py

@@ -69,4 +69,4 @@ async def validation_exception_handler(request: Request, exc: RequestValidationE
     return JSONResponse(
         status_code=422,
         content={"detail": exc.errors()},
-    )
+    )

athena/athena/models/db_exercise.py

@@ -18,8 +18,6 @@ class DBExercise(Model, Base):
     grading_criteria = Column(JSON, nullable=True)
     meta = Column(JSON, nullable=False)
 
-    structured_grading_criterion = relationship("DBStructuredGradingCriterion", back_populates="exercise", uselist=False)  # Use uselist=False for one-to-one
-
     # Polymorphism, discriminator attribute
     type = Column(SqlEnum(ExerciseType), index=True, nullable=False)
 

athena/athena/schemas/__init__.py

@@ -13,5 +13,4 @@
 from .modeling_feedback import ModelingFeedback
 from .modeling_exercise import ModelingExercise
 from .modeling_submission import ModelingSubmission
-from .grading_criterion import GradingCriterion, StructuredGradingInstruction
-from .structured_grading_criterion import StructuredGradingCriterion
+from .grading_criterion import GradingCriterion, StructuredGradingInstruction, StructuredGradingCriterion

athena/athena/schemas/grading_criterion.py

@@ -23,4 +23,7 @@ class GradingCriterion(Schema, ABC):
     title: Optional[str] = Field(None, example="Some instructions")
     structured_grading_instructions: List[StructuredGradingInstruction] = Field(
         [], example=[{"credits": 1.0, "gradingScale": "Good", "instructionDescription": "Some instructions", "feedback": "Nicely done!", "usageCount": 1},
-                     {"credits": 0.0, "gradingScale": "Bad", "instructionDescription": "Some instructions", "feedback": "Try again!", "usageCount": 0}])
+                     {"credits": 0.0, "gradingScale": "Bad", "instructionDescription": "Some instructions", "feedback": "Try again!", "usageCount": 0}])
+
+class StructuredGradingCriterion(BaseModel):
+    criteria: List[GradingCriterion]

modules/modeling/module_modeling_llm/module_modeling_llm/core/generate_suggestions.py

@@ -1,4 +1,4 @@
-from athena.schemas.structured_grading_criterion import StructuredGradingCriterion
+from athena.schemas.grading_criterion import StructuredGradingCriterion
 from langchain_core.output_parsers import PydanticOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 

modules/modeling/module_modeling_llm/module_modeling_llm/core/get_structured_grading_instructions.py

@@ -1,13 +1,9 @@
-import hashlib
-import json
-from typing import Any, Dict, List, Optional
-from athena import logger
+from typing import List, Optional
 from athena.metadata import emit_meta
-from athena.storage.structured_grading_criterion_storage import get_structured_grading_criterion, store_structured_grading_criterion
 from langchain_core.output_parsers import PydanticOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 
-from athena.schemas import GradingCriterion, StructuredGradingCriterion
+from athena.schemas.grading_criterion import GradingCriterion, StructuredGradingCriterion
 from llm_core.utils.predict_and_parse import predict_and_parse
 from module_modeling_llm.config import BasicApproachConfig
 from module_modeling_llm.models.exercise_model import ExerciseModel
@@ -23,13 +19,6 @@ async def get_structured_grading_instructions(
 
     if grading_criteria:
         return StructuredGradingCriterion(criteria=grading_criteria)
-
-    # Check if we have cached instructions for this exercise
-    current_hash = get_grading_instructions_hash(exercise_model)
-    cached_instructions = get_structured_grading_criterion(exercise_model.exercise_id, current_hash)
-    if cached_instructions:
-        print("Using cached instructions")
-        return cached_instructions
 
     chat_prompt = ChatPromptTemplate.from_messages([
         ("system", config.generate_suggestions_prompt.structured_grading_instructions_system_message),
@@ -64,22 +53,5 @@ async def get_structured_grading_instructions(
 
     if not grading_instruction_result:
         raise ValueError("No structured grading instructions were returned by the model.")
-
-    # Cache the grading instructions
-    hash = get_grading_instructions_hash(exercise_model)
-    store_structured_grading_criterion(exercise_model.exercise_id, hash, grading_instruction_result)
-
-    return grading_instruction_result
-
-def get_grading_instructions_hash(exercise: ExerciseModel) -> str:
-
-    hashable_data = {
-        "problem_statement": exercise.problem_statement,
-        "grading_instructions": exercise.grading_instructions,
-        "sample_solution": exercise.transformed_example_solution,
-        "max_points": exercise.max_points,
-        "bonus_points": exercise.bonus_points,
-    }
 
-    json_string = json.dumps(hashable_data, sort_keys=True, default=str)
-    return hashlib.sha256(json_string.encode()).hexdigest()
+    return grading_instruction_result