Update app.py

app.py

@@ -1,3 +1,63 @@
+import gradio as gr
+import pandas as pd
+import freesound
+import joblib
+import xgboost as xgb
+
+API_TOKEN = "zE9NjEOgUMzH9K7mjiGBaPJiNwJLjSM53LevarRK"
+client = freesound.FreesoundClient()
+client.set_token(API_TOKEN, "token")
+
+# ----------------------------
+# 1️⃣ Charger les modèles
+# ----------------------------
+# Music
+xgb_music_num = joblib.load("xgb_num_downloads_music_model.pkl")
+xgb_music_feat_num = joblib.load("xgb_num_downloads_music_features.pkl")
+xgb_music_avg = joblib.load("xgb_avg_rating_music_model.pkl")
+xgb_music_feat_avg = joblib.load("xgb_avg_rating_music_features.pkl")
+le_music_avg = joblib.load("xgb_avg_rating_music_label_encoder.pkl")
+
+# Effect Sound
+xgb_effect_num = joblib.load("xgb_num_downloads_effectsound_model.pkl")
+xgb_effect_feat_num = joblib.load("xgb_num_downloads_effectsound_features.pkl")
+xgb_effect_avg = joblib.load("xgb_avg_rating_effectsound_model.pkl")
+xgb_effect_feat_avg = joblib.load("xgb_avg_rating_effectsound_features.pkl")
+le_effect_avg = joblib.load("xgb_avg_rating_effectsound_label_encoder.pkl")
+
+# ----------------------------
+# 2️⃣ Fonctions utilitaires
+# ----------------------------
+def safe_float(v):
+    try:
+        return float(v)
+    except:
+        return 0.0
+
+def predict_with_model(model, features, feat_list, le=None):
+    # Préparer la ligne
+    row = []
+    for col in feat_list:
+        val = features.get(col, 0)
+        if val is None or isinstance(val, (list, dict)):
+            val = 0
+        row.append(safe_float(val))
+    
+    X = pd.DataFrame([row], columns=feat_list)
+    
+    # Transformer en DMatrix
+    dmatrix = xgb.DMatrix(X.values, feature_names=feat_list)
+    
+    # Prédiction
+    pred_int = int(model.get_booster().predict(dmatrix)[0])
+    
+    if le:
+        return le.inverse_transform([pred_int])[0]
+    return pred_int
+
+# ----------------------------
+# 3️⃣ Extraction + prédiction
+# ----------------------------
 def extract_and_predict(url):
     try:
         sound_id = int(url.rstrip("/").split("/")[-1])
@@ -35,4 +95,15 @@ def extract_and_predict(url):
             return pd.DataFrame([{"Erreur": "Durée non supportée pour prédiction", "Duration": duration}])
 
     except Exception as e:
-        return pd.DataFrame([{"Erreur": str(e)}])
+        return pd.DataFrame([{"Erreur": str(e)}])
+# ----------------------------
+# 4️⃣ Interface Gradio
+# ----------------------------
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎧 FreeSound – Prédiction XGBoost (DMatrix)")
+    url = gr.Textbox(label="URL FreeSound")
+    btn = gr.Button("Prédire")
+    out = gr.Dataframe()
+    btn.click(extract_and_predict, url, out)
+
+demo.launch()