Delete pdf_processor.py

pdf_processor.py

@@ -1,50 +0,0 @@
-import json
-from langchain.document_loaders import PyPDFLoader
-from models import ExtractionResult, EvaluationResult
-from llm import get_llm
-
-llm = get_llm()
-
-def extract_answers_from_pdf(pdf_path: str) -> ExtractionResult:
-    """
-    Loads a PDF, extracts its content, and uses the LLM to output a JSON of the answers.
-    """
-    loader = PyPDFLoader(pdf_path)
-    pages = loader.load_and_split()
-    all_page_content = "\n".join(page.page_content for page in pages)
-    
-    # Prepare the prompt with the JSON schema.
-    extraction_schema = ExtractionResult.model_json_schema()
-    system_message = (
-        "You are a document analysis tool that extracts the options and correct answers from the provided document content. "
-        "The output must be a JSON object that strictly follows the schema: " + json.dumps(extraction_schema, indent=2)
-    )
-    user_message = (
-        "Please extract the correct answers and options (A, B, C, D, E) from the following document content:\n\n"
-        + all_page_content
-    )
-    prompt = system_message + "\n\n" + user_message
-    
-    response = llm.invoke(prompt, response_format={"type": "json_object"})
-    result = ExtractionResult.model_validate_json(response.content)
-    return result
-
-def evaluate_student(answer_key: ExtractionResult, student: ExtractionResult) -> EvaluationResult:
-    """
-    Compares the answer key with a student's answers and returns the evaluation result.
-    """
-    evaluation_schema = EvaluationResult.model_json_schema()
-    system_message = (
-        "You are an academic evaluation tool that compares the answer key with a student's answers. "
-        "Calculate the total marks, grade, and percentage based on the provided JSON objects. "
-        "The output must be a JSON object that strictly follows the schema: " + json.dumps(evaluation_schema, indent=2)
-    )
-    user_message = (
-        "Answer Key JSON:\n" + json.dumps(answer_key.model_dump(), indent=2) + "\n\n"
-        "Student Answer JSON:\n" + json.dumps(student.model_dump(), indent=2)
-    )
-    prompt = system_message + "\n\n" + user_message
-    
-    response = llm.invoke(prompt, response_format={"type": "json_object"})
-    evaluation_result = EvaluationResult.model_validate_json(response.content)
-    return evaluation_result