Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import pandas as pd
-import freesound
 import numpy as np
+import freesound
 import joblib
 
 # ========= CONFIG =========
@@ -9,10 +9,8 @@ API_TOKEN = "zE9NjEOgUMzH9K7mjiGBaPJiNwJLjSM53LevarRK"
 client = freesound.FreesoundClient()
 client.set_token(API_TOKEN, "token")
 
-# Charger le modèle
+# Charger le modèle et les features
 model = joblib.load("xgb_num_downloads_music_model.pkl")
-
-# Charger les features
 FEATURE_COLS = joblib.load("xgb_num_downloads_music_features.pkl")
 
 # ========= UTILS =========
@@ -23,19 +21,28 @@ def to_str(v):
         return str(v)
     return v
 
-# ========= FONCTION DEBUG + PREDICTION =========
-def debug_and_predict(url):
+# ========= MAIN FUNCTION =========
+def extract_and_predict(url):
     try:
         sound_id = int(url.rstrip("/").split("/")[-1])
 
-        # 🔹 Récupération FreeSound
-        sound = client.get_sound(sound_id).retrieve_persistent().json()
+        # 🔹 Récupération via search() avec fields
+        results = client.search(
+            query="",
+            filter=f"id:{sound_id}",
+            fields=",".join(FEATURE_COLS + ["id", "duration", "num_downloads", "avg_rating"])
+        )
+
+        if len(results.results) == 0:
+            return pd.DataFrame([{"Erreur": "Sound not found"}])
+
+        sound = results.results[0]  # dict directement
 
         # 🔹 RAW DEBUG
         raw_data = {f"RAW_{k}": to_str(v) for k, v in sound.items()}
 
         # 🔹 Préparer le DataFrame pour XGBoost
-        X_new = {col: float(sound.get(col, 0)) for col in FEATURE_COLS}  # toutes les colonnes + 0 si manquante
+        X_new = {col: float(sound.get(col, 0)) for col in FEATURE_COLS}  # colonnes manquantes = 0
         X_prepared = pd.DataFrame([X_new], columns=FEATURE_COLS).astype(float)  # ordre strict
 
         # 🔮 PRÉDICTION
@@ -65,9 +72,9 @@ with gr.Blocks(title="FreeSound Debug + Prediction") as demo:
     gr.Markdown("✔ mêmes features que l'entraînement\n✔ ordre strict\n✔ pas d'erreur XGBoost")
 
     url = gr.Textbox(label="URL FreeSound", placeholder="https://freesound.org/s/123456/")
-    btn = gr.Button("🛠️ Debug + Predict")
+    btn = gr.Button("🛠️ Extraire + Prédire")
     out = gr.Dataframe()
 
-    btn.click(debug_and_predict, url, out)
+    btn.click(extract_and_predict, url, out)
 
 demo.launch()