Déploiement initial du dashboard Getaround Streamlit

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.xlsx filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.ipynb filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+FROM python:3.11-slim
+
+
+# Installer les dépendances système
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Mettre à jour pip + installer setuptools et wheel
+RUN pip install --upgrade pip setuptools wheel
+
+# Créer un dossier de travail
+WORKDIR /app
+
+# Copier les fichiers nécessaires
+COPY requirements.txt ./
+COPY app.py ./
+COPY get_around_delay_analysis.xlsx ./
+COPY get_around_pricing_project.csv ./
+COPY logo.png ./
+
+# Installer les dépendances Python (avec wheels)
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Exposer le port
+EXPOSE 7860
+
+# Lancer Streamlit
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+

README.md

@@ -1,11 +1,46 @@
 ---
-title: Get Around Streamlit
-emoji: 🏢
+title: Getaround Streamlit Dashboard
+emoji: 🚗
 colorFrom: pink
 colorTo: indigo
 sdk: docker
 pinned: false
-short_description: 'dashboard '
+app_port: 8501
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🚗 Dashboard Getaround
+
+Bienvenue sur le tableau de bord interactif **Getaround** !
+
+Ce dashboard Streamlit vous permet de :
+
+### 📊 Analyses exploratoires (EDA)
+- Explorer les **retards de location** et leur gravité.
+- Visualiser l’impact des retards sur la **réservation suivante**.
+- Analyser la **répartition des check-ins** et des états de location.
+- Étudier la **distribution des prix** selon :
+  - le type de voiture 🚙
+  - le carburant ⛽
+  - la présence de Getaround Connect 🔗
+  - la puissance moteur ⚙️
+
+### 🔮 Prédiction du prix
+- Un formulaire intelligent permet d’estimer le prix journalier d’une voiture à partir de ses caractéristiques.
+- L’estimation est faite via une API déployée dans un [Space Hugging Face dédié](https://huggingface.co/spaces/licorne2lc/get_around_api).
+
+### 📎 Liens utiles
+- 🔌 API utilisée pour les prédictions : [Voir la documentation Swagger](https://licorne2lc-get-around-api.hf.space/docs)
+- 🧠 Notebook d’analyse : inclus dans la version locale
+
+---
+
+**Technologies** :
+- Streamlit
+- Plotly
+- Seaborn & Matplotlib
+- API REST (FastAPI)
+- Docker
+
+---
+
+**Auteur** : [licorne2lc](https://huggingface.co/licorne2lc) • Projet encadré par Jedha Bootcamp

app.py

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+
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import requests
+import plotly.express as px
+from streamlit_option_menu import option_menu
+from PIL import Image
+
+# ========== CONFIGURATION ==========
+st.set_page_config(page_title="Getaround Dashboard", layout="wide")
+st.markdown("<h1 style='text-align: center;'>🚗 Getaround Dashboard</h1>", unsafe_allow_html=True)
+
+# ========== LOGO ==========
+try:
+    logo = Image.open("logo.png")
+    st.sidebar.image(logo, width=200)
+except Exception:
+    st.sidebar.warning("Logo non chargé")
+
+# ========== MENU ==========
+with st.sidebar:
+    selected = option_menu(
+        "Menu principal",
+        ["🏁 EDA delay", "📈 EDA pricing", "🔮 API prediction"],
+        icons=["hourglass-split", "bar-chart-line", "cpu"],
+        menu_icon="cast",
+        default_index=0,
+    )
+    st.markdown("---")
+    st.markdown("📄 [Espace API HuggingFace](https://huggingface.co/spaces/licorne2lc/get_around_api)")
+    st.markdown("🛠️ [Docs API /predict](https://licorne2lc-get-around-api.hf.space/docs)")
+
+# ========== 1. EDA DELAY ==========
+if selected == "🏁 EDA delay":
+    st.title("📊 Analyse des retards de Getaround")
+    try:
+        dataset = pd.read_excel("get_around_delay_analysis.xlsx")
+
+        dataset["delay_status"] = dataset["delay_at_checkout_in_minutes"].apply(
+            lambda x: "En avance" if x < 0 else "À l’heure" if x == 0 else "En retard"
+        )
+
+        def catégoriser_retard(x):
+            if x <= 0:
+                return "Pas de retard"
+            elif x <= 30:
+                return "< 30 min"
+            elif x <= 60:
+                return "30-60 min"
+            elif x <= 120:
+                return "1h-2h"
+            elif x <= 240:
+                return "2h-4h"
+            else:
+                return "> 4h"
+
+        retard_order = ["Pas de retard", "< 30 min", "30-60 min", "1h-2h", "2h-4h", "> 4h"]
+        dataset["retard_cat"] = dataset["delay_at_checkout_in_minutes"].apply(catégoriser_retard)
+        dataset["retard_cat"] = pd.Categorical(dataset["retard_cat"], categories=retard_order, ordered=True)
+
+        fig_checkin = px.pie(dataset, names="checkin_type", title="Répartition des types de check-in",
+                             hole=0.4, color_discrete_sequence=px.colors.qualitative.Pastel, width=1000, height=400)
+        st.plotly_chart(fig_checkin)
+
+        fig_state = px.pie(dataset, names="state", title="Répartition des états de location",
+                           hole=0.4, color_discrete_sequence=px.colors.qualitative.Set3, width=1000, height=400)
+        st.plotly_chart(fig_state)
+
+        fig_delay_status = px.pie(dataset, names="delay_status", title="Retards : avance / à l’heure / en retard",
+                                  hole=0.4, color_discrete_sequence=px.colors.qualitative.Vivid, width=1000, height=400)
+        st.plotly_chart(fig_delay_status)
+
+        filtered = dataset[(dataset["delay_at_checkout_in_minutes"] > 0) & (dataset["delay_at_checkout_in_minutes"] <= 1440)]
+        fig_delay_hist = px.histogram(filtered, x="delay_at_checkout_in_minutes", nbins=50,
+                                      title="Distribution des retards (0 à 1440 minutes)", width=1000, height=400)
+        st.plotly_chart(fig_delay_hist)
+
+        fig_retard_cat = px.bar(dataset["retard_cat"].value_counts().reindex(retard_order),
+                                title="Gravité des retards", labels={"value": "Nombre", "index": "Catégorie"},
+                                width=1000, height=400)
+        st.plotly_chart(fig_retard_cat)
+
+        dataset_for_join_B = dataset[["rental_id", "delay_at_checkout_in_minutes"]].rename(
+            columns={"rental_id": "rental_id_join", "delay_at_checkout_in_minutes": "delay_at_checkout_in_minutes_join"}
+        )
+        dataset_join = pd.merge(dataset, dataset_for_join_B,
+                                left_on="previous_ended_rental_id", right_on="rental_id_join")
+        dataset_join["real_delay"] = dataset_join["time_delta_with_previous_rental_in_minutes"] - dataset_join["delay_at_checkout_in_minutes_join"]
+        dataset_join_clean = dataset_join.dropna(subset=["delay_at_checkout_in_minutes_join"])
+        dataset_join_clean["annulation_due_au_retard"] = dataset_join_clean.apply(
+            lambda row: "Oui" if row["state"] == "canceled" and row["real_delay"] < 0 else
+                        "Annulée (autre raison)" if row["state"] == "canceled" else "Non", axis=1
+        )
+        fig_annul = px.pie(dataset_join_clean, names="annulation_due_au_retard",
+                           title="Répartition des annulations dues au retard du locataire précédent",
+                           hole=0.4, color_discrete_sequence=px.colors.qualitative.Bold, width=1000, height=400)
+        st.plotly_chart(fig_annul)
+
+    except FileNotFoundError:
+        st.error("❌ Fichier Excel non trouvé : get_around_delay_analysis.xlsx")
+
+# ========== 2. EDA PRICING ==========
+elif selected == "📈 EDA pricing":
+    st.title("💰 Analyse des prix de location Getaround")
+    try:
+        df_price = pd.read_csv("get_around_pricing_project.csv")
+
+        st.subheader("Distribution du prix journalier")
+        fig, ax = plt.subplots(figsize=(10, 4))
+        sns.histplot(df_price["rental_price_per_day"], bins=50, kde=True, ax=ax)
+        st.pyplot(fig)
+
+        st.subheader("Prix par type de carburant")
+        fig, ax = plt.subplots(figsize=(10, 4))
+        sns.boxplot(x="fuel", y="rental_price_per_day", data=df_price, ax=ax)
+        st.pyplot(fig)
+
+        st.subheader("Prix par type de voiture")
+        fig, ax = plt.subplots(figsize=(10, 4))
+        sns.boxplot(x="car_type", y="rental_price_per_day", data=df_price, ax=ax)
+        st.pyplot(fig)
+
+        st.subheader("Prix selon la présence de Getaround Connect")
+        fig, ax = plt.subplots(figsize=(10, 4))
+        sns.boxplot(x="has_getaround_connect", y="rental_price_per_day", data=df_price, ax=ax)
+        st.pyplot(fig)
+
+        st.subheader("Corrélation entre puissance moteur et prix journalier")
+        fig, ax = plt.subplots(figsize=(10, 4))
+        sns.scatterplot(x="engine_power", y="rental_price_per_day", data=df_price, hue="car_type", ax=ax)
+        st.pyplot(fig)
+    except FileNotFoundError:
+        st.error("❌ Fichier CSV non trouvé : get_around_pricing_project.csv")
+
+# ========== 3. API PREDICTION ==========
+elif selected == "🔮 API prediction":
+    st.title("🔮 Prédiction du prix de location")
+    st.markdown("Remplis les informations suivantes pour obtenir une estimation du prix")
+
+    with st.form("prediction_form"):
+        model_key = st.selectbox("Modèle de voiture", ["Peugeot", "Audi", "BMW"])
+        mileage = st.number_input("Kilométrage", value=50000)
+        engine_power = st.number_input("Puissance moteur", value=100)
+        fuel = st.selectbox("Type de carburant", ["diesel", "petrol", "electric", "hybrid"])
+        paint_color = st.selectbox("Couleur", ["black", "white", "grey", "blue", "red"])
+        car_type = st.selectbox("Type de voiture", ["sedan", "convertible", "suv", "coupe"])
+        private_parking_available = st.checkbox("Parking privé disponible", value=True)
+        has_gps = st.checkbox("GPS", value=True)
+        has_air_conditioning = st.checkbox("Climatisation", value=True)
+        automatic_car = st.checkbox("Boîte automatique", value=True)
+        has_getaround_connect = st.checkbox("Getaround Connect", value=True)
+        has_speed_regulator = st.checkbox("Régulateur de vitesse", value=True)
+        winter_tires = st.checkbox("Pneus hiver", value=True)
+        submitted = st.form_submit_button("📤 Envoyer")
+
+    if submitted:
+        data = {
+            "model_key": model_key,
+            "mileage": mileage,
+            "engine_power": engine_power,
+            "fuel": fuel,
+            "paint_color": paint_color,
+            "car_type": car_type,
+            "private_parking_available": private_parking_available,
+            "has_gps": has_gps,
+            "has_air_conditioning": has_air_conditioning,
+            "automatic_car": automatic_car,
+            "has_getaround_connect": has_getaround_connect,
+            "has_speed_regulator": has_speed_regulator,
+            "winter_tires": winter_tires,
+        }
+        api_url = "https://licorne2lc-get-around-api.hf.space/predict"
+        try:
+            response = requests.post(api_url, json=data)
+            if response.status_code == 200:
+                price = round(response.json()["prediction"], 2)
+                st.success(f"💰 Prix de location estimé : {price} € / jour")
+            else:
+                st.error(f"❌ Erreur {response.status_code} : {response.text}")
+        except Exception as e:
+            st.error(f"❌ Erreur lors de la requête : {e}")

get_around_delay_analysis.xlsx

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get_around_pricing_project.csv

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getaround_EDA.ipynb

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requirements.txt

@@ -0,0 +1,14 @@
+bokeh==3.6.0
+numpy==1.26.0
+plotly==5.24.1
+streamlit-lottie==0.0.5
+
+streamlit==1.37.1
+pandas==2.2.1
+matplotlib==3.7.3
+seaborn==0.13.2
+requests==2.32.3
+streamlit-option-menu==0.4.0
+Pillow==10.3.0
+pyarrow==16.1.0
+openpyxl==3.1.2