Upload app.py

app.py

@@ -3,25 +3,38 @@ import joblib
 import pandas as pd
 import gradio as gr
 
-# Charger le modele depuis Hugging Face 
+# charger le modele depuis Hugging Face  Xantoss/energy_model
 model_path = hf_hub_download(repo_id="Xantoss/energy_model", filename="model.joblib")
 model = joblib.load(model_path)
 
-# Fonction de prediction
+#fonction de prediction avec verifications
 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"
+    try:
+        #verifications manuelles
+        assert PropertyGFATotal > 0, "La surface doit être > 0"
+        assert 0 <= BuildingAge <= 200, "Âge du bâtiment entre 0 et 200 ans"
+        assert NumberofFloors >= 1, "Le nombre d'étages doit être ≥ 1"
+        assert NumberofBuildings >= 1, "Le nombre de bâtiments doit être ≥ 1"
+        assert 0 <= PctElec <= 1, "Part électrique entre 0 et 1"
+        assert 0 <= PctSteam <= 1, "Part vapeur entre 0 et 1"
+        assert PctElec + PctSteam <= 1, "PctElec + PctSteam doit être ≤ 1"
+        
+        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
+    except AssertionError as e:
+        return f"Erreur : {e}"
+
+# interface Gradio
 demo = gr.Interface(
     fn=predict,
     inputs=[