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enonce_info.py

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sebastien
2026-07-09 15:54:36 +02:00
parent 121bd0714a
commit fd35675e4c
6 changed files with 619 additions and 180 deletions
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gemini_for_enonce.py
+468 -93
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@@ -1,31 +1,70 @@
import shlex
import re
import os
import subprocess
import sys
import argparse
from pathlib import Path
from pydantic import BaseModel, Field
from typing import List
from typing import List, Union
from google import genai
from google.genai import types
from concurrent.futures import ThreadPoolExecutor
from utils import compile_to_pdf
def get_lcp(s1: str, s2: str) -> str:
i = 0
while i < len(s1) and i < len(s2) and s1[i] == s2[i]:
i += 1
return s1[:i]
# Bug : l'output est limité à 8k token…
# MODEL_ID = "gemini-3-flash-preview"
MODEL_ID = "gemini-3.1-flash-lite"
api_key = os.environ.get("GEMINI_API_KEY")
class QuestionItem(BaseModel):
# --- Modèles pour la Requête 1 ---
class QuestionOnlyItem(BaseModel):
label: str = Field(description="The unique label of the question (e.g., '1.a', 'Exercice 1')")
question_content: str = Field(description="The source text of the question, strictly extracted from the enonce file, EXCLUDING the label itself.")
class ExamQuestions(BaseModel):
questions: List[QuestionOnlyItem]
# --- Modèles pour la Requête 2 ---
class SolutionOnlyItem(BaseModel):
label: str = Field(description="The exact unique label of the question provided in the input.")
solution_content: str = Field(description="The source text of the solution, strictly extracted from the correction file.")
class ExamExtraction(BaseModel):
questions: List[QuestionItem]
class ExamSolutions(BaseModel):
solutions: List[SolutionOnlyItem]
PROMPT = """I am providing:
# --- 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.")
context_content: str = Field(description="The source text of the definitions, notations, or hypotheses, extracted from the enonce.")
class ExamContext(BaseModel):
contexts: List[ExtractedContext]
# --- Modèle fusionné (pour le reste du script) ---
class QuestionItem(BaseModel):
label: str
question_content: str
solution_content: str
class ContextItem(BaseModel):
content: str # Juste une string encapsulée pour le différencier facilement
class ExamExtraction(BaseModel):
items: List[Union[QuestionItem, ContextItem]] # Liste mixte
class GroupedExamExtraction(BaseModel):
groups: List[List[Union[QuestionItem, ContextItem]]]
PROMPT_1 = """I am providing:
1. A PDF of an exam (`enonce.pdf`)
2. The source code of the exam questions (`enonce` file)
3. The source code of the exam solutions (`correction` file)
Your task:
1. Identify all distinct question labels using the PDF document.
@@ -34,9 +73,26 @@ Your task:
from the `enonce` source file. Do not include the label itself
in this extracted text (nor LaTeX like `item` nor org-mode list
labelling like `2.`).
3. For each label, extract its exact corresponding solution textual
content from the `correction` source file. Return the result as
a JSON list in the exact reading order of the document.
Return the result as a JSON list in the exact reading order of the document.
"""
PROMPT_2 = """I am providing:
1. A JSON list of question labels and their texts extracted from an exam.
2. The source code of the exam solutions (`correction` file).
Your task:
For each question label provided in the JSON, extract its exact corresponding solution textual
content from the `correction` source file. Return the result as a JSON list in the exact same order.
"""
PROMPT_3 = """I am providing:
1. A JSON list of question labels and their texts extracted from an exam.
2. The source code of the exam questions (`enonce` file).
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.
Return the result as a JSON list.
"""
def find_file(folder: Path, base_name: str) -> Path:
@@ -70,126 +126,445 @@ def process_exam(folder_path: str):
client = genai.Client(api_key=api_key)
contents = [
# ==========================================
# REQUÊTE 1 : Extraction des Énoncés
# ==========================================
contents_1 = [
types.Content(
role="user",
parts=[
types.Part.from_text(text=PROMPT),
types.Part.from_text(text=PROMPT_1),
types.Part.from_bytes(data=pdf_bytes, mime_type="application/pdf"),
types.Part.from_text(text=f"--- ENONCE SOURCE ({enonce_path.name}) ---\n{enonce_text}"),
],
)
]
config_1 = types.GenerateContentConfig(
temperature=0.1,
response_mime_type="application/json",
response_json_schema=ExamQuestions.model_json_schema(),
)
cache_q_file = folder / "gemini_questions.json"
if cache_q_file.is_file():
print("Loading cached questions from gemini_questions.json...")
response_q_text = cache_q_file.read_text(encoding="utf-8")
else:
print("Sending request 1 (Questions) to Gemini...")
response_q = client.models.generate_content(
model=MODEL_ID,
contents=contents_1,
config=config_1
)
response_q_text = response_q.text
print("Saving questions to cache...")
cache_q_file.write_text(response_q_text, encoding="utf-8")
questions_data = ExamQuestions.model_validate_json(response_q_text)
# ==========================================
# REQUÊTE 2 : Extraction des Corrections
# ==========================================
extracted_questions_json = questions_data.model_dump_json(indent=2)
contents_2 = [
types.Content(
role="user",
parts=[
types.Part.from_text(text=PROMPT_2),
types.Part.from_text(text=f"--- EXTRACTED QUESTIONS ---\n{extracted_questions_json}"),
types.Part.from_text(text=f"--- CORRECTION SOURCE ({correction_path.name}) ---\n{correction_text}"),
],
)
]
config = types.GenerateContentConfig(
config_2 = types.GenerateContentConfig(
temperature=0.1,
response_mime_type="application/json",
response_json_schema=ExamExtraction.model_json_schema(),
response_json_schema=ExamSolutions.model_json_schema(),
)
cache_file = folder / "gemini_response.json"
cache_s_file = folder / "gemini_solutions.json"
if cache_file.is_file():
print("Loading cached response from gemini_response.json...")
response_text = cache_file.read_text(encoding="utf-8")
if cache_s_file.is_file():
print("Loading cached solutions from gemini_solutions.json...")
response_s_text = cache_s_file.read_text(encoding="utf-8")
else:
print("Sending request to Gemini...")
response = client.models.generate_content(
print("Sending request 2 (Solutions) to Gemini...")
response_s = client.models.generate_content(
model=MODEL_ID,
contents=contents,
config=config
contents=contents_2,
config=config_2
)
response_text = response.text
response_s_text = response_s.text
print("Saving solutions to cache...")
cache_s_file.write_text(response_s_text, encoding="utf-8")
print("Saving response to cache...")
cache_file.write_text(response_text, encoding="utf-8")
solutions_data = ExamSolutions.model_validate_json(response_s_text)
# Validate from the text variable (cached or fresh)
extracted_data = ExamExtraction.model_validate_json(response_text)
# ==========================================
# REQUÊTE 3 : Extraction du Contexte (Notations, etc.)
# ==========================================
contents_3 = [
types.Content(
role="user",
parts=[
types.Part.from_text(text=PROMPT_3),
types.Part.from_text(text=f"--- EXTRACTED QUESTIONS ---\n{extracted_questions_json}"),
types.Part.from_text(text=f"--- ENONCE SOURCE ({enonce_path.name}) ---\n{enonce_text}"),
],
)
]
config_3 = types.GenerateContentConfig(
temperature=0.1,
response_mime_type="application/json",
response_json_schema=ExamContext.model_json_schema(),
)
cache_c_file = folder / "gemini_context.json"
if cache_c_file.is_file():
print("Loading cached context from gemini_context.json...")
response_c_text = cache_c_file.read_text(encoding="utf-8")
else:
print("Sending request 3 (Context) to Gemini...")
response_c = client.models.generate_content(
model=MODEL_ID,
contents=contents_3,
config=config_3
)
response_c_text = response_c.text
print("Saving context to cache...")
cache_c_file.write_text(response_c_text, encoding="utf-8")
context_data = ExamContext.model_validate_json(response_c_text)
# ==========================================
# FUSION des trois résultats
# ==========================================
sol_map = {s.label: s.solution_content for s in solutions_data.solutions}
# 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
else:
ctx_map[c.target_question_label] = c.context_content
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]))
# 2. Puis on ajoute la question
sol_content = sol_map.get(q.label, "")
merged_items.append(QuestionItem(
label=q.label,
question_content=q.question_content,
solution_content=sol_content
))
extracted_data = ExamExtraction(items=merged_items)
# ==========================================
# INITIAL GROUPING COMPUTATION
# ==========================================
items_file = folder / "exam_items.txt"
full_items_file = folder / "exam_items_full.txt"
trunc_map = {}
# --- INITIAL GROUPING COMPUTATION ---
questions_only = [item for item in extracted_data.items if isinstance(item, QuestionItem)]
q_group_indices = []
if questions_only:
current_g = [0]
for i in range(1, len(questions_only)):
p = get_lcp(questions_only[current_g[0]].label, questions_only[i].label)
proposed = current_g + [i]
valid = True
for k in range(len(proposed) - 1):
if get_lcp(questions_only[proposed[k]].label, questions_only[proposed[k+1]].label) != p:
valid = False
break
if valid:
current_g.append(i)
else:
q_group_indices.append(current_g)
current_g = [i]
q_group_indices.append(current_g)
group_starter_labels = {questions_only[g[0]].label for g in q_group_indices[1:]} if q_group_indices else set()
initial_groups = []
current_group = []
for i, item in enumerate(extracted_data.items):
is_new_group = False
if isinstance(item, QuestionItem):
if item.label in group_starter_labels:
if not (len(current_group) > 0 and isinstance(current_group[-1], ContextItem)):
is_new_group = True
elif isinstance(item, ContextItem):
if i + 1 < len(extracted_data.items):
next_item = extracted_data.items[i+1]
if isinstance(next_item, QuestionItem) and next_item.label in group_starter_labels:
is_new_group = True
if is_new_group and current_group:
initial_groups.append(current_group)
current_group = []
current_group.append(item)
if current_group:
initial_groups.append(current_group)
# ---- Transform labels, and check uniqueness
seen_labels = set()
for group in initial_groups:
for item in group:
if isinstance(item, QuestionItem):
# 1. Transform label
item.label = item.label.replace("Exercice", "Ex")
item.label = item.label.replace(".", ")")
# 2. Ensure uniqueness (prefix with XX)
while item.label in seen_labels:
item.label = f"XX{item.label}"
seen_labels.add(item.label)
# --- WRITE TEXT FILES ---
print(f"Writing items files to {items_file.name} and {full_items_file.name}...")
with open(items_file, "w", encoding="utf-8") as f, \
open(full_items_file, "w", encoding="utf-8") as f_full:
header = "# Edit labels. Modify groups (---). Ensure label uniqueness (XX). Duplicate CONTEXT.\n\n"
f.write(header)
f_full.write(header)
for g_idx, group in enumerate(initial_groups):
if g_idx > 0:
f.write("\n---\n\n")
f_full.write("\n---\n\n")
for item in group:
if isinstance(item, QuestionItem):
safe_content = item.question_content.replace('\n', ' \\n ')
f_full.write(f"{item.label} ### {safe_content}\n")
if len(safe_content) > 65:
trunc_content = safe_content[:64] + "…"
trunc_map[trunc_content] = safe_content
else:
trunc_content = safe_content
f.write(f"{item.label} ### {trunc_content}\n")
elif isinstance(item, ContextItem):
safe_content = item.content.replace('\n', ' \\n ')
f.write(f"CONTEXT ### {safe_content}\n")
f_full.write(f"CONTEXT ### {safe_content}\n")
# --- OPEN EDITOR AND PARSE ---
while True:
print("Opening items file for editing...")
editor = os.environ.get("EDITOR")
try:
if editor:
subprocess.run(shlex.split(editor) + [str(items_file)])
else:
if sys.platform.startswith("linux"):
subprocess.run(["xdg-open", str(items_file)])
elif sys.platform == "darwin":
subprocess.run(["open", str(items_file)])
else:
os.startfile(str(items_file))
input("Press ENTER here once you have saved and closed the text file...")
except Exception as e:
print(f"Error running editor: {e}")
print(f"Parsing edited items from {items_file.name}...")
with open(items_file, "r", encoding="utf-8") as f:
edited_lines = [line.strip() for line in f if line.strip() and not line.startswith("#")]
# 1. Validation for XX labels
has_xx = False
for line in edited_lines:
if " ### " in line:
lbl = line.split(" ### ", 1)[0].strip()
if lbl.startswith("XX"):
has_xx = True
break
if has_xx:
print("\n!!! ERROR: Some labels still start with 'XX'. Please remove the 'XX' prefixes to ensure unique, valid labels.")
input("Press ENTER to return to the editor...")
continue
# 2. Actual Parsing
grouped_items = []
current_group = []
labels_list = []
orig_idx = 0
for line in edited_lines:
if line == "---":
if current_group:
grouped_items.append(current_group)
current_group = []
continue
if " ### " not in line:
continue
new_label, edited_content_raw = line.split(" ### ", 1)
new_label = new_label.strip()
if new_label != "CONTEXT":
labels_list.append(new_label)
if "…" in edited_content_raw and edited_content_raw in trunc_map:
edited_content_raw = trunc_map[edited_content_raw]
edited_content = edited_content_raw.replace(' \\n ', '\n')
if orig_idx < len(extracted_data.items):
orig_item = extracted_data.items[orig_idx]
if isinstance(orig_item, QuestionItem):
current_group.append(QuestionItem(
label=new_label,
question_content=edited_content,
solution_content=orig_item.solution_content
))
elif isinstance(orig_item, ContextItem):
current_group.append(ContextItem(content=edited_content))
orig_idx += 1
if current_group:
grouped_items.append(current_group)
# If we reached here without 'continue', the data is valid
break
# Save labels and proceed
with open(folder / "labels", 'w', encoding='utf-8') as f_labels:
for label in labels_list:
f_labels.write(f"{label}\n")
grouped_extraction = GroupedExamExtraction(groups=grouped_items)
# 2. Setup output directories
text_dir = folder / "Text"
sol_dir = folder / "Sol"
text_dir.mkdir(exist_ok=True)
sol_dir.mkdir(exist_ok=True)
text2_dir = folder / "Text2"
sol2_dir = folder / "Sol2"
dirs = [text_dir, sol_dir, text2_dir, sol2_dir]
labels_file = folder / "labels"
import shutil
# Ask only if any directory already exists
if any(d.exists() for d in dirs):
answer = input(
"Output directories already exist. Delete their contents? [y/N] "
).strip().lower()
# Step 1: Write initial labels
print("Writing initial labels file...")
with open(labels_file, "w", encoding="utf-8") as flabels:
for q in extracted_data.questions:
flabels.write(f"{q.label}\n")
if answer not in ("y", "yes"):
print("Aborted.")
sys.exit(1)
# Empty each directory
for d in dirs:
if d.exists():
shutil.rmtree(d)
d.mkdir(parents=True)
else:
# Create them if they don't exist
for d in dirs:
d.mkdir(parents=True)
# Step 2: Open labels file for user editing
print("Opening labels file for editing...")
editor = os.environ.get("EDITOR")
try:
if editor:
subprocess.run(shlex.split(editor) + [str(labels_file)])
else:
# Fallbacks if $EDITOR is not set
if sys.platform.startswith("linux"):
subprocess.Popen(["xdg-open", str(labels_file)])
elif sys.platform == "darwin":
subprocess.Popen(["open", str(labels_file)])
else:
os.startfile(str(labels_file))
text_dir.mkdir(exist_ok=True)
sol_dir.mkdir(exist_ok=True)
# xdg-open/open usually do not block, so we wait for user confirmation
input("Press ENTER here once you have saved and closed the labels file...")
except Exception:
print("Error running editor, using labels as given.")
# Step 3 & 4: Read the edited file back and create a mapping
with open(labels_file, "r", encoding="utf-8") as flabels:
edited_lines = [line.strip() for line in flabels if line.strip()]
print("Writing grouped question and solution files...")
mapping = []
final_labels = []
orig_idx = 0
for group in grouped_extraction.groups:
q_items = [item for item in group if isinstance(item, QuestionItem)]
labels = [q.label for q in q_items]
for line in edited_lines:
if line.startswith("+"):
new_label = line[1:].lstrip()
final_labels.append(new_label)
# New label, no source content
mapping.append((new_label, None))
else:
new_label = line
final_labels.append(new_label)
# Map to initial order, advancing index only for non-'+' items
q_item = extracted_data.questions[orig_idx] if orig_idx < len(extracted_data.questions) else None
mapping.append((new_label, q_item))
orig_idx += 1
if not labels:
continue # Skip if a group has no questions (only contexts)
# Rewrite the labels file cleanly (removing '+' prefixes)
with open(labels_file, "w", encoding="utf-8") as flabels:
for lbl in final_labels:
flabels.write(f"{lbl}\n")
# 1. Compute the common prefix for the group
prefix = labels[0]
for lbl in labels[1:]:
prefix = get_lcp(prefix, lbl)
# Step 5: Write the final question and solution files
print("Writing question and solution files...")
for new_label, q_item in mapping:
safe_label = new_label.replace("/", "_")
# 2. Format the Text filename: prefix [label1, label2]
labels_str = ",".join([label[len(prefix):] for label in labels])
group_filename = f"{prefix}[{labels_str}]"
safe_group_filename = group_filename.replace("/", "_")
if q_item:
q_content = q_item.question_content.replace("\\n", "\n")
s_content = q_item.solution_content.replace("\\n", "\n")
else:
q_content = ""
s_content = ""
text_content_lines = []
# Write Text/label
with open(text_dir / safe_label, "w", encoding="utf-8") as f:
f.write(f"{new_label}\n{q_content}")
# 3. Process each item in the group
for item in group:
if isinstance(item, QuestionItem):
# 1. Prepare tabulated content:
# Start with a tab, then replace every newline+whitespace with newline+tab
raw_content = item.question_content.strip()
tabulated = "\t" + re.sub(r'\n\s*', '\n\t', raw_content)
# Write Sol/label
with open(sol_dir / safe_label, "w", encoding="utf-8") as f:
f.write(f"{new_label}\n{s_content}")
# 2. Build Text entry
text_content_lines.append(f"{item.label} :")
text_content_lines.append(tabulated)
# Write individual Sol file (remains unchanged)
safe_label = item.label.replace("/", "_")
with open(sol_dir / safe_label, "w", encoding="utf-8") as f_sol:
f_sol.write(f"{item.label}\n{item.solution_content}")
with open(text2_dir / f"{safe_label}.tex", "w", encoding="utf-8") as f_t2:
f_t2.write(f"\\textbf{{{item.label}}} {item.question_content}")
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}")
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)
# 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))
print(f"Success! Processed {len(grouped_extraction.groups)} groups.")
# ==========================================
# PDF COMPILATION (4 Threads)
# ==========================================
all_tex_files = list(text2_dir.glob("*.tex")) + list(sol2_dir.glob("*.tex"))
def compile_worker(tex_file: Path):
"""Helper to read content and call the utility function."""
try:
content = tex_file.read_text(encoding="utf-8")
pdf_path = tex_file.with_suffix(".pdf")
compile_to_pdf(content, pdf_path)
except Exception as e:
print(f"Error compiling {tex_file.name}: {e}")
print(f"Compiling {len(all_tex_files)} files to PDF using 4 threads...")
with ThreadPoolExecutor(max_workers=4) as executor:
executor.map(compile_worker, all_tex_files)
print(f"Success! Processed {len(mapping)} labels.")
if __name__ == "__main__":
if not api_key: