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Initial Persp generation

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sebastien
2026-07-11 20:30:27 +02:00
parent fd35675e4c
commit 09befe1d55
4 changed files with 175 additions and 14 deletions
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gemini_for_enonce.py
+132 -9
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@@ -42,11 +42,32 @@ class ExamSolutions(BaseModel):
# --- Modèles pour la Requête 3 ---
class ExtractedContext(BaseModel):
target_question_label: str = Field(description="The exact label of the FIRST question that comes immediately AFTER this information in the exam.")
last_question_label: str = Field(description="The exact label of the LAST question that uses or relies on this information.")
context_content: str = Field(description="The source text of the definitions, notations, or hypotheses, extracted from the enonce.")
class ExamContext(BaseModel):
contexts: List[ExtractedContext]
# --- Modèles pour la Requête 4 (Barèmes) ---
class RubricItem(BaseModel):
label: str = Field(description="The exact label of the question.")
rubric_content: str = Field(description="Le barème détaillé en français.")
class GroupRubrics(BaseModel):
rubrics: List[RubricItem]
PROMPT_4 = """Je te fournis les questions, le contexte éventuel, et les corrections pour un groupe de questions d'un examen.
Ta tâche :
Établir un barème de correction détaillé en français pour CHAQUE question.
Chaque question DOIT être notée sur exactement 4 points. Propose une répartition logique de ces points.
Par exemple :
- Au moins 2 points si le résultat est correct.
- Mettre la moitié des points si le raisonnement est correct mais pas le résultat.
- Retirer 1.5 points si les hypothèses d'un théorème ou d'une question précédente ne sont pas vérifiées.
Renvoie le résultat sous forme de liste JSON correspondant aux labels des questions fournies.
"""
# --- Modèle fusionné (pour le reste du script) ---
class QuestionItem(BaseModel):
label: str
@@ -54,6 +75,8 @@ class QuestionItem(BaseModel):
solution_content: str
class ContextItem(BaseModel):
target_question_label: str
last_question_label: str
content: str # Juste une string encapsulée pour le différencier facilement
class ExamExtraction(BaseModel):
@@ -91,7 +114,9 @@ PROMPT_3 = """I am providing:
Your task:
Extract important information necessary to understand the questions (e.g., definitions of objects, global notations, hypotheses, context) that are NOT part of the question texts themselves.
For each extracted piece of information, identify the label of the FIRST question that comes immediately AFTER this information in the exam.
For each extracted piece of information, identify:
1. The label of the FIRST question that comes immediately AFTER this information in the exam.
2. The label of the LAST question that uses or relies on this information.
Return the result as a JSON list.
"""
@@ -247,21 +272,45 @@ def process_exam(folder_path: str):
# ==========================================
sol_map = {s.label: s.solution_content for s in solutions_data.solutions}
# Map labels to their index to validate ordering
label_to_idx = {q.label: i for i, q in enumerate(questions_data.questions)}
for c in context_data.contexts:
first_idx = label_to_idx.get(c.target_question_label, -1)
last_idx = label_to_idx.get(c.last_question_label, -1)
# Enforce LAST is after (or equal to) FIRST
if first_idx != -1 and last_idx != -1 and last_idx < first_idx:
print(f"Warning: LAST question ({c.last_question_label}) is before FIRST ({c.target_question_label}). Fixing.")
c.last_question_label = c.target_question_label
elif last_idx == -1: # Fallback if invalid
c.last_question_label = c.target_question_label
# Grouper les contextes par label cible
ctx_map = {}
for c in context_data.contexts:
if c.target_question_label in ctx_map:
ctx_map[c.target_question_label] += "\n\n" + c.context_content
ctx_map[c.target_question_label]['content'] += "\n\n" + c.context_content
# Keep the furthest LAST question label
curr_last = ctx_map[c.target_question_label]['last']
if label_to_idx.get(c.last_question_label, -1) > label_to_idx.get(curr_last, -1):
ctx_map[c.target_question_label]['last'] = c.last_question_label
else:
ctx_map[c.target_question_label] = c.context_content
ctx_map[c.target_question_label] = {
'content': c.context_content,
'last': c.last_question_label
}
merged_items = []
for q in questions_data.questions:
# 1. S'il y a un contexte pour cette question, on l'insère d'abord dans la liste
if q.label in ctx_map:
merged_items.append(ContextItem(content=ctx_map[q.label]))
merged_items.append(ContextItem(
target_question_label=q.label,
last_question_label=ctx_map[q.label]['last'],
content=ctx_map[q.label]['content']
))
# 2. Puis on ajoute la question
sol_content = sol_map.get(q.label, "")
merged_items.append(QuestionItem(
label=q.label,
@@ -328,9 +377,11 @@ def process_exam(folder_path: str):
# ---- Transform labels, and check uniqueness
seen_labels = set()
label_updates = {}
for group in initial_groups:
for item in group:
if isinstance(item, QuestionItem):
orig_label = item.label
# 1. Transform label
item.label = item.label.replace("Exercice", "Ex")
item.label = item.label.replace(".", ")")
@@ -339,7 +390,14 @@ def process_exam(folder_path: str):
while item.label in seen_labels:
item.label = f"XX{item.label}"
seen_labels.add(item.label)
label_updates[orig_label] = item.label
# Sync the modified labels to ContextItem
for group in initial_groups:
for item in group:
if isinstance(item, ContextItem):
item.target_question_label = label_updates.get(item.target_question_label, item.target_question_label)
item.last_question_label = label_updates.get(item.last_question_label, item.last_question_label)
# --- WRITE TEXT FILES ---
print(f"Writing items files to {items_file.name} and {full_items_file.name}...")
@@ -438,7 +496,8 @@ def process_exam(folder_path: str):
edited_content = edited_content_raw.replace(' \\n ', '\n')
if orig_idx < len(extracted_data.items):
orig_item = extracted_data.items[orig_idx]
orig_item = extracted_data.items[orig_idx] # <-- Add this line
if isinstance(orig_item, QuestionItem):
current_group.append(QuestionItem(
label=new_label,
@@ -446,7 +505,11 @@ def process_exam(folder_path: str):
solution_content=orig_item.solution_content
))
elif isinstance(orig_item, ContextItem):
current_group.append(ContextItem(content=edited_content))
current_group.append(ContextItem(
target_question_label=orig_item.target_question_label,
last_question_label=orig_item.last_question_label,
content=edited_content
))
orig_idx += 1
if current_group:
@@ -467,7 +530,8 @@ def process_exam(folder_path: str):
sol_dir = folder / "Sol"
text2_dir = folder / "Text2"
sol2_dir = folder / "Sol2"
dirs = [text_dir, sol_dir, text2_dir, sol2_dir]
persp_dir = folder / "Persp"
dirs = [text_dir, sol_dir, text2_dir, sol2_dir, persp_dir]
import shutil
# Ask only if any directory already exists
@@ -502,6 +566,47 @@ def process_exam(folder_path: str):
if not labels:
continue # Skip if a group has no questions (only contexts)
# ==========================================
# REQUÊTE 4 : Génération du Barème pour le groupe
# ==========================================
group_text_parts = []
for item in group:
if isinstance(item, QuestionItem):
group_text_parts.append(f"Question [{item.label}]:\n{item.question_content}\nCorrection [{item.label}]:\n{item.solution_content}")
elif isinstance(item, ContextItem):
group_text_parts.append(f"Contexte (Cible: {item.target_question_label}):\n{item.content}")
group_context_text = "\n\n---\n\n".join(group_text_parts)
contents_4 = [
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}"),
],
)
]
config_4 = types.GenerateContentConfig(
temperature=0.2,
response_mime_type="application/json",
response_json_schema=GroupRubrics.model_json_schema(),
)
print(f"Generating rubric (Persp) for group: {', '.join(labels)}...")
try:
response_r = client.models.generate_content(
model=MODEL_ID,
contents=contents_4,
config=config_4
)
rubrics_data = GroupRubrics.model_validate_json(response_r.text)
rubrics_map = {r.label: r.rubric_content for r in rubrics_data.rubrics}
except Exception as e:
print(f"Error generating rubric for group {labels[0]}: {e}")
rubrics_map = {}
# 1. Compute the common prefix for the group
prefix = labels[0]
for lbl in labels[1:]:
@@ -536,12 +641,30 @@ def process_exam(folder_path: str):
with open(sol2_dir / f"{safe_label}.tex", "w", encoding="utf-8") as f_s2:
f_s2.write(f"\\textbf{{{item.label}}} {item.solution_content}")
# --- Écriture du Barème (Persp) ---
rubric_text = rubrics_map.get(item.label, "")
with open(persp_dir / safe_label, "w", encoding="utf-8") as f_persp:
f_persp.write(f"{item.label}\n{rubric_text}")
elif isinstance(item, ContextItem):
raw_ctx = item.content.strip()
tabulated_ctx = "\t" + re.sub(r'\n\s*', '\n\t', raw_ctx)
text_content_lines.append(f"CONTEXT :")
text_content_lines.append(tabulated_ctx)
# --- Save context to Text2 (Concatenating if exists) ---
safe_first = item.target_question_label.replace("/", "_")
safe_last = item.last_question_label.replace("/", "_")
ctxt_filename = f"CTXT {safe_first} -> {safe_last}.tex"
ctxt_path = text2_dir / ctxt_filename
# If file exists, prepend some spacing before appending
prefix = "\n\n" if ctxt_path.exists() else ""
with open(ctxt_path, "a", encoding="utf-8") as f_c2:
f_c2.write(prefix + item.content)
# 4. Write the grouped Text file
with open(text_dir / safe_group_filename, "w", encoding="utf-8") as f_text:
f_text.write("\n".join(text_content_lines))