Initial Persp generation

gemini_for_enonce.py

@@ -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))

prompting.py

@@ -42,7 +42,7 @@ unless the distinction is very important.
 
 In some case, you may find that either
  - The student didn't answer the right question. Set the score to 0.
-   Since it could be a labeling error, indicate is by setting `error`
+   Since it could be a labeling error, indicate it by setting `error`
    to \"wrong-label\".
  - You can find an answer to another question of the exercice (taking
    more than a couple of lines). Score the question you are supposed

reading_grouped_annotations.py

@@ -9,14 +9,15 @@ import threading
 
 import annotating
 
-from utils import natural_key, pdf_image_of_enonce, pdf_image_of_solution
+from utils import natural_key, pdf_image_of_enonce, pdf_image_of_solution, pdf_images_of_contexts
 from reading_annotations import detect_checks_and_notes, has_significant_notes
 
-def get_extra_pdfs_as_images(root_dir, label, annotating_module):
+def get_extra_pdfs_as_images(root_dir, label, annotating_module, all_labels):
     """Fetches Text and Sol pdfs for a given label and converts them to images."""
     extra_images = []
     a, b = pdf_image_of_enonce(root_dir, label), pdf_image_of_solution(root_dir, label)
-    for c in [a, b]:
+    e = pdf_images_of_contexts(root_dir, label, all_labels)
+    for c in e + [a, b]:
         if c:
             img, _, _ = annotating_module.make_base_image(c)
             if img:
@@ -239,7 +240,7 @@ def apply_actions_and_regenerate_grouped(root_dir, data, student_id,
                     perfect_no_comment = False
 
         if not perfect_no_comment or has_notes:
-            extras = get_extra_pdfs_as_images(root_dir, label, annotating)
+            extras = get_extra_pdfs_as_images(root_dir, label, annotating, all_labels)
             extras.append(final_img)
             concat_list_F.append(extras)
 

utils.py

@@ -34,6 +34,43 @@ def pdf_image_of_solution(root_dir, label):
     if os.path.exists(pdf_path):
         return pdf_path
 
+def pdf_images_of_contexts(root_dir, label, all_labels):
+    text2_dir = os.path.join(root_dir, "Text2")
+    if not os.path.isdir(text2_dir):
+        return []
+
+    # Map safe labels (with '/' replaced by '_') to their chronological index
+    safe_to_idx = {l.replace("/", "_"): i for i, l in enumerate(all_labels)}
+
+    safe_target = label.replace("/", "_")
+    target_idx = safe_to_idx.get(safe_target, -1)
+
+    if target_idx == -1:
+        return []
+
+    pertinent_contexts = []
+
+    for filename in os.listdir(text2_dir):
+        if filename.startswith("CTXT ") and filename.endswith(".pdf"):
+            # Extract "first_label -> last_label" from "CTXT first_label -> last_label.pdf"
+            core = filename[5:-4]
+            parts = core.split(" -> ")
+
+            if len(parts) == 2:
+                first_safe, last_safe = parts
+                first_idx = safe_to_idx.get(first_safe, -1)
+                last_idx = safe_to_idx.get(last_safe, -1)
+
+                # Check if the current label falls within the context's validity range
+                if first_idx != -1 and last_idx != -1:
+                    if first_idx <= target_idx <= last_idx:
+                        pdf_path = os.path.join(text2_dir, filename)
+                        pertinent_contexts.append((first_idx, pdf_path))
+
+    # Sort by first_idx to ensure contexts are returned in logical reading order
+    pertinent_contexts.sort(key=lambda x: x[0])
+    return [path for _, path in pertinent_contexts]    
+
 
 def get_exam_file_content(folder_path, mode, label):
     """