diff --git a/.github/workflows/check_code_quality.yaml b/.github/workflows/check_code_quality.yaml index c6cef25..da53b9c 100644 --- a/.github/workflows/check_code_quality.yaml +++ b/.github/workflows/check_code_quality.yaml @@ -1,17 +1,22 @@ -name: check_code_quality -on: pull_request -jobs: - black: # mettre le nom qu'on souhaite ici - runs-on: ubuntu-20.04 +name: code quality + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + ruff: + runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 - - uses: actions/setup-python@v4 - with: - python-version: 3.9 - - run: | #bash multi-lignes - python -m pip install --upgrade pip - pip install black - - run: | - black --check --verbose . + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + - name: Check formatting + run: uvx ruff format --check . + - name: Check linting + run: uvx ruff check . \ No newline at end of file diff --git a/.gitignore b/.gitignore index 20e3a62..d06d94f 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ -venv/ +.venv/ data/ __pycache__ -test.py \ No newline at end of file +test.py +*.duckdb \ No newline at end of file diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/Exercises.yaml b/Exercises.yaml new file mode 100644 index 0000000..f3ead13 --- /dev/null +++ b/Exercises.yaml @@ -0,0 +1,93 @@ +# Source de vérité unique pour tous les exercices SQLingo. +# Pour ajouter un exercice : ajouter un bloc ici + le fichier .sql correspondant. +# +# Champs : +# theme : catégorie de l'exercice (utilisé pour grouper) +# name : identifiant unique de l'exercice +# tables : tables DuckDB nécessaires (pour affichage uniquement) +# answer : nom du fichier SQL solution dans Exercices/answers// + +- theme: cross_joins + name: cross_joins_1 + tables: [beverages, food_items] + answer: cross_joins_1.sql + +- theme: cross_joins + name: cross_joins_2 + tables: [sizes, trademarks] + answer: cross_joins_2.sql + +- theme: cross_joins + name: cross_joins_3 + tables: [hours, quarters] + answer: cross_joins_3.sql + +- theme: inner_joins + name: inner_joins_1 + tables: [salaries, seniorities] + answer: inner_joins_1.sql + +- theme: left_joins + name: left_joins_1 + tables: [products, order_details] + answer: left_joins_1.sql + +- theme: left_joins + name: left_joins_2 + tables: [orders, customers, products, order_details] + answer: left_joins_2.sql + +- theme: full_outer_joins + name: full_outer_joins_1 + tables: [df_store_products, df_products] + answer: full_outer_joins_1.sql + +- theme: full_outer_joins + name: full_outer_joins_2 + tables: [df_customers, df_stores, df_store_products, df_products] + answer: full_outer_joins_2.sql + +- theme: self_joins + name: self_joins_1 + tables: [employees] + answer: self_joins_1.sql + +- theme: self_joins + name: self_joins_2 + tables: [sales] + answer: self_joins_2.sql + +- theme: group_by + name: group_by_1 + tables: [ventes_immo] + answer: group_by_1.sql + +- theme: group_by + name: group_by_2 + tables: [ventes] + answer: group_by_2.sql + +- theme: case_when + name: case_when_1 + tables: [salaires] + answer: case_when_1.sql + +- theme: case_when + name: case_when_2 + tables: [discount] + answer: case_when_2.sql + +- theme: case_when + name: case_when_3 + tables: [salaires] + answer: case_when_3.sql + +- theme: grouping_set + name: grouping_set_1 + tables: [redbull] + answer: grouping_set_1.sql + +- theme: grouping_set + name: grouping_set_2 + tables: [datapop] + answer: grouping_set_2.sql \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..7551d0b --- /dev/null +++ b/Makefile @@ -0,0 +1,16 @@ +.PHONY: install init run start reset + +install: + uv sync + +init: + uv run db.py + +run: + uv run streamlit run app.py + +start: install init run + +reset: + rm -f data/exercises_sql_tables.duckdb + uv run db.py \ No newline at end of file diff --git a/README.md b/README.md index aeb17c2..4c715c0 100644 --- a/README.md +++ b/README.md @@ -1,24 +1,65 @@ +# 😎 SQLingo -# SRS - Spaced Repetition System +Entraîne-toi au SQL ! -Le **Spaced Repetition System (SRS)** est une technique d'apprentissage qui consiste à augmenter les intervalles de révision entre chaque session de révision pour un même contenu. Cette méthode est basée sur l'effet de la courbe de l'oubli, qui montre comment notre capacité à retenir des informations diminue avec le temps sans révision. +## Comment ça marche -**Utilité :** -- **Efficacité de l'apprentissage :** En révisant les informations juste avant que l'oubli ne commence, le SRS augmente la rétention à long terme des connaissances. -- **Optimisation du temps :** Les utilisateurs passent moins de temps sur des sujets qu'ils maîtrisent déjà et se concentrent davantage sur des domaines moins familiers. +Au lancement, tu sélectionnes les thèmes que tu veux travailler. SQLingo mélange les exercices et te les présente un par un. Si tu réponds correctement, l'exercice disparaît de la queue. Sinon, il revient quelques exercices plus tard. En fin de session, tu vois ton score et la liste des exercices à retravailler. -## Streamlit +## Thèmes disponibles -**Définition :** **Streamlit** est un framework open-source de Python conçu pour créer des applications webs. +- Cross joins, Inner joins, Left joins, Full outer joins, Self joins +- Group by +- Case when +- Grouping sets -**Utilité :** -- **Déploiement rapide :** Permet aux data scientists de transformer des scripts Python en applications web interactives sans nécessiter une expertise approfondie en développement web. -- **Interactivité :** Offre des widgets intégrés pour interagir avec les utilisateurs. +## Stack -## DuckDB +- **[Streamlit](https://streamlit.io/)** — interface web interactive en Python +- **[DuckDB](https://duckdb.org/)** — base de données embarquée pour exécuter les requêtes SQL +- **[uv](https://github.com/astral-sh/uv)** — gestion des dépendances -**DuckDB** est un moteur de base de données embarqué orienté colonnes, conçu pour l'analyse de données OLAP (Online Analytical Processing) sur des systèmes de gestion de base de données relationnelle. +## Choix techniques -**Utilité :** -- **Facilité d'intégration :** Peut être utilisé comme une bibliothèque au sein d'autres applications sans nécessiter de configuration de serveur séparée. -- **Performance optimisée :** Utilise l'exécution de requêtes vectorisées pour améliorer les performances de lecture et d'analyse des données. +**Pas de compte utilisateur.** La progression est gérée entièrement via `st.session_state`. Une queue d'exercices mélangée au démarrage, repositionnée selon les résultats. + +**DuckDB comme moteur SQL.** Pas de serveur à configurer, DuckDB tourne directement dans le process Python et exécute des requêtes SQL. + +**Une seule source de vérité pour les exercices.** Tous les exercices sont définis dans `exercises.yaml`. Ajouter un exercice ne nécessite pas de toucher au code Python. + +**Comparaison order-insensitive.** La vérification des requêtes trie les deux DataFrames avant comparaison — une requête correcte sans `ORDER BY` explicite n'est pas pénalisée. + +## Lancer le projet + +```bash +make start # install + init DB + lance l'app +``` + +Les commandes disponibles : + +```bash +make install # installe les dépendances (uv sync) +make init # initialise la base de données +make run # lance l'application +make reset # repart d'une DB vierge +``` + +## Structure + +``` +SQLingo/ +├── app.py # Point d'entrée, routing entre les écrans +├── db.py # Initialisation DB et connexion partagée +├── utils.py # Logique SRS, vérification des requêtes, affichage +├── config.py # Chemins et constantes globales +├── exercises.yaml # Source de vérité pour tous les exercices +└── Exercices/ + ├── answers/ # Fichiers SQL solution par thème + └── questions/ # Énoncés Markdown par thème +``` + +## Ajouter un exercice + +1. Ajouter un bloc dans `exercises.yaml` +2. Créer le fichier `Exercices/questions//.md` +3. Créer le fichier `Exercices/answers//.sql` diff --git a/app.py b/app.py index 017295e..c275e57 100644 --- a/app.py +++ b/app.py @@ -1,201 +1,207 @@ -# pylint: disable=missing-module-docstring -import os -import sys -import subprocess -import logging -from datetime import date, timedelta - -import duckdb +""" +app.py - Point d'entrée de SQLingo. +""" + import streamlit as st -from functions import ( - get_questions, - get_selected_exercise, - get_selector_exercises, - get_selector_themes, +from db import get_connection, init_db +from utils import ( check_users_solution, - query_memory_df, - signup_user, - user_auth, + get_current_exercise, + init_session, + load_exercises, + load_solution, + record_result, + show_question, + show_tables, ) - # ------------------------------------------------------------ -# CONFIG PAGE +# CONFIG # ------------------------------------------------------------ + st.set_page_config( - page_title="SQL_SRS", + page_title="SQLingo", page_icon="😎", layout="wide", ) -st.markdown( - """ - - """, - unsafe_allow_html=True, -) +# ------------------------------------------------------------ +# INITIALISATION DB +# ------------------------------------------------------------ + +init_db() +con = get_connection() # ------------------------------------------------------------ -# SETUP +# ÉCRAN D'ACCUEIL # ------------------------------------------------------------ -# Création du dossier data -if "data" not in os.listdir(): - print("creating folder data") - logging.error(os.listdir()) - logging.error("creating folder data") - os.mkdir("data") - -# On lance init_db.py pour créer la BDD -if "exercises_sql_tables.duckdb" not in os.listdir("data"): - try: - subprocess.run([sys.executable, "init_db.py"], check=True) - signup_user("guest", "guest") - except subprocess.CalledProcessError as e: - print(f"Failed to execute init_db.py: {e}") + + +def render_home() -> None: + st.title("😎 SQLingo") + st.markdown("Entraîne-toi au SQL avec un système de répétition espacée.") + st.divider() + + exercises = load_exercises() + all_themes = sorted({e["theme"] for e in exercises}) + + st.subheader("Thèmes") + selected_themes = [] + cols = st.columns(3) + for i, theme in enumerate(all_themes): + with cols[i % 3]: + if st.checkbox( + theme.replace("_", " ").title(), value=True, key=f"theme_{theme}" + ): + selected_themes.append(theme) + + st.divider() + + n_selected = sum(1 for e in exercises if e["theme"] in selected_themes) + st.caption(f"{n_selected} exercice(s) sélectionné(s)") + + if st.button("🚀 Démarrer", disabled=not selected_themes, type="primary"): + init_session(themes=selected_themes) + st.rerun() + # ------------------------------------------------------------ -# AUTHENT +# ÉCRAN EXERCICE # ------------------------------------------------------------ -user_auth() -if "logged_in" in st.session_state and st.session_state["logged_in"]: - # -------------------- - # Affichage de l'app - # -------------------- - current_user = st.session_state["username"] - con = duckdb.connect(database="data/exercises_sql_tables.duckdb", read_only=False) - memory_df = query_memory_df(con, current_user) +def render_exercise() -> None: + exercise = get_current_exercise() + + if exercise is None: + st.session_state["session_started"] = False + st.session_state["session_finished"] = True + st.rerun() + return + + score = st.session_state["score"] + queue = st.session_state["queue"] + total = score["correct"] + score["incorrect"] + len(queue) + done = score["correct"] + score["incorrect"] + + # --- Sidebar --- with st.sidebar: - # st.write(f"Bienvenue, {st.session_state['username']}!") + st.markdown("## 😎 SQLingo") + st.divider() + st.metric("✅ Correct", score["correct"]) + st.metric("❌ À revoir", score["incorrect"]) + st.progress(done / total if total else 0) + st.caption(f"{done} / {total} exercices") + st.divider() + if st.button("🏠 Accueil", use_container_width=True): + st.session_state["session_started"] = False + st.rerun() - # Forcer le padding de la sidebar pour éviter l'espace blanc en haut + st.divider() st.markdown( - """ - - """, + "Made with 🍜 by " + "Fabien" + "
" + "Voir sur GitHub", unsafe_allow_html=True, ) - # Titre dans la sidebar - st.markdown( - """ -
-

😎 SQLingo 😎

-
- """, - unsafe_allow_html=True, - ) + # --- Contenu principal --- + theme_label = exercise["theme"].replace("_", " ").title() + st.markdown(f"**Thème :** {theme_label} · **Exercice :** `{exercise['name']}`") + st.divider() - # Sélection du thème - theme = get_selector_themes(memory_df) + col_question, col_tables = st.columns([3, 2]) - # Sélection de l'exercice en fonction du thème sélectionné - exercises_lst = get_selector_exercises(memory_df, theme) + with col_question: + st.subheader("Question") + show_question(exercise) - # Récupérer l'exercice - exercise, answer_str, answer, solution_df = get_selected_exercise( - con, theme, exercises_lst, current_user + st.subheader("Ta requête") + user_query = st.text_area( + label="Saisir ta requête SQL :", + height=200, + key=f"query_{exercise['name']}", + label_visibility="collapsed", ) - # TODO : Ajuster l'affichage du df en injectant du css pour fixer sa taille - # user + deconnect - st.write(f"Vous êtes connecté en tant que {current_user}") - if st.button("Déconnexion"): - st.session_state["logged_in"] = False - st.session_state["username"] = None - st.rerun() - st.markdown( - """ - Made with 🍜 by \ - Fabien\ -

Mon site \ -
GitHub - """, - unsafe_allow_html=True, - ) + if st.button("✅ Valider", type="primary", disabled=not user_query): + try: + solution_df = load_solution(con, exercise) + except FileNotFoundError: + st.error(f"Fichier solution introuvable pour `{exercise['name']}`.") + return + + correct = check_users_solution(con, solution_df, user_query) + record_result(exercise, correct) + + if correct: + st.success("Bonne réponse ! Exercice suivant →") + if st.button("Continuer →"): + st.rerun() + else: + st.error("Pas tout à fait - l'exercice reviendra un peu plus tard.") + if st.button("Continuer →"): + st.rerun() + + with col_tables: + st.subheader("Tables disponibles") + show_tables(con, exercise) + + # --- Onglet solution --- + with st.expander("Voir la solution"): + try: + solution_df = load_solution(con, exercise) + from config import ANSWERS_PATH + + sql = (ANSWERS_PATH / exercise["theme"] / exercise["answer"]).read_text() + st.code(sql, language="sql") + st.dataframe(solution_df) + except FileNotFoundError: + st.warning("Fichier solution introuvable.") + + +# ------------------------------------------------------------ +# ÉCRAN DE FIN +# ------------------------------------------------------------ + + +def render_end() -> None: + score = st.session_state["score"] + history = st.session_state["history"] + total = score["correct"] + score["incorrect"] + + st.title("🎉 Session terminée !") + st.divider() + + col1, col2, col3 = st.columns(3) + col1.metric("Total", total) + col2.metric("✅ Corrects", score["correct"]) + col3.metric("❌ À revoir", score["incorrect"]) + + errors = [(ex, r) for ex, r in history if r == "incorrect"] + if errors: + st.divider() + st.subheader("Exercices à retravailler") + for ex, _ in errors: + st.markdown(f"- **{ex['theme']}** · `{ex['name']}`") + + st.divider() + if st.button("🔄 Recommencer", type="primary"): + for key in ["session_started", "session_finished", "queue", "score", "history"]: + st.session_state.pop(key, None) + st.rerun() + + +# ------------------------------------------------------------ +# ROUTEUR +# ------------------------------------------------------------ - # Affichage des questions dynamiques - st.subheader("Question :") - get_questions(theme, answer_str) - - # Saisir la requête - query = st.text_area(label="Saisir votre requête SQL :", key="user_input") - - # Check de la requête - if query: - IS_SOLUTION_CORRECT = check_users_solution(con, solution_df, query) - - # si la solution est ok alors on met à jour la date "last_reviewed" - if IS_SOLUTION_CORRECT: - today = date.today() - - # Boutons pour mettre à jour la date de prochaine apparition de la question - col1, col2, col3 = st.columns(spec=3, gap="small") - with col1: - if st.button(label="Revoir dès demain"): - next_review_date = today + timedelta(days=1) - # print(f"{current_user} veut revoir la requête le {next_review_date}") - - with col2: - if st.button(label="Revoir dans 7 jours"): - next_review_date = today + timedelta(days=7) - # print(f"{current_user} veut revoir la requête le {next_review_date}") - - with col3: - if st.button(label="Revoir dans 14 jours"): - next_review_date = today + timedelta(days=14) - # print(f"{current_user} veut revoir la requête le {next_review_date}") - - # Si un bouton a été cliqué, mettre à jour la date - if "next_review_date" in locals(): - exercise_name = exercise.loc[0, "exercise_name"] - UPDATE_QUERY = """ - UPDATE memory_state - SET last_reviewed = ? - WHERE exercise_name = ? - AND user_id = ? - """ - with duckdb.connect("data/exercises_sql_tables.duckdb") as conn: - conn.execute( - UPDATE_QUERY, - ( - next_review_date.strftime("%Y-%m-%d"), - exercise_name, - current_user, - ), - ) - conn.close() - # st.rerun() - - tab1, tab2 = st.tabs(["Tables", "Solution"]) - - with tab1: - exercise_tables = exercise.loc[0, "tables"] - exercise_tables_len = len(exercise_tables) - cols = st.columns(exercise_tables_len) - for i in range(0, exercise_tables_len): - cols[i].write(exercise_tables[i]) - df_table = con.execute(f"SELECT * FROM {exercise_tables[i]}").df() - cols[i].table(df_table) - - with tab2: - st.code(answer, language="sql") - st.dataframe(solution_df) +if st.session_state.get("session_finished"): + render_end() +elif st.session_state.get("session_started"): + render_exercise() else: - st.write("Veuillez vous connecter ou vous inscrire pour continuer.") - st.write( - "Si vous ne souhaitez pas vous inscrire \ - vous pouvez utiliser l'identifiant et \ - le mot de passe : guest" - ) + render_home() diff --git a/config.py b/config.py new file mode 100644 index 0000000..6e1dad1 --- /dev/null +++ b/config.py @@ -0,0 +1,13 @@ +"""Constantes globales pour le projet SQLingo.""" + +from pathlib import Path + +# Paramètres chemin d'accès +DB_PATH = Path("exercices_tables.duckdb") +EXERCISES_PATH = Path("Exercises.yaml") +QUESTIONS_PATH = Path("Exercices/questions") +ANSWERS_PATH = Path("Exercices/answers") + +# Paramètres SRS +SRS_CORRECT_DELAY = None +SRS_INCORRECT_DELAY = 3 diff --git a/db.py b/db.py new file mode 100644 index 0000000..0ce0804 --- /dev/null +++ b/db.py @@ -0,0 +1,367 @@ +""" +db.py - Initialisation de la base de données et connexion partagée. + +Ce module a deux responsabilités : + 1. Créer les tables de données des exercices (à lancer une seule fois). + 2. Exposer get_connection() pour partager une connexion DuckDB dans toute l'app. +""" + +import io +import random +import pandas as pd +import duckdb +import streamlit as st +from pathlib import Path + +from config import DB_PATH + + +# ------------------------------------------------------------ +# CONNEXION PARTAGÉE +# ------------------------------------------------------------ + + +@st.cache_resource +def get_connection() -> duckdb.DuckDBPyConnection: + """Retourne une connexion DuckDB partagée pour toute la session Streamlit.""" + return duckdb.connect(DB_PATH) + + +# ------------------------------------------------------------ +# CRÉATION DES TABLES D'EXERCICES +# ------------------------------------------------------------ + + +def create_cross_joins_exercises(con: duckdb.DuckDBPyConnection) -> None: + beverages_df = pd.read_csv( # noqa: F841 + io.StringIO("beverage,price\norange juice,2.5\nExpresso,2\nTea,3\n") + ) + con.execute("CREATE TABLE IF NOT EXISTS beverages AS SELECT * FROM beverages_df") + + food_items_df = pd.read_csv( # noqa: F841 + io.StringIO("food_item,food_price\ncookie,2.5\ncholatine,2\nmuffin,3\n") + ) + con.execute("CREATE TABLE IF NOT EXISTS food_items AS SELECT * FROM food_items_df") + + sizes_df = pd.read_csv(io.StringIO("size\nXS\nM\nL\nXL\n")) # noqa: F841 + con.execute("CREATE TABLE IF NOT EXISTS sizes AS SELECT * FROM sizes_df") + + trademarks_df = pd.read_csv( # noqa: F841 + io.StringIO("trademark\nNike\nAsphalte\nAbercrombie\nLewis\n") + ) + con.execute("CREATE TABLE IF NOT EXISTS trademarks AS SELECT * FROM trademarks_df") + + hours_df = pd.read_csv(io.StringIO("hour\n08\n09\n10\n11\n12\n")) # noqa: F841 + con.execute("CREATE TABLE IF NOT EXISTS hours AS SELECT * FROM hours_df") + + quarters_df = pd.read_csv(io.StringIO("quarter\n00\n15\n30\n45\n")) # noqa: F841 + con.execute("CREATE TABLE IF NOT EXISTS quarters AS SELECT * FROM quarters_df") + + +def create_inner_joins_exercises(con: duckdb.DuckDBPyConnection) -> None: + salaries_df = pd.read_csv( # noqa: F841 + io.StringIO("salary,employee_id\n2000,1\n2500,2\n2200,3\n") + ) + con.execute("CREATE TABLE IF NOT EXISTS salaries AS SELECT * FROM salaries_df") + + seniorities_df = pd.read_csv(io.StringIO("employee_id,seniority\n1,2ans\n2,4ans\n")) # noqa: F841, E501 + con.execute( + "CREATE TABLE IF NOT EXISTS seniorities AS SELECT * FROM seniorities_df" + ) + + +def create_left_joins_exercises(con: duckdb.DuckDBPyConnection) -> None: + orders_df = pd.DataFrame( # noqa: F841 + {"order_id": [1, 2, 3, 4, 5], "customer_id": [101, 102, 103, 104, 105]} + ) + con.execute("CREATE TABLE IF NOT EXISTS orders AS SELECT * FROM orders_df") + + customers_df = pd.DataFrame( # noqa: F841 + { + "customer_id": [101, 102, 103, 104, 105, 106], + "customer_name": [ + "Toufik", + "Daniel", + "Tancrède", + "Kaouter", + "Jean-Nicolas", + "David", + ], + } + ) + con.execute("CREATE TABLE IF NOT EXISTS customers AS SELECT * FROM customers_df") + + products_df = pd.DataFrame( # noqa: F841 + { + "product_id": [101, 103, 104, 105], + "product_name": ["Laptop", "Ipad", "Livre", "Petitos"], + "product_price": [800, 400, 30, 2], + } + ) + con.execute("CREATE TABLE IF NOT EXISTS products AS SELECT * FROM products_df") + + order_details_df = pd.DataFrame( # noqa: F841 + { + "order_id": [1, 2, 3, 4, 5], + "product_id": [102, 104, 101, 103, 105], + "quantity": [2, 1, 3, 2, 1], + } + ) + con.execute( + "CREATE TABLE IF NOT EXISTS order_details AS SELECT * FROM order_details_df" + ) + + +def create_full_outer_joins_exercises(con: duckdb.DuckDBPyConnection) -> None: + customers_df = pd.DataFrame( # noqa: F841 + { + "customer_id": [11, 12, 13, 14, 15], + "customer_name": ["Zeinaba", "Tancrède", "Israel", "Kaouter", "Alan"], + } + ) + con.execute("CREATE TABLE IF NOT EXISTS df_customers AS SELECT * FROM customers_df") + + stores_df = pd.DataFrame( # noqa: F841 + {"store_id": [1, 2, 3, 4], "customer_id": [11, 12, 13, 15]} + ) + con.execute("CREATE TABLE IF NOT EXISTS df_stores AS SELECT * FROM stores_df") + + store_products_df = pd.DataFrame( # noqa: F841 + { + "store_id": [1, 1, 1, 2, 2, 3, 4], + "product_id": [101, 103, 105, 101, 103, 104, 105], + } + ) + con.execute( + "CREATE TABLE IF NOT EXISTS df_store_products AS SELECT * FROM store_products_df" + ) + + products_df = pd.DataFrame( # noqa: F841 + { + "product_id": [100, 101, 103, 104], + "product_name": ["Cherry coke", "Laptop", "Ipad", "Livre"], + "product_price": [3, 800, 400, 30], + } + ) + con.execute("CREATE TABLE IF NOT EXISTS df_products AS SELECT * FROM products_df") + + +def create_self_joins_exercises(con: duckdb.DuckDBPyConnection) -> None: + employees_df = pd.DataFrame( # noqa: F841 + { + "employee_id": [11, 12, 13, 14, 15], + "employee_name": ["Sophie", "Sylvie", "Daniel", "Kaouter", "David"], + "manager_id": [13, None, 12, 13, 11], + } + ) + con.execute("CREATE TABLE IF NOT EXISTS employees AS SELECT * FROM employees_df") + + sales_df = pd.DataFrame( + { + "order_id": list(range(1110, 1198)), + "customer_id": random.choices([11, 12, 13, 14, 15, 11, 12, 13, 14], k=88), + } + ) + sales_df["date"] = [d // 3 + 1 for d in range(1, 89)] + con.execute("CREATE TABLE IF NOT EXISTS sales AS SELECT * FROM sales_df") + + +def create_group_by_exercises(con: duckdb.DuckDBPyConnection) -> None: + ventes_immo_df = pd.DataFrame( # noqa: F841 + [ + [0, "vieux_lille", 460000], + [1, "vieux_lille", 430000], + [2, "vieux_lille", 450000], + [3, float("nan"), 470000], + [4, "vieux_lille", 440000], + [10, "gambetta", 336000], + [11, float("nan"), 333000], + [12, "gambetta", 335000], + [13, "gambetta", 337000], + [14, "gambetta", 334000], + [20, "centre", 356000], + [21, "centre", 353000], + [22, float("nan"), 355000], + [23, "centre", 357000], + [24, "centre", 354000], + [30, "wazemmes", 260000], + [31, float("nan"), 230000], + [32, "wazemmes", 250000], + [33, "wazemmes", 270000], + [34, "wazemmes", 240000], + ], + columns=["flat_id", "neighborhood", "price"], + ) + con.execute( + "CREATE TABLE IF NOT EXISTS ventes_immo AS SELECT * FROM ventes_immo_df" + ) + + clients = [ + "Oussama", + "Julie", + "Chris", + "Tom", + "Jean-Nicolas", + "Aline", + "Ben", + "Toufik", + "Sylvie", + "David", + ] + ventes_df = pd.DataFrame( + [110, 49, 65, 23, 24, 3.99, 29, 48.77, 44, 10, 60, 12, 62, 19, 75] * 2, + columns=["montant"], + ) + ventes_df["client"] = clients * 3 + con.execute("CREATE TABLE IF NOT EXISTS ventes AS SELECT * FROM ventes_df") + + +def create_case_when_exercises(con: duckdb.DuckDBPyConnection) -> None: + salaires_df = pd.DataFrame( # noqa: F841 + { + "name": [ + "Toufik", + "Jean-Nicolas", + "Daniel", + "Kaouter", + "Sylvie", + "Sebastien", + "Diane", + "Romain", + "François", + "Anna", + "Zeinaba", + "Gregory", + "Karima", + "Arthur", + "Benjamin", + ], + "wage": [ + 60000, + 75000, + 55000, + 80000, + 70000, + 90000, + 65000, + 72000, + 68000, + 85000, + 100000, + 120000, + 95000, + 83000, + 110000, + ], + "department": [ + "IT", + "HR", + "SALES", + "IT", + "IT", + "HR", + "SALES", + "IT", + "HR", + "SALES", + "IT", + "IT", + "HR", + "SALES", + "CEO", + ], + } + ) + con.execute("CREATE TABLE IF NOT EXISTS salaires AS SELECT * FROM salaires_df") + + discount_df = pd.DataFrame( # noqa: F841 + { + "order_id": [1, 2, 3, 4, 5, 6], + "product_id": [101, 102, 101, 103, 102, 103], + "quantity": [5, 3, 2, 4, 6, 2], + "price_per_unit": [10.0, 25.0, 10.0, 8.0, 25.0, 8.0], + "discount_code": [None, "DISCOUNT10", "DISCOUNT20", None, None, "UNKNOWN"], + } + ) + con.execute("CREATE TABLE IF NOT EXISTS discount AS SELECT * FROM discount_df") + + +def create_grouping_set_exercises(con: duckdb.DuckDBPyConnection) -> None: + redbull_df = pd.DataFrame( # noqa: F841 + { + "store_id": ["Armentieres"] * 4 + ["Lille"] * 4 + ["Douai"] * 4, + "product_name": [ + "redbull", + "chips", + "wine", + "redbull", + "redbull", + "chips", + "wine", + "icecream", + "redbull", + "chips", + "wine", + "icecream", + ], + "amount": [45, 60, 60, 45, 100, 140, 190, 170, 55, 70, 20, 45], + } + ) + con.execute("CREATE TABLE IF NOT EXISTS redbull AS SELECT * FROM redbull_df") + + datapop_df = pd.DataFrame( # noqa: F841 + { + "year": [2016, 2017, 2018, 2019, 2020] * 3, + "region": ["IDF"] * 5 + ["HDF"] * 5 + ["PACA"] * 5, + "population": [ + 1010000, + 1020000, + 1030000, + 1040000, + 1000000, + 910000, + 920000, + 930000, + 940000, + 900000, + 810000, + 820000, + 830000, + 840000, + 950000, + ], + } + ) + con.execute("CREATE TABLE IF NOT EXISTS datapop AS SELECT * FROM datapop_df") + + +# ------------------------------------------------------------ +# POINT D'ENTRÉE +# ------------------------------------------------------------ + + +def init_db() -> None: + """Crée toutes les tables si elles n'existent pas encore.""" + + Path(DB_PATH).parent.mkdir(parents=True, exist_ok=True) + + con = duckdb.connect(DB_PATH) + create_cross_joins_exercises(con) + create_inner_joins_exercises(con) + create_left_joins_exercises(con) + create_full_outer_joins_exercises(con) + create_self_joins_exercises(con) + create_group_by_exercises(con) + create_case_when_exercises(con) + create_grouping_set_exercises(con) + con.close() + + +if __name__ == "__main__": + init_db() + print("Base de données initialisée.") + con = duckdb.connect(DB_PATH) + + with con: + tables = con.execute("SHOW TABLES").fetchall() + print("Tables créées :", tables) diff --git a/functions.py b/functions.py deleted file mode 100644 index 71d57ee..0000000 --- a/functions.py +++ /dev/null @@ -1,416 +0,0 @@ -""" -Ce module contient toutes les fonctions nécessaires pour faire fonctionner l'application -""" - -import pandas as pd -import streamlit as st -import duckdb -import bcrypt -from pathlib import Path - - -# ------------------------------------------------------------ -# FUNCTIONS -# ------------------------------------------------------------ - - -def create_exercises_table(con, user_id): - """ - Initialise la table des exercices pour user - """ - data = { - "user_id": [user_id] * 17, - "theme": [ - "cross_joins", - "cross_joins", - "cross_joins", - "inner_joins", - "left_joins", - "left_joins", - "full_outer_joins", - "full_outer_joins", - "self_joins", - "self_joins", - "group_by", - "group_by", - "case_when", - "case_when", - "case_when", - "grouping_set", - "grouping_set", - # "grouping_set", - ], - "exercise_name": [ - "cross_joins_1", - "cross_joins_2", - "cross_joins_3", - "inner_joins_1", - "left_joins_1", - "left_joins_2", - "full_outer_joins_1", - "full_outer_joins_2", - "self_joins_1", - "self_joins_2", - "group_by_1", - "group_by_2", - "case_when_1", - "case_when_2", - "case_when_3", - "grouping_set_1", - "grouping_set_2", - # "grouping_set_3", - ], - "tables": [ - ["beverages", "food_items"], - ["sizes", "trademarks"], - ["hours", "quarters"], - ["salaries", "seniorities"], - ["products", "order_details"], - ["orders", "customers", "products", "order_details"], - ["df_store_products", "df_products"], - ["df_customers", "df_stores", "df_store_products", "df_products"], - ["employees"], - ["sales"], - ["ventes_immo"], - ["ventes"], - ["salaires"], - ["discount"], - ["salaires"], - ["redbull"], - ["datapop"], - # ["redbull"], - ], - "last_reviewed": ["1970-01-01"] * 17, - "answer": [ - "cross_joins_1.sql", - "cross_joins_2.sql", - "cross_joins_3.sql", - "inner_joins_1.sql", - "left_joins_1.sql", - "left_joins_2.sql", - "full_outer_joins_1.sql", - "full_outer_joins_2.sql", - "self_joins_1.sql", - "self_joins_2.sql", - "group_by_1.sql", - "group_by_2.sql", - "case_when_1.sql", - "case_when_2.sql", - "case_when_3.sql", - "grouping_set_1.sql", - "grouping_set_2.sql", - # "grouping_set_3.sql", - ], - } - memory_state_df = pd.DataFrame(data) - con.execute("INSERT INTO memory_state SELECT * FROM memory_state_df") - - -def hash_password(password: str) -> bytes: - """Hache un mot de passe en utilisant bcrypt. - - Cette fonction prend un mot de passe en entrée, l'encode en bytes, - génère un sel aléatoire, et retourne le mdp haché. - - Args: - password (str): le mot de passe à hacher - - Returns: - bytes : le mdp haché sous forme de bytes - """ - - return bcrypt.hashpw(password.encode(), bcrypt.gensalt()) - - -def check_password(hashed_password: bytes, user_password: str) -> bool: - """Vérifie que le mot de passe est valide. - - Cette fonction prend un mot de passe haché (généré par hash_password) et un mot de passe - en clair, puis vérifie que le hash du mot de passe en clair correspond au mot de passe haché. - - Args: - hashed_password (bytes): Le mot de passe haché (résultat de hash_password) - user_password (str): Le mot de passe en clair à vérifier - - Returns: - bool: True si le mot de passe est correct, False sinon - """ - return bcrypt.checkpw(user_password.encode(), hashed_password) - - -def signup_user(username: str, password: str) -> bool: - """Inscrit un nouvel utilisateur dans la base de données. - - Cette fonction prend un nom d'utilisateur et un mot de passe, vérifie si l'utilisateur - existe déjà, et si non, hache le mot de passe et insère ces informations dans la table 'users'. - - Args: - username (str): Le nom d'utilisateur du nouvel utilisateur. - password (str): Le mot de passe en clair du nouvel utilisateur. - - Returns: - bool: True si l'inscription a réussi, False si l'utilisateur existe déjà. - - Raises: - duckdb.Error: Si une erreur inattendue se produit lors de l'interaction avec la base de données. - """ - try: - with duckdb.connect("data/exercises_sql_tables.duckdb") as con: - # Vérifier si l'utilisateur existe déjà - result = con.execute( - "SELECT COUNT(*) FROM users WHERE username = ?", [username] - ).fetchone() - if result[0] > 0: - print(f"L'utilisateur '{username}' existe déjà.") - return False - - # Si l'utilisateur n'existe pas, procéder à l'inscription - hashed_password = hash_password(password) - con.execute( - """ - INSERT INTO users (username, password) VALUES (?, ?) - """, - (username, hashed_password), - ) - - create_exercises_table(con, username) - print(f"L'utilisateur '{username}' a été inscrit avec succès.") - return True - except duckdb.Error as e: - print(f"Une erreur s'est produite lors de l'inscription : {e}") - raise - - -def login_user(username: str, password: str) -> bool: - """Vérifie les informations de connexion de l'utilisateur. - - Cette fonction prend un nom d'utilisateur et un mot de passe, vérifie leur validité - dans la base de données, et retourne True si les informations sont correctes. - - Args: - username (str): Le nom d'utilisateur à vérifier. - password (str): Le mot de passe à vérifier. - - Returns: - bool: True si les informations de connexion sont valides, False sinon. - - Raises: - duckdb.Error: Si une erreur se produit lors de l'interaction avec la base de données. - """ - try: - with duckdb.connect(database="data/exercises_sql_tables.duckdb") as con: - result = con.execute( - """ - SELECT password FROM users WHERE username = ? - """, - (username,), - ).fetchone() - - if result: - hashed_password = result[0].encode() - return check_password(hashed_password, password) - return False - except duckdb.Error as e: - print(f"Erreur lors de la vérification des informations de connexion : {e}") - raise - - -def user_auth() -> None: - """Affiche le formulaire d'inscription ou de connexion et gère l'authentification de l'utilisateur.""" - - # Vérifier si l'utilisateur est déjà connecté - if st.session_state.get("logged_in", False): - # st.write(f"Vous êtes connecté en tant que {st.session_state['username']}") - # if st.button("Déconnexion"): - # st.session_state["logged_in"] = False - # st.session_state["username"] = None - # st.rerun() - return - - # Initialiser l'état d'inscription s'il n'existe pas - if "signup" not in st.session_state: - st.session_state["signup"] = False - - if st.session_state["signup"]: - st.title("Inscription") - signup_username = st.text_input("Nom d'utilisateur", key="signup_username") - signup_password = st.text_input( - "Mot de passe", type="password", key="signup_password" - ) - signup_password_confirm = st.text_input( - "Confirmer le mot de passe", type="password", key="signup_password_confirm" - ) - - if st.button("S'inscrire"): - if not signup_username or not signup_password: - st.error("Veuillez remplir tous les champs.") - elif signup_password != signup_password_confirm: - st.error("Les mots de passe ne correspondent pas.") - elif signup_user(signup_username, signup_password): - st.success( - "Inscription réussie. Vous pouvez maintenant vous connecter." - ) - st.session_state["signup"] = False - else: - st.error( - "L'utilisateur existe déjà. Essayez un autre nom d'utilisateur." - ) - - if st.button("Retour à la connexion"): - st.session_state["signup"] = False - else: - st.title("Connexion") - login_username = st.text_input("Nom d'utilisateur", key="login_username") - login_password = st.text_input( - "Mot de passe", type="password", key="login_password" - ) - - if st.button("Se connecter"): - if not login_username or not login_password: - st.error("Veuillez remplir tous les champs.") - elif login_user(login_username, login_password): - st.success("Connexion réussie") - st.session_state["logged_in"] = True - st.session_state["username"] = login_username - st.rerun() - else: - st.error("Nom d'utilisateur ou mot de passe incorrect") - - if st.button("S'inscrire"): - st.session_state["signup"] = True - - -def query_memory_df(con, user_id: str) -> pd.DataFrame: - with duckdb.connect(database="data/exercises_sql_tables.duckdb") as con: - memory_df = ( - con.execute("SELECT * FROM memory_state WHERE user_id = ?", [user_id]) - .df() - .sort_values("last_reviewed") - .reset_index(drop=True) - ) - - return memory_df - - -def check_users_solution(con, solution_df: pd.DataFrame, user_query: str) -> bool: - """ - Checks that user SQL query is correct by : - 1: checking the columns - 2: checking the values - :param con: connection duckdb - :param solution_df: THE TRUTH - :param user_query: string containing the query typed by the user - - returns : bool indicating wether the user's query matches the solution - """ - try: - # Exécute la requête utilisateur et récupère les résultats dans un DataFrame - result = con.execute(user_query).df() - st.dataframe(result) # Affiche le résultat pour l'utilisateur - - # Vérifie si les colonnes correspondent et dans le bon ordre - if not result.columns.equals(solution_df.columns): - st.write( - "Les colonnes de votre requête ne correspondent pas exactement à celles de la solution." - ) - return False - - # Vérifie si le nombre de lignes correspond - if result.shape[0] != solution_df.shape[0]: - st.write( - f"Votre requête retourne un nombre de lignes différent de la solution attendue ({result.shape[0]} vs {solution_df.shape[0]})." - ) - return False - - # Compare les valeurs des deux DataFrames - if not result.equals(solution_df): - differences = result.compare(solution_df) - st.write( - f"Il y a des différences dans les valeurs de votre requête : {differences}" - ) - return False - - st.balloons() # Affiche des ballons si la requête est correcte 8) - return True - - except Exception as e: - # Capture et affiche toute exception survenue lors de l'exécution de la requête ou de la comparaison - st.write(f"Erreur lors de l'exécution ou de la comparaison des requêtes : {e}") - return False - - -def get_selector_themes(memory_df: pd.DataFrame) -> str: - """ - Function to get themes selected by the user - - Args: - memory_df (pd.DataFrame): the memory state df from the database - - Returns: - str: the theme selected - """ - theme = st.selectbox( - "Sélectionner un thème", - options=memory_df["theme"].unique(), - index=0, - key="theme_selectbox", - ) - - return theme - - -def get_selector_exercises(memory_df: pd.DataFrame, theme: str) -> str: - filtered_exercises = memory_df[memory_df["theme"] == theme][ - "exercise_name" - ].to_list() - exercises_lst = st.selectbox( - "Sélectionner un exercice", - options=filtered_exercises, - index=0, - key="exercises_selectbox", - ) - - return exercises_lst - - -def get_selected_exercise(con, theme: str, exercises_lst: str, current_user: str): - exercise = con.execute( - f"SELECT * FROM memory_state where theme = '{theme}' and exercise_name = '{exercises_lst}' and user_id = '{current_user}'" - ).df() - # st.write('Vous avez sélectionné', exercise) - if not exercise.empty: - st.dataframe(exercise.iloc[:, 1:-2]) # On affiche pas la colonne réponse - elif exercise.empty: - st.write("Il faut sélectionner un thème") - else: - st.write("Pas d'exercice disponibles pour le thème sélectionné") - - # Récupérer la solution de l'exercice - answer_str = exercise.loc[0, "answer"] - try: - answer_file = Path(f"Exercices/answers/{theme}/{answer_str}") - with open(answer_file, "r") as f: - answer = f.read() - solution_df = con.execute(answer).df() - - except FileNotFoundError: - st.write("Fichier de réponse non trouvé") - return exercise, answer_str, answer, solution_df - - -def get_questions(theme: str, answer_str: str) -> None: - """Récupère les questions depuis un folder - - Args: - theme (str): thème sélectionné par l'utilisateur - answer_str (str): nom exercice sélectionné par l'utilisateur - """ - # print(f"{answer_str[:-4]}.txt") - try: - question_file = Path(f"Exercices/questions/{theme}/{answer_str[:-4]}.md") - with question_file.open("r") as f: - - question: str = f.read() - st.markdown(question) - except FileNotFoundError: - st.write("Fichier question absent") diff --git a/init_db.py b/init_db.py deleted file mode 100644 index 0b85016..0000000 --- a/init_db.py +++ /dev/null @@ -1,352 +0,0 @@ -""" -Ce module contient les fonctions permettant d'initialiser la base de données de l'application. -""" - -import io -import random -import numpy as np -import pandas as pd -import duckdb - - -def create_memory_state_table(con): - """ - Créer la table vide des exercices - """ - con.execute( - """ - CREATE TABLE IF NOT EXISTS memory_state ( - user_id TEXT, - theme TEXT, - exercise_name TEXT, - tables TEXT[], - last_reviewed DATE, - answer TEXT, - PRIMARY KEY (user_id, exercise_name) - ) - """ - ) - - -def create_cross_joins_exercises(con): - beverages_csv = "beverage,price\norange juice,2.5\nExpresso,2\nTea,3\n" - beverages_df = pd.read_csv(io.StringIO(beverages_csv)) - con.execute("CREATE TABLE IF NOT EXISTS beverages AS SELECT * FROM beverages_df") - - food_items_csv = "food_item,food_price\ncookie juice,2.5\nchocolatine,2\nmuffin,3\n" - food_items_df = pd.read_csv(io.StringIO(food_items_csv)) - con.execute("CREATE TABLE IF NOT EXISTS food_items AS SELECT * FROM food_items_df") - - sizes_csv = "size\nXS\nM\nL\nXL\n" - sizes_df = pd.read_csv(io.StringIO(sizes_csv)) - con.execute("CREATE TABLE IF NOT EXISTS sizes AS SELECT * FROM sizes_df") - - trademarks_csv = "trademark\nNike\nAsphalte\nAbercrombie\nLewis\n" - trademarks_df = pd.read_csv(io.StringIO(trademarks_csv)) - con.execute("CREATE TABLE IF NOT EXISTS trademarks AS SELECT * FROM trademarks_df") - - hours_csv = "hour\n08\n09\n10\n11\n12\n" - hours_df = pd.read_csv(io.StringIO(hours_csv)) - con.execute("CREATE TABLE IF NOT EXISTS hours AS SELECT * FROM hours_df") - - quarters_csv = "quarter\n00\n15\n30\n45\n" - quarters_df = pd.read_csv(io.StringIO(quarters_csv)) - con.execute("CREATE TABLE IF NOT EXISTS quarters AS SELECT * FROM quarters_df") - - -def create_inner_joins_exercises(con): - salaries_csv = "salary,employee_id\n2000,1\n2500,2\n2200,3\n" - salaries_df = pd.read_csv(io.StringIO(salaries_csv)) - con.execute("CREATE TABLE IF NOT EXISTS salaries AS SELECT * FROM salaries_df") - - seniorities_csv = "employee_id,seniority\n1,2ans\n2,4ans\n" - seniorities_df = pd.read_csv(io.StringIO(seniorities_csv)) - con.execute( - "CREATE TABLE IF NOT EXISTS seniorities AS SELECT * FROM seniorities_df" - ) - - -def create_left_joins_exercises(con): - orders_data = { - "order_id": [1, 2, 3, 4, 5], - "customer_id": [101, 102, 103, 104, 105], - } - df_orders = pd.DataFrame(orders_data) - con.execute("CREATE TABLE IF NOT EXISTS orders AS SELECT * FROM df_orders") - - customers_data = { - "customer_id": [101, 102, 103, 104, 105, 106], - "customer_name": [ - "Toufik", - "Daniel", - "Tancrède", - "Kaouter", - "Jean-Nicolas", - "David", - ], - } - df_customers = pd.DataFrame(customers_data) - con.execute("CREATE TABLE IF NOT EXISTS customers AS SELECT * FROM df_customers") - - products_data = { - "product_id": [101, 103, 104, 105], - "product_name": ["Laptop", "Ipad", "Livre", "Petitos"], - "product_price": [800, 400, 30, 2], - } - df_products = pd.DataFrame(products_data) - con.execute("CREATE TABLE IF NOT EXISTS products AS SELECT * FROM df_products") - - order_details_data = { - "order_id": [1, 2, 3, 4, 5], - "product_id": [102, 104, 101, 103, 105], - "quantity": [2, 1, 3, 2, 1], - } - df_order_details = pd.DataFrame(order_details_data) - con.execute( - "CREATE TABLE IF NOT EXISTS order_details AS SELECT * FROM df_order_details" - ) - - -def create_full_outer_joins_exercises(con): - customers_data = { - "customer_id": [11, 12, 13, 14, 15], - "customer_name": ["Zeinaba", "Tancrède", "Israel", "Kaouter", "Alan"], - } - customers_df = pd.DataFrame(customers_data) - con.execute("CREATE TABLE IF NOT EXISTS df_customers AS SELECT * FROM customers_df") - - stores_data = {"store_id": [1, 2, 3, 4], "customer_id": [11, 12, 13, 15]} - stores_df = pd.DataFrame(stores_data) - con.execute("CREATE TABLE IF NOT EXISTS df_stores AS SELECT * FROM stores_df") - - store_products_data = { - "store_id": [1, 1, 1, 2, 2, 3, 4], - "product_id": [101, 103, 105, 101, 103, 104, 105], - } - store_products_df = pd.DataFrame(store_products_data) - con.execute( - "CREATE TABLE IF NOT EXISTS df_store_products AS SELECT * FROM store_products_df" - ) - - products_data = { - "product_id": [100, 101, 103, 104], - "product_name": ["Cherry coke", "Laptop", "Ipad", "Livre"], - "product_price": [3, 800, 400, 30], - } - products_df = pd.DataFrame(products_data) - con.execute("CREATE TABLE IF NOT EXISTS df_products AS SELECT * FROM products_df") - - -def create_self_joins_exercises(con): - employees_data = { - "employee_id": [11, 12, 13, 14, 15], - "employee_name": ["Sophie", "Sylvie", "Daniel", "Kaouter", "David"], - "manager_id": [13, None, 12, 13, 11], - } - employees_df = pd.DataFrame(employees_data) - con.execute("CREATE TABLE IF NOT EXISTS employees AS SELECT * FROM employees_df") - - sales_data = { - "order_id": list(range(1110, 1198)), - "customer_id": random.choices([11, 12, 13, 14, 15, 11, 12, 13, 14], k=88), - } - sales_df = pd.DataFrame(sales_data) - sales_df["date"] = [d // 3 + 1 for d in range(1, 89)] - con.execute("CREATE TABLE IF NOT EXISTS sales AS SELECT * FROM sales_df") - - -def create_group_by_exercises(con): - ventes_immo_data = [ - [0, "vieux_lille", 460000], - [1, "vieux_lille", 430000], - [2, "vieux_lille", 450000], - [3, np.NaN, 470000], - [4, "vieux_lille", 440000], - [10, "gambetta", 336000], - [11, np.NaN, 333000], - [12, "gambetta", 335000], - [13, "gambetta", 337000], - [14, "gambetta", 334000], - [20, "centre", 356000], - [21, "centre", 353000], - [22, np.NaN, 355000], - [23, "centre", 357000], - [24, "centre", 354000], - [30, "wazemmes", 260000], - [31, np.NaN, 230000], - [32, "wazemmes", 250000], - [33, "wazemmes", 270000], - [34, "wazemmes", 240000], - ] - ventes_immo_df = pd.DataFrame( - ventes_immo_data, columns=["flat_id", "neighborhood", "price"] - ) - con.execute( - "CREATE TABLE IF NOT EXISTS ventes_immo AS SELECT * FROM ventes_immo_df" - ) - - clients = [ - "Oussama", - "Julie", - "Chris", - "Tom", - "Jean-Nicolas", - "Aline", - "Ben", - "Toufik", - "Sylvie", - "David", - ] - ventes_data = [110, 49, 65, 23, 24, 3.99, 29, 48.77, 44, 10, 60, 12, 62, 19, 75] * 2 - ventes_df = pd.DataFrame(ventes_data, columns=["montant"]) - ventes_df["client"] = clients * 3 - con.execute("CREATE TABLE IF NOT EXISTS ventes AS SELECT * FROM ventes_df") - - -def create_case_when_exercises(con): - salaires_data = { - "name": [ - "Toufik", - "Jean-Nicolas", - "Daniel", - "Kaouter", - "Sylvie", - "Sebastien", - "Diane", - "Romain", - "François", - "Anna", - "Zeinaba", - "Gregory", - "Karima", - "Arthur", - "Benjamin", - ], - "wage": [ - 60000, - 75000, - 55000, - 80000, - 70000, - 90000, - 65000, - 72000, - 68000, - 85000, - 100000, - 120000, - 95000, - 83000, - 110000, - ], - "department": [ - "IT", - "HR", - "SALES", - "IT", - "IT", - "HR", - "SALES", - "IT", - "HR", - "SALES", - "IT", - "IT", - "HR", - "SALES", - "CEO", - ], - } - salaires_df = pd.DataFrame(salaires_data) - con.execute("CREATE TABLE IF NOT EXISTS salaires AS SELECT * FROM salaires_df") - - discount_data = { - "order_id": [1, 2, 3, 4, 5, 6], - "product_id": [101, 102, 101, 103, 102, 103], - "quantity": [5, 3, 2, 4, 6, 2], - "price_per_unit": [10.0, 25.0, 10.0, 8.0, 25.0, 8.0], - "discount_code": [None, "DISCOUNT10", "DISCOUNT20", None, None, "UNKNOWN"], - } - discount_df = pd.DataFrame(discount_data) - con.execute("CREATE TABLE IF NOT EXISTS discount AS SELECT * FROM discount_df") - - -def create_grouping_set_exercises(con): - redbull_data = { - "store_id": ["Armentieres"] * 4 + ["Lille"] * 4 + ["Douai"] * 4, - "product_name": [ - "redbull", - "chips", - "wine", - "redbull", - "redbull", - "chips", - "wine", - "icecream", - "redbull", - "chips", - "wine", - "icecream", - ], - "amount": [45, 60, 60, 45, 100, 140, 190, 170, 55, 70, 20, 45], - } - redbull_df = pd.DataFrame(redbull_data) - con.execute("CREATE TABLE IF NOT EXISTS redbull AS SELECT * FROM redbull_df") - - datapop_data = { - "year": [2016, 2017, 2018, 2019, 2020] * 3, - "region": ["IDF"] * 5 + ["HDF"] * 5 + ["PACA"] * 5, - "population": [ - 1010000, - 1020000, - 1030000, - 1040000, - 1000000, - 910000, - 920000, - 930000, - 940000, - 900000, - 810000, - 820000, - 830000, - 840000, - 950000, - ], - } - datapop_df = pd.DataFrame(datapop_data) - con.execute("CREATE TABLE IF NOT EXISTS datapop AS SELECT * FROM datapop_df") - - -def create_users_table(con): - con.execute("CREATE SEQUENCE IF NOT EXISTS user_id_seq") - con.execute( - """ - CREATE TABLE IF NOT EXISTS users ( - id INT DEFAULT nextval('user_id_seq') PRIMARY KEY, - username TEXT UNIQUE NOT NULL, - password TEXT NOT NULL - ) - """ - ) - - -def main(): - con = duckdb.connect(database="data/exercises_sql_tables.duckdb", read_only=False) - - create_memory_state_table(con) - create_cross_joins_exercises(con) - create_inner_joins_exercises(con) - create_left_joins_exercises(con) - create_full_outer_joins_exercises(con) - create_self_joins_exercises(con) - create_group_by_exercises(con) - create_case_when_exercises(con) - create_grouping_set_exercises(con) - create_users_table(con) - - con.close() - - -if __name__ == "__main__": - main() diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..4738464 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,11 @@ +[project] +name = "sqlingo" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "duckdb>=1.5.2", + "pyyaml>=6.0.3", + "streamlit>=1.56.0", +] diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..b5752f8 --- /dev/null +++ b/utils.py @@ -0,0 +1,207 @@ +""" +utils.py — Fonctions utilitaires de SQLingo. + +Ce module contient : + - Le chargement des exercices depuis exercises.yaml + - L'initialisation de la session SRS-like + - La vérification des requêtes utilisateur + - L'affichage des questions et tables +""" + +import random + +import pandas as pd +import streamlit as st +import yaml + +from config import ( + EXERCISES_PATH, + ANSWERS_PATH, + QUESTIONS_PATH, + SRS_INCORRECT_DELAY, +) + + +# ------------------------------------------------------------ +# CHARGEMENT DES EXERCICES +# ------------------------------------------------------------ + + +def load_exercises() -> list[dict]: + """Charge la liste des exercices depuis exercises.yaml. + + Returns: + list[dict]: liste de dicts avec les clés theme, name, tables, answer. + """ + with EXERCISES_PATH.open("r") as f: + return yaml.safe_load(f) + + +# ------------------------------------------------------------ +# SESSION SRS-LIKE +# ------------------------------------------------------------ + + +def init_session(themes: list[str] | None = None) -> None: + """Initialise la session Streamlit avec une queue d'exercices mélangée. + + Args: + themes: si fourni, filtre les exercices sur ces thèmes uniquement. + si None, tous les exercices sont inclus. + """ + exercises = load_exercises() + + if themes: + exercises = [e for e in exercises if e["theme"] in themes] + + random.shuffle(exercises) + + st.session_state["queue"] = exercises + st.session_state["score"] = {"correct": 0, "incorrect": 0} + st.session_state["history"] = [] # liste de (exercise, "correct" | "incorrect") + st.session_state["session_started"] = True + + +def get_current_exercise() -> dict | None: + """Retourne l'exercice en tête de queue, ou None si la session est terminée.""" + queue = st.session_state.get("queue", []) + return queue[0] if queue else None + + +def record_result(exercise: dict, correct: bool) -> None: + """Enregistre le résultat et repositionne l'exercice dans la queue. + + - Bonne réponse → exercice retiré de la queue (ne revient pas) + - Mauvaise réponse → exercice réinséré à la position SRS_INCORRECT_DELAY + """ + queue = st.session_state["queue"] + result = "correct" if correct else "incorrect" + + st.session_state["history"].append((exercise, result)) + + if correct: + st.session_state["score"]["correct"] += 1 + queue.pop(0) + else: + st.session_state["score"]["incorrect"] += 1 + queue.pop(0) + insert_at = min(SRS_INCORRECT_DELAY, len(queue)) + queue.insert(insert_at, exercise) + + +# ------------------------------------------------------------ +# VÉRIFICATION DE LA REQUÊTE UTILISATEUR +# ------------------------------------------------------------ + + +def check_users_solution(con, solution_df: pd.DataFrame, user_query: str) -> bool: + """Vérifie que la requête SQL de l'utilisateur produit le bon résultat. + + La comparaison est insensible à l'ordre des lignes : les deux DataFrames + sont triés avant d'être comparés. + + Args: + con: connexion DuckDB active + solution_df: DataFrame de référence (la solution attendue) + user_query: requête SQL saisie par l'utilisateur + + Returns: + bool: True si la requête est correcte, False sinon. + """ + try: + result = con.execute(user_query).df() + st.dataframe(result) + + # Colonnes + if list(result.columns) != list(solution_df.columns): + st.warning( + f"Colonnes incorrectes.\n\n" + f"**Attendu :** {list(solution_df.columns)}\n\n" + f"**Obtenu :** {list(result.columns)}" + ) + return False + + # Nombre de lignes + if len(result) != len(solution_df): + st.warning( + f"Nombre de lignes incorrect : " + f"ta requête retourne **{len(result)}** lignes, " + f"la solution en attend **{len(solution_df)}**." + ) + return False + + # Valeurs (order-insensitive) + result_sorted = result.sort_values(by=list(result.columns)).reset_index( + drop=True + ) + solution_sorted = solution_df.sort_values( + by=list(solution_df.columns) + ).reset_index(drop=True) # noqa: E501 + + if not result_sorted.equals(solution_sorted): + st.warning( + "Les colonnes et le nombre de lignes sont bons, mais certaines valeurs différent." + ) # noqa: E501 + return False + + st.balloons() + return True + + except Exception as e: + st.error(f"Erreur lors de l'exécution de ta requête : {e}") + return False + + +# ------------------------------------------------------------ +# AFFICHAGE +# ------------------------------------------------------------ + + +def show_question(exercise: dict) -> None: + """Affiche l'énoncé markdown de l'exercice courant. + + Args: + exercise: dict d'un exercice chargé depuis exercises.yaml + """ + theme = exercise["theme"] + answer = exercise["answer"] + question_file = QUESTIONS_PATH / theme / f"{answer[:-4]}.md" + + try: + st.markdown(question_file.read_text()) + except FileNotFoundError: + st.warning(f"Énoncé introuvable : `{question_file}`") + + +def show_tables(con, exercise: dict) -> None: + """Affiche un aperçu des tables nécessaires à l'exercice. + + Args: + con: connexion DuckDB active + exercise: dict d'un exercice chargé depuis exercises.yaml + """ + for table in exercise["tables"]: + st.markdown(f"**`{table}`**") + try: + df = con.execute(f"SELECT * FROM {table} LIMIT 5").df() + st.dataframe(df) + except Exception as e: + st.error(f"Impossible de charger la table `{table}` : {e}") + + +def load_solution(con, exercise: 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