Support evaluation-local configuration and names overrides

copienator/commands/gemini_for_enonce.py

@@ -48,9 +48,6 @@ def get_lcp(s1: str, s2: str) -> str:
     return lcp
 
 
-MODEL_ID = config.MODEL_LITE_ID
-api_key = config.API_KEY
-
 # --- Modèles pour la Requête 1 ---
 class StatementContent(BaseModel):
     @field_validator("question_content", "solution_content", "context_content",
@@ -246,7 +243,7 @@ def find_file(folder: Path, base_name: str) -> Path | None:
 
 def _request_rubrics(client, group_context_text: str):
     response = client.models.generate_content(
-        model=MODEL_ID,
+        model=config.MODEL_LITE_ID,
         contents=[types.Content(role="user", parts=[
             types.Part.from_text(text=PROMPT_4),
             types.Part.from_text(text=f"--- CONTENU DU GROUPE ---\n{group_context_text}"),
@@ -378,13 +375,13 @@ def refine_existing(workspace: EvaluationWorkspace, mode: str, *, api_client=Non
         questions[label] = "\n".join(parts)
     context = utils.enonce_total(workspace.root)
     if api_client is None:
-        if not api_key:
+        if not config.API_KEY:
             raise CliError("GEMINI_API_KEY is not configured")
-        api_client = genai.Client(api_key=api_key)
+        api_client = genai.Client(api_key=config.API_KEY)
 
     if mode == "groups":
         response = api_client.models.generate_content(
-            model=MODEL_ID,
+            model=config.MODEL_LITE_ID,
             contents=[types.Content(role="user", parts=[types.Part.from_text(text=(
                 "Regroupe ces questions d’examen en groupes cohérents pour la correction "
                 "et l’annotation, selon leurs dépendances et leur contexte commun. "
@@ -515,9 +512,9 @@ def process_exam(
     correction_text = correction_path.read_text(encoding="utf-8")
 
     if api_client is None:
-        if not api_key:
+        if not config.API_KEY:
             raise CliError("GEMINI_API_KEY is not configured")
-        api_client = genai.Client(api_key=api_key)
+        api_client = genai.Client(api_key=config.API_KEY)
     client = api_client
 
     # ==========================================
@@ -543,7 +540,7 @@ def process_exam(
 
     questions_data = ExamQuestions(questions=extract_stage(
         folder, 'questions', QuestionOnlyItem, 'question_content',
-        lambda _pending: client.models.generate_content(model=MODEL_ID, contents=contents_1, config=config_1),
+        lambda _pending: client.models.generate_content(model=config.MODEL_LITE_ID, contents=contents_1, config=config_1),
     ))
 
     # ==========================================
@@ -567,7 +564,7 @@ def process_exam(
             types.Part.from_text(text='--- QUESTIONS EXTRAITES ---\n' + selected.model_dump_json(indent=2)),
             types.Part.from_text(text=f'--- CORRECTION SOURCE ({correction_path.name}) ---\n{correction_text}'),
         ])]
-        return client.models.generate_content(model=MODEL_ID, contents=contents, config=config_2)
+        return client.models.generate_content(model=config.MODEL_LITE_ID, contents=contents, config=config_2)
     solutions_data = ExamSolutions(solutions=extract_stage(
         folder, 'solutions', SolutionOnlyItem, 'solution_content', request_solutions, labels=question_labels,
     ))
@@ -599,7 +596,7 @@ def process_exam(
             contents = [types.Content(role='user', parts=[*contents_3[0].parts,
                 types.Part.from_text(text='Produis des contextes UNIQUEMENT pour les labels cibles suivants : '
                                      + ', '.join(pending))])]
-        return client.models.generate_content(model=MODEL_ID, contents=contents, config=config_3)
+        return client.models.generate_content(model=config.MODEL_LITE_ID, contents=contents, config=config_3)
     context_data = ExamContext(contexts=extract_stage(
         folder, 'contexts', ExtractedContext, 'context_content', request_contexts, labels=question_labels,
     ))
@@ -1083,7 +1080,7 @@ def compile_existing_pdfs(workspace: EvaluationWorkspace) -> ExitCode:
             if not source_path.is_file():
                 source_path = None
             fingerprint = hashlib.sha256((utils.fragment_latex_header(source_path)
-                                          + utils.LATEX_AFTER + str(source_path)).encode()).hexdigest()
+                                          + config.LATEX_AFTER + str(source_path)).encode()).hexdigest()
             status = json.loads(status_path.read_text()) if status_path.exists() else {}
             failed_before = not status.get('complete') or status.get('preamble_hash') != fingerprint
             if (pdf_path.is_file() and pdf_path.stat().st_size > 0