testtest

app.py

@@ -1,24 +1,22 @@
-import os
-import tempfile
-import numpy as np
-import pandas as pd
 import gradio as gr
-
+import pandas as pd
+import numpy as np
 import joblib
-import soundfile as sf
-from pydub import AudioSegment
-import opensmile
-
-import freesound
 import xgboost as xgb
+import freesound
+
+
+# =========================
+# Token FreeSound
+# =========================
+API_TOKEN = "A ECRIRE"   # <- tu remplaces ici
 
 
 # =========================
-# RÈGLES DURÉE
+# Durées
 # =========================
 MIN_EFFECT, MAX_EFFECT = 0.5, 3.0
 MIN_MUSIC, MAX_MUSIC = 10.0, 60.0
-SR_TARGET = 16000
 
 
 # =========================
@@ -35,45 +33,21 @@ CSS = """
   border-color: #fca5a5;
   background: #fff1f2;
 }
-.card-title{
-  font-weight: 950;
-  margin-bottom: 8px;
-}
-.badges{
-  display:flex;
-  gap:10px;
-  flex-wrap:wrap;
-  margin-bottom:12px;
-}
+.card-title{ font-weight: 950; margin-bottom: 8px; }
+.badges{ display:flex; gap:10px; flex-wrap:wrap; margin-bottom:12px; }
 .badge{
-  padding:6px 10px;
-  border-radius:999px;
-  font-weight:900;
-  font-size: 13px;
+  padding:6px 10px; border-radius:999px; font-weight:900; font-size:13px;
   border: 1px solid #e5e7eb;
 }
 .badge-type{ background:#eef2ff; color:#3730a3;}
 .badge-time{ background:#ecfeff; color:#155e75;}
 
-.grid{
-  display:grid;
-  grid-template-columns: 1fr;
-  gap:10px;
-}
-.box{
-  border:1px solid #e5e7eb;
-  border-radius:14px;
-  padding:12px;
-  background:#fafafa;
-}
+.grid{ display:grid; grid-template-columns:1fr; gap:10px; }
+.box{ border:1px solid #e5e7eb; border-radius:14px; padding:12px; background:#fafafa; }
 .box-title{ font-weight:900; margin-bottom:4px; }
 .box-value{ font-size:18px; font-weight:800; }
 
-.hint{
-  margin-top:10px;
-  color:#6b7280;
-  font-size:12px;
-}
+.hint{ margin-top:10px; color:#6b7280; font-size:12px; }
 
 #header-title { font-size: 28px; font-weight: 950; margin-bottom: 6px; }
 #header-sub { color:#6b7280; margin-top:0px; line-height:1.45; }
@@ -118,68 +92,50 @@ def html_result(badge_text, duration, rating_text, downloads_text, extra_html=""
 
 
 # =========================
-# INTERPRETATION (COMMUNE)
+# Interprétation
 # =========================
 def interpret_results(avg_class: int, dl_class: int) -> str:
-    """
-    avg_class: 0=Missed info, 1=Low, 2=Medium, 3=High
-    dl_class: 0=Low, 1=Medium, 2=High
-    """
     if avg_class == 0:
         return (
             "ℹ️ <b>Interprétation</b> :<br>"
-            "Aucune évaluation possible (rating manquant).<br>"
-            
+            "Aucune/peu d'évaluations utilisateurs (rating manquant).<br>"
+            "La popularité est donc probablement liée à l'usage (téléchargements) plutôt qu'à la qualité perçue."
         )
 
     rating_txt = {1: "faible", 2: "moyenne", 3: "élevée"}.get(avg_class, "inconnue")
     downloads_txt = {0: "faible", 1: "modérée", 2: "élevée"}.get(dl_class, "inconnue")
 
     if avg_class == 3 and dl_class == 2:
-        potentiel = "très fort"
-        detail = "contenu de haute qualité et très populaire."
+        potentiel, detail = "très fort", "contenu de haute qualité et très populaire."
     elif avg_class == 3 and dl_class == 1:
-        potentiel = "fort"
-        detail = "contenu bien apprécié, en croissance."
+        potentiel, detail = "fort", "contenu bien apprécié, en croissance."
     elif avg_class == 3 and dl_class == 0:
-        potentiel = "prometteur"
-        detail = "bonne qualité mais faible visibilité (peut gagner en popularité)."
+        potentiel, detail = "prometteur", "bonne qualité mais faible visibilité."
     elif avg_class == 2 and dl_class == 2:
-        potentiel = "modéré à fort"
-        detail = "populaire mais qualité perçue moyenne."
+        potentiel, detail = "modéré à fort", "populaire mais qualité perçue moyenne."
     elif avg_class == 2 and dl_class == 1:
-        potentiel = "modéré"
-        detail = "profil standard, popularité stable."
+        potentiel, detail = "modéré", "profil standard, popularité stable."
     elif avg_class == 2 and dl_class == 0:
-        potentiel = "limité"
-        detail = "engagement faible, diffusion limitée."
+        potentiel, detail = "limité", "engagement faible, diffusion limitée."
     elif avg_class == 1 and dl_class == 2:
-        potentiel = "contradictoire"
-        detail = "très téléchargé mais peu apprécié (usage pratique possible)."
+        potentiel, detail = "contradictoire", "très téléchargé mais peu apprécié."
     elif avg_class == 1 and dl_class == 1:
-        potentiel = "faible"
-        detail = "peu attractif pour les utilisateurs."
+        potentiel, detail = "faible", "peu attractif pour les utilisateurs."
     else:
-        potentiel = "très faible"
-        detail = "faible intérêt global."
+        potentiel, detail = "très faible", "faible intérêt global."
 
     return (
-        "<b>Interprétation</b> :<br>"
-        f"Potentiel estimé : <b>{potentiel}</b> — {detail}"
+        "🧠 <b>Interprétation</b> :<br>"
+        f"- Qualité perçue : <b>{rating_txt}</b><br>"
+        f"- Popularité : <b>{downloads_txt}</b><br><br>"
+        f"👉 Potentiel estimé : <b>{potentiel}</b> — {detail}"
     )
 
 
 def avg_label_to_class(avg_label: str) -> int:
-    """
-    Convertit un label texte (LabelEncoder) en classe 0..3 :
-    0=Missed info, 1=Low, 2=Medium, 3=High
-    Robuste aux variantes.
-    """
     if avg_label is None:
         return 0
-
     s = str(avg_label).strip().lower()
-
     if "miss" in s or "missing" in s or "none" in s or "no" in s:
         return 0
     if "high" in s or "élev" in s or "eleve" in s:
@@ -191,168 +147,16 @@ def avg_label_to_class(avg_label: str) -> int:
     return 0
 
 
-# ============================================================
-# PARTIE A — Upload audio → openSMILE → modèles (toi)
-# ============================================================
-
-MODEL_EFFECT = joblib.load("xgb_model_EffectSound.pkl")
-MODEL_MUSIC = joblib.load("xgb_model_Music.pkl")
-
-RATING_DISPLAY_AUDIO = {
-    0: "❌ Informations manquantes",
-    1: "⭐ Faible",
-    2: "⭐⭐ Moyen",
-    3: "⭐⭐⭐ Élevé",
-}
-DOWNLOADS_DISPLAY_AUDIO = {
-    0: "⭐ Faible",
-    1: "⭐⭐ Moyen",
-    2: "⭐⭐⭐ Élevé",
-}
-
-SMILE = opensmile.Smile(
-    feature_set=opensmile.FeatureSet.eGeMAPSv02,
-    feature_level=opensmile.FeatureLevel.Functionals,
-)
-
-
-def get_duration_seconds(filepath):
-    ext = os.path.splitext(filepath)[1].lower()
-    if ext == ".mp3":
-        audio = AudioSegment.from_file(filepath)
-        return len(audio) / 1000.0
-    with sf.SoundFile(filepath) as f:
-        return len(f) / f.samplerate
-
-
-def to_wav_16k_mono(filepath):
-    ext = os.path.splitext(filepath)[1].lower()
-    if ext == ".wav":
-        try:
-            with sf.SoundFile(filepath) as f:
-                if f.samplerate == SR_TARGET and f.channels == 1:
-                    return filepath
-        except Exception:
-            pass
-
-    audio = AudioSegment.from_file(filepath)
-    audio = audio.set_channels(1).set_frame_rate(SR_TARGET)
-
-    tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
-    tmp.close()
-    audio.export(tmp.name, format="wav")
-    return tmp.name
-
-
-def extract_opensmile_features(filepath):
-    wav_path = to_wav_16k_mono(filepath)
-    feats = SMILE.process_file(wav_path)
-    feats = feats.select_dtypes(include=[np.number]).reset_index(drop=True)
-    return feats
-
-
-def predict_upload_with_dmatrix(model, X_df: pd.DataFrame):
-    """
-    Résout 'data did not contain feature names' en passant via Booster + DMatrix(feature_names=...).
-    Retour: array shape (1, n_outputs)
-    """
-    if hasattr(model, "estimators_"):
-        preds = []
-        for est in model.estimators_:
-            booster = est.get_booster() if hasattr(est, "get_booster") else est
-            dm = xgb.DMatrix(X_df.values, feature_names=list(X_df.columns))
-            p = booster.predict(dm)
-            preds.append(np.asarray(p).reshape(-1))
-        return np.column_stack(preds)
-
-    booster = model.get_booster() if hasattr(model, "get_booster") else model
-    dm = xgb.DMatrix(X_df.values, feature_names=list(X_df.columns))
-    p = booster.predict(dm)
-    return np.asarray(p).reshape(1, -1)
-
-
-def predict_from_uploaded_audio(audio_file):
-    if audio_file is None:
-        return html_error("Aucun fichier", "Veuillez importer un fichier audio (wav, mp3, flac…).")
-
-    # Durée
-    try:
-        duration = get_duration_seconds(audio_file)
-    except Exception as e:
-        return html_error("Audio illisible", f"Impossible de lire l'audio.<br>Détail : <code>{e}</code>")
-
-    # Vérif durées
-    if duration < MIN_EFFECT:
-        return html_error(
-            "Audio trop court",
-            f"Durée détectée : <b>{duration:.2f} s</b><br><br>"
-            f"Plages acceptées :<br>"
-            f"• Effet sonore : <b>{MIN_EFFECT}–{MAX_EFFECT} s</b><br>"
-            f"• Musique : <b>{MIN_MUSIC}–{MAX_MUSIC} s</b>"
-        )
-
-    if (MAX_EFFECT < duration < MIN_MUSIC) or duration > MAX_MUSIC:
-        return html_error(
-            "Audio hors plage",
-            f"Durée détectée : <b>{duration:.2f} s</b><br><br>"
-            f"Plages acceptées :<br>"
-            f"• Effet sonore : <b>{MIN_EFFECT}–{MAX_EFFECT} s</b><br>"
-            f"• Musique : <b>{MIN_MUSIC}–{MAX_MUSIC} s</b>"
-        )
-
-    # Type + modèle
-    if duration <= MAX_EFFECT:
-        badge = "🔊 Effet sonore (upload)"
-        model = MODEL_EFFECT
-    else:
-        badge = "🎵 Musique (upload)"
-        model = MODEL_MUSIC
-
-    # openSMILE
-    try:
-        X = extract_opensmile_features(audio_file)
-    except Exception as e:
-        return html_error("Extraction openSMILE échouée", f"Détail : <code>{e}</code>")
-
-    # Align features
-    try:
-        expected = model.estimators_[0].feature_names_in_ if hasattr(model, "estimators_") else model.feature_names_in_
-        X = X.reindex(columns=list(expected), fill_value=0)
-    except Exception as e:
-        return html_error("Alignement des features échoué", f"Détail : <code>{e}</code>")
-
-    # Predict
-    try:
-        y = predict_upload_with_dmatrix(model, X)
-    except Exception as e:
-        return html_error("Prédiction échouée", f"Détail : <code>{e}</code>")
-
-    y = np.array(y)
-    avg_class = int(y[0, 0])
-    dl_class = int(y[0, 1])
-
-    rating_text = RATING_DISPLAY_AUDIO.get(avg_class, "Inconnu")
-    downloads_text = DOWNLOADS_DISPLAY_AUDIO.get(dl_class, "Inconnu")
-
-    conclusion = interpret_results(avg_class, dl_class)
-    extra = f"""
-<div style="margin-top:12px; padding-top:10px; border-top:1px dashed #d1d5db">
-{conclusion}
-</div>
-"""
-
-    return html_result(badge, duration, rating_text, downloads_text, extra_html=extra)
-
-
-# ============================================================
-# PARTIE B — URL FreeSound → API → modèles (collègue)
-# ============================================================
-
-API_TOKEN = "zE9NjEOgUMzH9K7mjiGBaPJiNwJLjSM53LevarRK"  # <-- tu remplaces ici
+# =========================
+# Init FreeSound Client
+# =========================
+client = freesound.FreesoundClient()
+client.set_token(API_TOKEN, "token")
 
-fs_client = freesound.FreesoundClient()
-fs_client.set_token(API_TOKEN, "token")
 
+# =========================
+# Charger modèles (metadata)
+# =========================
 # Music
 xgb_music_num = joblib.load("xgb_num_downloads_music_model.pkl")
 xgb_music_feat_num = joblib.load("xgb_num_downloads_music_features.pkl")
@@ -377,41 +181,40 @@ def safe_float(v):
         return 0.0
 
 
-def predict_with_model_fs(model, features_dict, feat_list, label_encoder=None):
+def predict_with_model(model, features, feat_list, le=None):
     row = []
     for col in feat_list:
-        val = features_dict.get(col, 0)
+        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 label_encoder is not None:
-        return label_encoder.inverse_transform([pred_int])[0]
+    if le:
+        return le.inverse_transform([pred_int])[0]
     return pred_int
 
 
-def predict_from_freesound_url(url: str):
+def extract_and_predict(url: str):
     if not url or not url.strip():
         return html_error("URL vide", "Collez une URL FreeSound du type <code>https://freesound.org/s/123456/</code>")
 
-    # ID
     try:
         sound_id = int(url.rstrip("/").split("/")[-1])
     except Exception:
         return html_error("URL invalide", "Impossible d'extraire l'ID depuis l'URL.")
 
+    # Champs nécessaires
     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)
 
     try:
-        results = fs_client.search(query="", filter=f"id:{sound_id}", fields=fields)
+        results = client.search(query="", filter=f"id:{sound_id}", fields=fields)
     except Exception as e:
         return html_error("Erreur API FreeSound", f"Détail : <code>{e}</code>")
 
@@ -421,11 +224,25 @@ def predict_from_freesound_url(url: str):
     sound = results.results[0]
     duration = safe_float(sound.get("duration", 0))
 
-    # Effect Sound
+    # Vérif durées
+    if duration < MIN_EFFECT:
+        return html_error(
+            "Audio trop court",
+            f"Durée : <b>{duration:.2f}s</b><br><br>"
+            f"Plages : Effet sonore <b>{MIN_EFFECT}-{MAX_EFFECT}s</b> | Musique <b>{MIN_MUSIC}-{MAX_MUSIC}s</b>"
+        )
+    if (MAX_EFFECT < duration < MIN_MUSIC) or duration > MAX_MUSIC:
+        return html_error(
+            "Audio hors plage",
+            f"Durée : <b>{duration:.2f}s</b><br><br>"
+            f"Plages : Effet sonore <b>{MIN_EFFECT}-{MAX_EFFECT}s</b> | Musique <b>{MIN_MUSIC}-{MAX_MUSIC}s</b>"
+        )
+
+    # Effect
     if MIN_EFFECT <= duration <= MAX_EFFECT:
-        badge = "🔊 Effet sonore (FreeSound URL)"
-        dl_class = int(predict_with_model_fs(xgb_effect_num, sound, xgb_effect_feat_num))
-        avg_text = str(predict_with_model_fs(xgb_effect_avg, sound, xgb_effect_feat_avg, le_effect_avg))
+        badge = "🔊 Effet sonore (metadata FreeSound)"
+        dl_class = int(predict_with_model(xgb_effect_num, sound, xgb_effect_feat_num))
+        avg_text = str(predict_with_model(xgb_effect_avg, sound, xgb_effect_feat_avg, le_effect_avg))
         dl_text = NUM_DOWNLOADS_MAP.get(dl_class, str(dl_class))
 
         avg_class = avg_label_to_class(avg_text)
@@ -433,76 +250,47 @@ def predict_from_freesound_url(url: str):
 
         extra = f"""
 <div class="hint">ID FreeSound : <b>{sound_id}</b></div>
-<div style="margin-top:12px; padding-top:10px; border-top:1px dashed #d1d5db">
-{conclusion}
-</div>
+<div style="margin-top:12px; padding-top:10px; border-top:1px dashed #d1d5db">{conclusion}</div>
 """
         return html_result(badge, duration, avg_text, dl_text, extra_html=extra)
 
     # Music
-    if MIN_MUSIC <= duration <= MAX_MUSIC:
-        badge = "🎵 Musique (FreeSound URL)"
-        dl_class = int(predict_with_model_fs(xgb_music_num, sound, xgb_music_feat_num))
-        avg_text = str(predict_with_model_fs(xgb_music_avg, sound, xgb_music_feat_avg, le_music_avg))
-        dl_text = NUM_DOWNLOADS_MAP.get(dl_class, str(dl_class))
+    badge = "🎵 Musique (metadata FreeSound)"
+    dl_class = int(predict_with_model(xgb_music_num, sound, xgb_music_feat_num))
+    avg_text = str(predict_with_model(xgb_music_avg, sound, xgb_music_feat_avg, le_music_avg))
+    dl_text = NUM_DOWNLOADS_MAP.get(dl_class, str(dl_class))
 
-        avg_class = avg_label_to_class(avg_text)
-        conclusion = interpret_results(avg_class, dl_class)
+    avg_class = avg_label_to_class(avg_text)
+    conclusion = interpret_results(avg_class, dl_class)
 
-        extra = f"""
+    extra = f"""
 <div class="hint">ID FreeSound : <b>{sound_id}</b></div>
-<div style="margin-top:12px; padding-top:10px; border-top:1px dashed #d1d5db">
-{conclusion}
-</div>
+<div style="margin-top:12px; padding-top:10px; border-top:1px dashed #d1d5db">{conclusion}</div>
 """
-        return html_result(badge, duration, avg_text, dl_text, extra_html=extra)
-
-    return html_error(
-        "Durée non supportée",
-        f"Durée détectée : <b>{duration:.2f} s</b><br><br>"
-        f"Plages acceptées :<br>"
-        f"• Effet sonore : <b>{MIN_EFFECT}–{MAX_EFFECT} s</b><br>"
-        f"• Musique : <b>{MIN_MUSIC}–{MAX_MUSIC} s</b>"
-    )
+    return html_result(badge, duration, avg_text, dl_text, extra_html=extra)
 
 
 # =========================
-# APP UI (2 onglets)
+# UI Gradio (metadata only)
 # =========================
 theme = gr.themes.Soft()
 
-with gr.Blocks(title="Démo — Popularité Audio", css=CSS) as demo:
+with gr.Blocks(title="Test — Metadata FreeSound", css=CSS) as demo:
     gr.HTML(
         f"""
-<div id="header-title">Démo — Prédiction de popularité audio</div>
+<div id="header-title">🔎 Test — Prédiction via Metadata FreeSound</div>
 <p id="header-sub">
-Deux modes : <b>Upload audio</b> (openSMILE) ou <b>URL FreeSound</b> (features API).<br><br>
+Collez une URL FreeSound. L'app récupère les <b>metadata</b> via l'API et prédit la popularité (avg_rating, num_downloads).
+<br><br>
 <b>Durées acceptées :</b> 🔊 Effet sonore {MIN_EFFECT}–{MAX_EFFECT}s · 🎵 Musique {MIN_MUSIC}–{MAX_MUSIC}s
 </p>
 """
     )
 
-    with gr.Tabs():
-        with gr.Tab("1) Upload audio (openSMILE)"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.Markdown("### Importer un fichier")
-                    audio_in = gr.Audio(type="filepath", label="Fichier audio")
-                    btn_audio = gr.Button("🚀 Prédire (upload)", variant="primary")
-                with gr.Column(scale=1):
-                    gr.Markdown("### Résultat")
-                    out_audio = gr.HTML()
-            btn_audio.click(predict_from_uploaded_audio, inputs=audio_in, outputs=out_audio)
-
-        with gr.Tab("2) URL FreeSound (features API)"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.Markdown("### Coller une URL FreeSound")
-                    url_in = gr.Textbox(label="URL FreeSound", placeholder="https://freesound.org/s/123456/")
-                    btn_url = gr.Button("🚀 Prédire (URL FreeSound)", variant="primary")
-                with gr.Column(scale=1):
-                    gr.Markdown("### Résultat")
-                    out_url = gr.HTML()
-            btn_url.click(predict_from_freesound_url, inputs=url_in, outputs=out_url)
+    url = gr.Textbox(label="URL FreeSound", placeholder="https://freesound.org/s/123456/")
+    btn = gr.Button("🚀 Tester la prédiction", variant="primary")
+    out = gr.HTML()
+
+    btn.click(extract_and_predict, inputs=url, outputs=out)
 
 demo.launch(theme=theme)

xgb_avg_rating_effectsound_features.pkl → effectSound_model_num_downloads.joblib

@@ -1,3 +1,3 @@
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-oid sha256:fa0509103f06306aa1d21f074fc89ee08d90d9cf4b0c2b3a9a5b3c4436d4c5af
-size 631
+oid sha256:d1d4c378ad69f9339733e16537a49ca700425404ce7b19f8c4d28f72e32ab087
+size 7327682

xgb_num_downloads_effectsound_features.pkl → effectSound_xgb_avg_rating.joblib

@@ -1,3 +1,3 @@
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-oid sha256:fa0509103f06306aa1d21f074fc89ee08d90d9cf4b0c2b3a9a5b3c4436d4c5af
-size 631
+oid sha256:378c975a1ae60a53d18d02f6e3f50567060c7c7a3660216ab487fb3eb7559356
+size 6617890

xgb_avg_rating_effectsound_model.pkl → effect_model_features_list.joblib

@@ -1,3 +1,3 @@
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-oid sha256:af8cfbf2e681e80ea641e30b40ac280999e9a72e9bd95b28f33755771ccd51e5
-size 10219909
+oid sha256:7e69161319b7759e8ca2c0d0d4352c45e3970c2b2a14ee24db00f51752179b42
+size 201078598

model_features_list.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3de53a97822783e64c599135996ba7e92b3b53c3a05d8ebd34ad9f4c5664942
+size 125604014

music_model_features_list.joblib

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