model sup0 formulaire

app_sup0.py

@@ -22,19 +22,74 @@ def predict():
         # Récupérer les données du formulaire
         data = request.form.to_dict()
 
-        # Convertir en DataFrame et en float
-        df_input = pd.DataFrame([data])
-        df_input = df_input.astype(float)
+        # Calculer les features automatiquement
+        if 'name' in data:
+            data['nb_caracteres_sans_espaces'] = len(data['name'].replace(" ", ""))
+        if 'artists' in data:
+            data['nb_artistes'] = data['artists'].count(',') + 1
+            data['featuring'] = int(data['nb_artistes'] > 1)
+        if 'duration_ms' in data:
+            duration_ms = float(data['duration_ms'])
+            data['duree_minute'] = float(f"{int(duration_ms // 60000)}.{int((duration_ms % 60000) // 1000):02d}")
+        if 'year' in data:
+            year = int(data['year'])
+            data['categorie_annee'] = 3 if year < 1954 else 2 if year < 2002 else 1
+        if 'tempo' in data:
+            tempo = float(data['tempo'])
+            if 40 <= tempo < 60:
+                data['categorie_tempo'] = 1
+            elif 60 <= tempo < 66:
+                data['categorie_tempo'] = 2
+            elif 66 <= tempo < 76:
+                data['categorie_tempo'] = 3
+            elif 76 <= tempo < 108:
+                data['categorie_tempo'] = 4
+            elif 108 <= tempo < 120:
+                data['categorie_tempo'] = 5
+            elif 120 <= tempo < 163:
+                data['categorie_tempo'] = 6
+            elif 163 <= tempo < 200:
+                data['categorie_tempo'] = 7
+            elif 200 <= tempo <= 208:
+                data['categorie_tempo'] = 8
+            else:
+                data['categorie_tempo'] = 9
         
-        # Appliquer le scaler
-        df_input_scaled = scaler.transform(df_input)
+        # Supprimer les clés inutiles
+        data.pop('name', None)
+        data.pop('artists', None)
+        data.pop('duration_ms', None)
         
-        # Faire la prédiction
-        prediction = rf.predict(df_input_scaled)
+        # Convertir les valeurs en float si possible
+        for key in data:
+            try:
+                data[key] = float(data[key])
+            except ValueError:
+                pass  # Garder les valeurs non convertibles (ex: texte)
+        
+        # Liste des features dans le bon ordre (comme lors de l'entraînement)
+        expected_features = ['year', 'acousticness', 'danceability', 'energy', 'explicit',
+                             'instrumentalness', 'key', 'liveness', 'loudness', 'mode',
+                             'speechiness', 'tempo', 'valence', 'nb_caracteres_sans_espaces',
+                             'nb_artistes', 'featuring', 'duree_minute', 'categorie_annee', 'categorie_tempo']
+        
+        # Construire le DataFrame avec les features dans le bon ordre
+        input_data = pd.DataFrame([[data.get(key, 0) for key in expected_features]], columns=expected_features)
+        
+        # Vérifier que toutes les colonnes attendues sont présentes
+        missing_cols = [col for col in expected_features if col not in input_data.columns]
+        if missing_cols:
+            return jsonify({'error': f'Missing features: {missing_cols}'}), 400
+        
+        # Normalisation des features
+        input_data_scaled = scaler.transform(input_data)
+        
+        # Prédiction
+        predictions = rf.predict(input_data_scaled)
         
-        return jsonify({"predictions": prediction.tolist()})
+        return jsonify({'predictions': predictions.tolist()})
     except Exception as e:
-        return jsonify({"error": str(e)})
+        return jsonify({'error': str(e)})
 
 if __name__ == '__main__':
     app.run(debug=True)

mlsup0final.py

@@ -6,41 +6,51 @@ from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
 print('HELLO')
+
 # 📂 Charger les données
 df = pd.read_csv("data_sup_0popularity.csv")
 
-# 1️⃣ Séparation des features et de la cible
-X = df.drop(columns=["popularity", "id", "artists", "name", "release_date", "date_sortie", "duration_ms", "nom_artiste"])
+# 1️⃣ Vérifier que les features sont bien présentes
+expected_features = ['year', 'acousticness', 'danceability', 'energy', 'explicit',
+                     'instrumentalness', 'key', 'liveness', 'loudness', 'mode',
+                     'speechiness', 'tempo', 'valence', 'nb_caracteres_sans_espaces',
+                     'nb_artistes', 'featuring', 'duree_minute', 'categorie_annee', 'categorie_tempo']
+
+# Vérifier que toutes les colonnes attendues sont présentes
+missing_features = [col for col in expected_features if col not in df.columns]
+if missing_features:
+    raise ValueError(f"⚠️ Il manque ces colonnes dans le dataset : {missing_features}")
+
+# 2️⃣ Séparation des features et de la cible
 y = df["popularity"]
+X = df[expected_features]  # On garde uniquement les features déjà présentes
 
-# 2️⃣ Split train/test
+# 3️⃣ Split train/test
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
-# 3️⃣ Normalisation des features
+# 4️⃣ Normalisation des features
 scaler = StandardScaler()
 X_train_scaled = scaler.fit_transform(X_train)
 X_test_scaled = scaler.transform(X_test)
 
-# 4️⃣ Entraînement du modèle
+# 5️⃣ Entraînement du modèle
 rf = RandomForestRegressor(n_estimators=200, max_depth=20, min_samples_split=2, random_state=42)
 rf.fit(X_train_scaled, y_train)
 
-# 5️⃣ Sauvegarde du modèle et du scaler
+# 6️⃣ Sauvegarde du modèle et du scaler
 with open("random_forest_model_sup0.pkl", "wb") as model_file:
     pickle.dump(rf, model_file)
 
 with open("scaler_sup0.pkl", "wb") as scaler_file:
     pickle.dump(scaler, scaler_file)
 
-# 6️⃣ Évaluation du modèle
+# 7️⃣ Évaluation du modèle
 y_pred = rf.predict(X_test_scaled)
 mae = mean_absolute_error(y_test, y_pred)
 rmse = np.sqrt(mean_squared_error(y_test, y_pred))
 r2 = r2_score(y_test, y_pred)
 
 print(f"\n📊 RandomForestRegressor - MAE: {mae:.2f}, RMSE: {rmse:.2f}, R²: {r2:.3f}")
-print("\n✅ Modèle et scaler sauvegardés !")
-
-#nohup python3 -u mlsup0final.py > modelsup0final.txt 2>&1 &
-#ps aux | grep python
+print("\n✅ Modèle et scaler sauvegardés avec succès !")

templates/index-sup0.html

@@ -50,62 +50,47 @@
 <body>
     <h1>Prédire la Popularité d'une Chanson</h1>
     <form action="/predict_sup0" method="post">
+        <label for="name">Titre de la chanson :</label>
+        <input type="text" id="name" name="name" required>
+
         <label for="year">Année :</label>
         <input type="number" id="year" name="year" required>
 
         <label for="acousticness">Acousticness :</label>
-        <input type="number" step="0.01" id="acousticness" name="acousticness" required>
+        <input type="number" step="0.0001" id="acousticness" name="acousticness" required>
 
         <label for="danceability">Danceability :</label>
-        <input type="number" step="0.01" id="danceability" name="danceability" required>
+        <input type="number" step="0.0001" id="danceability" name="danceability" required>
 
         <label for="energy">Energy :</label>
-        <input type="number" step="0.01" id="energy" name="energy" required>
+        <input type="number" step="0.0001" id="energy" name="energy" required>
 
         <label for="explicit">Explicit (0 ou 1) :</label>
         <input type="number" id="explicit" name="explicit" required>
 
         <label for="instrumentalness">Instrumentalness :</label>
-        <input type="number" step="0.01" id="instrumentalness" name="instrumentalness" required>
+        <input type="number" step="0.0001" id="instrumentalness" name="instrumentalness" required>
 
         <label for="key">Key :</label>
         <input type="number" id="key" name="key" required>
 
         <label for="liveness">Liveness :</label>
-        <input type="number" step="0.01" id="liveness" name="liveness" required>
+        <input type="number" step="0.0001" id="liveness" name="liveness" required>
 
         <label for="loudness">Loudness :</label>
-        <input type="number" step="0.1" id="loudness" name="loudness" required>
+        <input type="number" step="0.0001" id="loudness" name="loudness" required>
 
         <label for="mode">Mode :</label>
         <input type="number" id="mode" name="mode" required>
 
         <label for="speechiness">Speechiness :</label>
-        <input type="number" step="0.01" id="speechiness" name="speechiness" required>
+        <input type="number" step="0.0001" id="speechiness" name="speechiness" required>
 
         <label for="tempo">Tempo :</label>
-        <input type="number" step="0.1" id="tempo" name="tempo" required>
+        <input type="number" step="0.0001" id="tempo" name="tempo" required>
 
         <label for="valence">Valence :</label>
-        <input type="number" step="0.01" id="valence" name="valence" required>
-
-        <label for="nb_caracteres_sans_espaces">Nombre de caractères sans espaces :</label>
-        <input type="number" id="nb_caracteres_sans_espaces" name="nb_caracteres_sans_espaces" required>
-
-        <label for="nb_artistes">Nombre d'artistes :</label>
-        <input type="number" id="nb_artistes" name="nb_artistes" required>
-
-        <label for="featuring">Featuring :</label>
-        <input type="number" id="featuring" name="featuring" required>
-
-        <label for="duree_minute">Durée (minutes) :</label>
-        <input type="number" step="0.01" id="duree_minute" name="duree_minute" required>
-
-        <label for="categorie_annee">Catégorie année :</label>
-        <input type="number" id="categorie_annee" name="categorie_annee" required>
-
-        <label for="categorie_tempo">Catégorie tempo :</label>
-        <input type="number" id="categorie_tempo" name="categorie_tempo" required>
+        <input type="number" step="0.0001" id="valence" name="valence" required>
 
         <input type="submit" value="Prédire">
     </form>