Create pdf_processor.py

pdf_processor.py

@@ -0,0 +1,50 @@
+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)
+    
+    # Build the system message with JSON schema instructions.
+    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