148 lines
5.1 KiB
Python
148 lines
5.1 KiB
Python
import os
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from typing import List
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from openai import OpenAI
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from sqlalchemy.orm import Session
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from . import models
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from .curriculum import QUESTIONS, SKILLS
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client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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SYSTEM_PROMPT = """
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Tu es ProfAmi, un professeur virtuel français pour enfants de 8 à 12 ans.
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Règles :
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- Tu parles toujours en français simple et chaleureux.
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- Tu donnes des explications très courtes, puis un mini exemple.
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- Tu tiens compte du niveau de l'élève et de ses faiblesses indiquées dans le contexte.
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- Tu n'inventes pas la progression : elle est fournie dans le contexte.
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- Tu encourages sans infantiliser.
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- Quand l'élève se trompe, tu expliques calmement puis proposes une question très simple.
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- Tu enseignes principalement le programme national français niveau primaire/cycle 3.
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""".strip()
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def seed_skills(db: Session) -> None:
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for skill in SKILLS:
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existing = db.query(models.Skill).filter(models.Skill.code == skill["code"]).first()
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if not existing:
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db.add(models.Skill(**skill))
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db.commit()
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def ensure_student_mastery(db: Session, student: models.Student) -> None:
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all_skills = db.query(models.Skill).all()
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for skill in all_skills:
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found = (
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db.query(models.StudentSkillMastery)
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.filter_by(student_id=student.id, skill_id=skill.id)
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.first()
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)
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if not found:
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db.add(models.StudentSkillMastery(student_id=student.id, skill_id=skill.id))
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db.commit()
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def get_student_context(db: Session, student_id: int) -> str:
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student = db.query(models.Student).filter_by(id=student_id).first()
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mastery = (
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db.query(models.StudentSkillMastery, models.Skill)
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.join(models.Skill, models.Skill.id == models.StudentSkillMastery.skill_id)
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.filter(models.StudentSkillMastery.student_id == student_id)
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.all()
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)
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recent_messages = (
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db.query(models.Message)
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.filter_by(student_id=student_id)
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.order_by(models.Message.created_at.desc())
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.limit(6)
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.all()
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)
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lines: List[str] = [
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f"Élève: {student.first_name}, {student.age} ans, classe {student.grade}.",
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"Progression par compétence:",
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]
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for mastery_row, skill in mastery:
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lines.append(
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f"- {skill.label}: score={mastery_row.mastery_score:.1f}, confiance={mastery_row.confidence:.2f}, preuves={mastery_row.evidence_count}"
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)
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lines.append("Historique récent:")
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for message in reversed(recent_messages):
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lines.append(f"- {message.role}: {message.content}")
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return "\n".join(lines)
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def build_llm_reply(db: Session, student_id: int, user_message: str) -> str:
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context = get_student_context(db, student_id)
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response = client.responses.create(
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model="gpt-4.1-mini",
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input=[
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{"role": "system", "content": SYSTEM_PROMPT},
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{
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"role": "user",
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"content": f"Contexte pédagogique:\n{context}\n\nMessage de l'élève:\n{user_message}",
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},
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],
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temperature=0.7,
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)
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return response.output_text.strip()
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def transcribe_audio(filename: str, audio_bytes: bytes, content_type: str | None = None) -> str:
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file_payload = (filename, audio_bytes, content_type or "application/octet-stream")
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transcript = client.audio.transcriptions.create(
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model="gpt-4o-mini-transcribe",
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file=file_payload,
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)
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return (getattr(transcript, "text", "") or "").strip()
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def pick_next_skill(db: Session, student_id: int) -> models.Skill:
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weakest = (
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db.query(models.StudentSkillMastery)
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.filter_by(student_id=student_id)
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.order_by(models.StudentSkillMastery.mastery_score.asc())
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.first()
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)
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return db.query(models.Skill).filter_by(id=weakest.skill_id).first()
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def evaluate_answer(db: Session, student_id: int, skill_code: str, answer: str):
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q = QUESTIONS[skill_code]
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normalized_student = answer.strip().lower()
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normalized_expected = q["expected_answer"].strip().lower()
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correct = normalized_student == normalized_expected
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skill = db.query(models.Skill).filter_by(code=skill_code).first()
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mastery = (
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db.query(models.StudentSkillMastery)
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.filter_by(student_id=student_id, skill_id=skill.id)
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.first()
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)
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if correct:
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mastery.mastery_score = min(100.0, mastery.mastery_score + 8)
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mastery.confidence = min(1.0, mastery.confidence + 0.15)
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feedback = q["feedback_ok"]
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else:
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mastery.mastery_score = max(0.0, mastery.mastery_score - 6)
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mastery.confidence = min(1.0, mastery.confidence + 0.1)
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feedback = q["feedback_ko"]
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mastery.evidence_count += 1
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db.add(
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models.AssessmentAttempt(
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student_id=student_id,
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skill_code=skill_code,
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question=q["question"],
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expected_answer=q["expected_answer"],
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student_answer=answer,
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is_correct=1 if correct else 0,
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feedback=feedback,
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)
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)
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db.commit()
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db.refresh(mastery)
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return correct, feedback, mastery.mastery_score
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