Adds LLM-based test case generation API endpoints

back_end/settings.py

@@ -253,6 +253,12 @@
 SANDBOX_TIMEOUT = int(os.getenv('SANDBOX_TIMEOUT', '30'))  # API 請求超時（秒）
 SANDBOX_API_KEY = os.getenv('SANDBOX_API_KEY', '')  # API Key for authentication
 
+# ====================
+# LLM Test Generation Service Configuration
+# ====================
+LLM_TESTGEN_API_URL = os.getenv('LLM_TESTGEN_API_URL', 'http://34.81.90.111:8001')
+LLM_TESTGEN_TIMEOUT = int(os.getenv('LLM_TESTGEN_TIMEOUT', '120'))  # LLM 生成可能較慢，預設 120 秒
+
 # ====================
 # Backend Configuration
 # ====================

problems/services/llm_testgen.py

@@ -0,0 +1,352 @@
+"""
+LLM Test Data Generation Service
+
+封裝與 LLM 測資生成服務的 API 互動邏輯
+"""
+
+import requests
+import logging
+from io import BytesIO
+from typing import Optional, List, Dict, Any
+from django.conf import settings
+
+logger = logging.getLogger(__name__)
+
+# LLM 測資生成服務設定
+LLM_TESTGEN_API_URL = getattr(settings, 'LLM_TESTGEN_API_URL', 'http://34.81.90.111:8001')
+LLM_TESTGEN_TIMEOUT = getattr(settings, 'LLM_TESTGEN_TIMEOUT', 120)  # 120 秒超時（LLM 生成可能較慢）
+
+
+def get_solution_runtime(language: str) -> str:
+    """
+    將 Django 的語言名稱轉換成 LLM 服務的 runtime 格式
+
+    Django: 'c', 'cpp', 'python', 'java', 'javascript'
+    LLM Service: 'c', 'cpp', 'python', 'java'
+    """
+    language_map = {
+        'c': 'c',
+        'cpp': 'cpp',
+        'c++': 'cpp',
+        'python': 'python',
+        'py': 'python',
+        'java': 'java',
+        'javascript': 'python',  # LLM 服務不支援 JS，暫時用 python
+        'js': 'python',
+    }
+    return language_map.get(language.lower(), 'python')
+
+
+def upload_solution(source_code: str, language: str) -> Dict[str, Any]:
+    """
+    上傳正解程式到 LLM 服務
+
+    Args:
+        source_code: 正解程式碼
+        language: 程式語言 (c, cpp, python, java)
+
+    Returns:
+        dict: {
+            'ok': bool,
+            'solution_id': str (如果成功),
+            'error': str (如果失敗)
+        }
+    """
+    try:
+        runtime = get_solution_runtime(language)
+
+        # 建立檔案物件
+        extension_map = {
+            'c': 'c',
+            'cpp': 'cpp',
+            'python': 'py',
+            'java': 'java',
+        }
+        ext = extension_map.get(runtime, 'txt')
+        filename = f'solution.{ext}'
+
+        files = {
+            'file': (filename, BytesIO(source_code.encode('utf-8')), 'text/plain')
+        }
+        data = {
+            'runtime': runtime
+        }
+
+        url = f'{LLM_TESTGEN_API_URL}/api/upload-solution'
+        logger.info(f'Uploading solution to LLM service: {url}')
+
+        response = requests.post(
+            url,
+            files=files,
+            data=data,
+            timeout=LLM_TESTGEN_TIMEOUT
+        )
+
+        response.raise_for_status()
+        result = response.json()
+
+        logger.info(f'Upload solution response: {result}')
+        return result
+
+    except requests.exceptions.Timeout:
+        logger.error('Upload solution timeout')
+        return {'ok': False, 'error': 'LLM 服務連線逾時'}
+    except requests.exceptions.ConnectionError:
+        logger.error('Upload solution connection error')
+        return {'ok': False, 'error': 'LLM 服務連線失敗'}
+    except requests.exceptions.RequestException as e:
+        logger.error(f'Upload solution request error: {str(e)}')
+        return {'ok': False, 'error': f'請求錯誤: {str(e)}'}
+    except Exception as e:
+        logger.error(f'Upload solution error: {str(e)}')
+        return {'ok': False, 'error': f'未知錯誤: {str(e)}'}
+
+
+def generate_testcases(
+    problem_statement: str,
+    input_spec: str,
+    output_spec: Optional[str] = None,
+    constraints: Optional[str] = None,
+    subtasks: Optional[List[Dict]] = None,
+    num_cases: Optional[int] = None,
+    mode: str = 'LLM_DIRECT',
+    solution_id: Optional[str] = None,
+    examples: Optional[List[Dict[str, str]]] = None
+) -> Dict[str, Any]:
+    """
+    呼叫 LLM 服務生成測資
+
+    Args:
+        problem_statement: 題目敘述
+        input_spec: 輸入格式說明
+        output_spec: 輸出格式說明
+        constraints: 限制條件
+        subtasks: 子任務列表 [{'id': 1, 'name': 'subtask1', 'desc': '...', 'num': 5}]
+        num_cases: 測資數量（若無 subtasks）
+        mode: 模式 - 'LLM_INPUT_ONLY' 或 'LLM_DIRECT'
+        solution_id: 正解 ID（LLM_INPUT_ONLY 必填）
+        examples: 範例測資 [{'input': '...', 'output': '...'}]
+
+    Returns:
+        dict: {
+            'ok': bool,
+            'data': {...} (如果成功),
+            'error': str (如果失敗)
+        }
+    """
+    try:
+        payload = {
+            'problem_statement': problem_statement,
+            'input_spec': input_spec,
+            'mode': mode,
+        }
+
+        if output_spec:
+            payload['output_spec'] = output_spec
+
+        if constraints:
+            payload['constraints'] = constraints
+
+        if subtasks:
+            payload['subtasks'] = subtasks
+
+        if num_cases:
+            payload['num_cases'] = num_cases
+
+        if solution_id:
+            payload['solution_id'] = solution_id
+
+        if examples:
+            payload['examples'] = examples
+
+        url = f'{LLM_TESTGEN_API_URL}/api/generate-testcases'
+        logger.info(f'Generating testcases via LLM service: {url}')
+        logger.debug(f'Generate request payload: {payload}')
+
+        response = requests.post(
+            url,
+            json=payload,
+            timeout=LLM_TESTGEN_TIMEOUT
+        )
+
+        response.raise_for_status()
+        result = response.json()
+
+        logger.info(f'Generate testcases response ok: {result.get("ok")}')
+        return result
+
+    except requests.exceptions.Timeout:
+        logger.error('Generate testcases timeout')
+        return {'ok': False, 'error': 'LLM 服務連線逾時（生成測資可能需要較長時間）'}
+    except requests.exceptions.ConnectionError:
+        logger.error('Generate testcases connection error')
+        return {'ok': False, 'error': 'LLM 服務連線失敗'}
+    except requests.exceptions.RequestException as e:
+        logger.error(f'Generate testcases request error: {str(e)}')
+        return {'ok': False, 'error': f'請求錯誤: {str(e)}'}
+    except Exception as e:
+        logger.error(f'Generate testcases error: {str(e)}')
+        return {'ok': False, 'error': f'未知錯誤: {str(e)}'}
+
+
+def list_solutions() -> Dict[str, Any]:
+    """
+    列出所有已上傳的正解
+
+    Returns:
+        dict: API 回應
+    """
+    try:
+        url = f'{LLM_TESTGEN_API_URL}/api/solutions'
+        response = requests.get(url, timeout=LLM_TESTGEN_TIMEOUT)
+        response.raise_for_status()
+        return response.json()
+    except Exception as e:
+        logger.error(f'List solutions error: {str(e)}')
+        return {'error': str(e)}
+
+
+def delete_solution(solution_id: str) -> Dict[str, Any]:
+    """
+    刪除正解
+
+    Args:
+        solution_id: 正解 ID
+
+    Returns:
+        dict: API 回應
+    """
+    try:
+        url = f'{LLM_TESTGEN_API_URL}/api/solutions/{solution_id}'
+        response = requests.delete(url, timeout=LLM_TESTGEN_TIMEOUT)
+        response.raise_for_status()
+        return response.json()
+    except Exception as e:
+        logger.error(f'Delete solution error: {str(e)}')
+        return {'error': str(e)}
+
+
+def health_check() -> bool:
+    """
+    檢查 LLM 服務健康狀態
+
+    Returns:
+        bool: 服務是否正常
+    """
+    try:
+        url = f'{LLM_TESTGEN_API_URL}/health'
+        response = requests.get(url, timeout=10)
+        return response.status_code == 200
+    except Exception:
+        return False
+
+
+def generate_testcases_for_problem(problem) -> Dict[str, Any]:
+    """
+    根據題目資訊自動選擇模式並生成測資
+
+    Args:
+        problem: Problems model instance
+
+    Returns:
+        dict: {
+            'ok': bool,
+            'mode': str,
+            'data': {...},
+            'error': str (如果失敗)
+        }
+    """
+    from problems.models import Problem_subtasks
+
+    # 建構題目敘述
+    problem_statement = problem.description
+
+    # 輸入/輸出格式
+    input_spec = problem.input_description or ''
+    output_spec = problem.output_description or ''
+
+    # 限制條件（從 subtask_description 或其他欄位取得）
+    constraints = problem.subtask_description or problem.hint or ''
+
+    # 範例測資
+    examples = []
+    if problem.sample_input and problem.sample_output:
+        examples.append({
+            'input': problem.sample_input,
+            'output': problem.sample_output
+        })
+
+    # 取得 subtasks
+    subtasks_qs = Problem_subtasks.objects.filter(problem_id=problem.id).order_by('subtask_no')
+    subtasks = []
+
+    if subtasks_qs.exists():
+        for st in subtasks_qs:
+            subtasks.append({
+                'id': st.subtask_no,
+                'name': f'Subtask {st.subtask_no}',
+                'desc': st.description or '',
+                'num': st.num_testcases if hasattr(st, 'num_testcases') else 5
+            })
+
+    # 決定生成模式
+    has_solution = bool(problem.solution_code and problem.solution_code.strip())
+
+    if has_solution:
+        # 使用 LLM_INPUT_ONLY 模式：先上傳正解，再生成測資
+        logger.info(f'Problem {problem.id} has solution code, using LLM_INPUT_ONLY mode')
+
+        # 上傳正解
+        upload_result = upload_solution(
+            source_code=problem.solution_code,
+            language=problem.solution_code_language or 'python'
+        )
+
+        if not upload_result.get('ok'):
+            return {
+                'ok': False,
+                'mode': 'LLM_INPUT_ONLY',
+                'error': f"上傳正解失敗: {upload_result.get('error', '未知錯誤')}"
+            }
+
+        solution_id = upload_result.get('solution_id')
+
+        # 生成測資
+        gen_result = generate_testcases(
+            problem_statement=problem_statement,
+            input_spec=input_spec,
+            output_spec=output_spec,
+            constraints=constraints,
+            subtasks=subtasks if subtasks else None,
+            num_cases=5 if not subtasks else None,
+            mode='LLM_INPUT_ONLY',
+            solution_id=solution_id,
+            examples=examples if examples else None
+        )
+
+        # 清理上傳的正解
+        try:
+            delete_solution(solution_id)
+        except Exception as e:
+            logger.warning(f'Failed to delete solution {solution_id}: {str(e)}')
+
+        gen_result['mode'] = 'LLM_INPUT_ONLY'
+        return gen_result
+
+    else:
+        # 使用 LLM_DIRECT 模式：直接生成 input 和 output
+        logger.info(f'Problem {problem.id} has no solution code, using LLM_DIRECT mode')
+
+        gen_result = generate_testcases(
+            problem_statement=problem_statement,
+            input_spec=input_spec,
+            output_spec=output_spec,
+            constraints=constraints,
+            subtasks=subtasks if subtasks else None,
+            num_cases=5 if not subtasks else None,
+            mode='LLM_DIRECT',
+            examples=examples if examples else None
+        )
+
+        gen_result['mode'] = 'LLM_DIRECT'
+        return gen_result

problems/urls.py

@@ -13,6 +13,12 @@
     ProblemTestCaseListCreateView, ProblemTestCaseDetailView, ProblemTestCaseZipUploadView,
 )
 from .views.sandbox import ProblemTestCasePackageView
+from .views.llm_testgen import (
+    LLMTestGenHealthView,
+    LLMTestGenGenerateView,
+    LLMTestGenCustomView,
+    LLMTestGenSaveView,
+)
 
 router = DefaultRouter()
 router.register(r"problems", ProblemsViewSet, basename="problems")
@@ -46,6 +52,11 @@
     path("<int:pk>/checksum", ProblemTestCaseChecksumView.as_view(), name="problem-testcase-checksum"),
     path("<int:pk>/meta", ProblemTestCaseMetaView.as_view(), name="problem-testcase-meta"),
     path("<int:pk>/testdata", ProblemTestCasePackageView.as_view(), name="problem-testcase-package"),
+    # LLM 測資生成 API
+    path("llm-testgen/health", LLMTestGenHealthView.as_view(), name="llm-testgen-health"),
+    path("<int:pk>/llm-testgen/generate", LLMTestGenGenerateView.as_view(), name="llm-testgen-generate"),
+    path("<int:pk>/llm-testgen/custom", LLMTestGenCustomView.as_view(), name="llm-testgen-custom"),
+    path("<int:pk>/llm-testgen/save", LLMTestGenSaveView.as_view(), name="llm-testgen-save"),
     # 新標籤 API
     path("tags", TagListCreateView.as_view(), name="tag-list-create"),
     path("<int:pk>/tags", ProblemTagAddView.as_view(), name="problem-tag-add"),