Update app.py

app.py

@@ -1,71 +1,11 @@
-import gradio as gr
-import pandas as pd
-import freesound
-import joblib
-import xgboost as xgb
-
-# ----------------------------
-# Config Freesound
-# ----------------------------
-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):
-    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)
-    dmatrix = xgb.DMatrix(X.values, feature_names=feat_list)
-    
-    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])
-        
-        # 🔹 Inclure duration explicitement
-        all_features = list(set(
-            xgb_music_feat_num + xgb_music_feat_avg + xgb_effect_feat_num + xgb_effect_feat_avg
-        ))
+
+        # Inclure duration explicitement
+        all_features = list(set(xgb_music_feat_num + xgb_music_feat_avg + xgb_effect_feat_num + xgb_effect_feat_avg))
         fields = "duration," + ",".join(all_features)
-        
+
         results = client.search(
             query="",
             filter=f"id:{sound_id}",
@@ -76,46 +16,23 @@ def extract_and_predict(url):
             return pd.DataFrame([{"Erreur": "Sound not found"}])
 
         sound = results.results[0]
+
+        # ⚠️ Récupérer duration séparément
         duration = safe_float(sound.get("duration", 0))
-        
-        # 🔹 Vérifier le type selon la durée
+
+        # ✅ Décider du type
         if 0.5 <= duration <= 3:
             # Effect Sound
             num = predict_with_model(xgb_effect_num, sound, xgb_effect_feat_num)
             avg = predict_with_model(xgb_effect_avg, sound, xgb_effect_feat_avg, le_effect_avg)
-            return pd.DataFrame([{
-                "Type": "Effect Sound",
-                "Duration": duration,
-                "Num_downloads": num,
-                "Avg_rating": avg
-            }])
+            return pd.DataFrame([{"Type": "Effect Sound", "Duration": duration, "Num_downloads": num, "Avg_rating": avg}])
         elif 10 <= duration <= 60:
             # Music
             num = predict_with_model(xgb_music_num, sound, xgb_music_feat_num)
             avg = predict_with_model(xgb_music_avg, sound, xgb_music_feat_avg, le_music_avg)
-            return pd.DataFrame([{
-                "Type": "Music",
-                "Duration": duration,
-                "Num_downloads": num,
-                "Avg_rating": avg
-            }])
+            return pd.DataFrame([{"Type": "Music", "Duration": duration, "Num_downloads": num, "Avg_rating": avg}])
         else:
-            return pd.DataFrame([{
-                "Erreur": "Durée non supportée pour prédiction",
-                "Duration": duration
-            }])
+            return pd.DataFrame([{"Erreur": "Durée non supportée pour prédiction", "Duration": duration}])
 
     except Exception as 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", placeholder="https://freesound.org/s/123456/")
-    btn = gr.Button("Prédire")
-    out = gr.Dataframe()
-    btn.click(extract_and_predict, url, out)
-
-demo.launch()
+        return pd.DataFrame([{"Erreur": str(e)}])