Feat: Complete CBT PDF upload, AI parsing, and MinIO storage integration

backend/pdf_parser.py

@@ -0,0 +1,85 @@
+import fitz  # PyMuPDF
+import json
+import logging
+import asyncio
+from typing import List, Dict, Any
+import os
+from openai import AsyncOpenAI
+
+logger = logging.getLogger("pdf_parser")
+
+client = AsyncOpenAI(
+    base_url=os.getenv("LLM_BASE_URL", "http://host.docker.internal:11434/v1"),
+    api_key=os.getenv("OPENAI_API_KEY", "ollama"),
+)
+
+async def parse_pdf_to_questions(pdf_bytes: bytes, exam_pattern: str) -> List[Dict[str, Any]]:
+    """
+    Extracts text from a PDF and uses the LLM to structure it into a JSON array of questions.
+    """
+    text = ""
+    try:
+        doc = fitz.open(stream=pdf_bytes, filetype="pdf")
+        for page in doc:
+            text += page.get_text("text") + "\n\n"
+    except Exception as e:
+        logger.error(f"Failed to parse PDF with PyMuPDF: {e}")
+        return []
+
+    system_prompt = f"""
+    You are an AI expert at extracting {exam_pattern} test questions from messy PDF text.
+    The text contains complex Chemistry and Math formulas. 
+    CRITICAL: You MUST format all formulas perfectly in LaTeX wrapping inline math in $ and block math in $$.
+    
+    Extract the questions and return them STRICTLY as a JSON array of objects. 
+    If your API requires a JSON object root, return {{"questions": [...]}}.
+    
+    Schema per question:
+    {{
+      "subject": "Physics" | "Chemistry" | "Maths" | "Biology",
+      "topic": "String",
+      "subtopic": "String",
+      "difficulty": "Easy" | "Medium" | "Hard",
+      "type": "MCQ_Single" | "MCQ_Multiple" | "Numerical",
+      "question_text": "Full text of the question with LaTeX",
+      "options": {{"A": "Option A text", "B": "Option B text", "C": "Option C text", "D": "Option D text"}},
+      "correct_answer": {{"value": "A"}},
+      "solution_text": "Detailed step-by-step solution if available, else 'Solution not provided'",
+      "language": "English"
+    }}
+    """
+    
+    try:
+        truncated_text = text[:15000] 
+        
+        response = await client.chat.completions.create(
+            model=os.getenv("LLM_MODEL_NAME", "gemma"),
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": truncated_text}
+            ],
+            response_format={ "type": "json_object" }
+        )
+        
+        output = response.choices[0].message.content
+        if not output:
+            return []
+            
+        if "```json" in output:
+            output = output.split("```json")[1].split("```")[0].strip()
+        elif "```" in output:
+            output = output.split("```")[1].split("```")[0].strip()
+            
+        data = json.loads(output)
+        
+        if isinstance(data, list):
+            return data
+        elif isinstance(data, dict) and "questions" in data:
+            return data["questions"]
+        elif isinstance(data, dict):
+            return [data]
+        else:
+            return []
+    except Exception as e:
+        logger.error(f"LLM PDF parsing failed: {e}")
+        return []