Upload app.py

app.py

@@ -1,41 +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()
+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"),
+        gr.Textbox(label="Type de bâtiment (ex: Hotel)"),
+        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()