Add model and api system

app.py

@@ -0,0 +1,41 @@
+from huggingface_hub import hf_hub_download
+import joblib
+import pandas as pd
+import gradio as gr
+
+# Charger le modele depuis Hugging Face 
+model_path = hf_hub_download(repo_id="Xantoss/energy_model", filename="model.joblib")
+model = joblib.load(model_path)
+
+# Fonction de prediction
+def predict(PropertyGFATotal, PrimaryPropertyType, BuildingAge, NumberofFloors, NumberofBuildings, PctElec, PctSteam):
+    df = pd.DataFrame([{
+        "PropertyGFATotal": PropertyGFATotal,
+        "PrimaryPropertyType": PrimaryPropertyType,
+        "BuildingAge": BuildingAge,
+        "NumberofFloors": NumberofFloors,
+        "NumberofBuildings": NumberofBuildings,
+        "PctElec": PctElec,
+        "PctSteam": PctSteam
+    }])
+    prediction = model.predict(df)[0]
+    return f"{prediction:,.0f} kBtu"
+
+# Interface Gradio
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="Surface totale (ft²)"),
+        gr.Textbox(label="Type de bâtiment (ex: Large Office)"),
+        gr.Number(label="Âge du bâtiment"),
+        gr.Number(label="Nombre d'étages"),
+        gr.Number(label="Nombre de bâtiments"),
+        gr.Slider(0, 1, step=0.01, label="Part Électrique"),
+        gr.Slider(0, 1, step=0.01, label="Part Vapeur")
+    ],
+    outputs="text",
+    title="Prédiction énergétique de bâtiments",
+    description="Modèle de Gradient Boosting optimisé. Entrez les infos du bâtiment pour estimer la consommation (kBtu)."
+)
+
+demo.launch()

model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:101f63be17c742ce4a4d70b5ddcb3fd8389bd04214fe82f87fabbd9a9a76a905
+size 196516

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+scikit-learn
+pandas
+huggingface_hub