Merge branch 'Simon' into dev

app_glob.py

-from flask import Flask, request, jsonify, render_template
-import pickle
+from flask import Flask, render_template, request, jsonify
 import pandas as pd
-from sklearn.preprocessing import StandardScaler
-import numpy as np
+import pickle
+import os
 
 app = Flask(__name__)
 
-# Charger le modèle
-with open('random_forest_model_binaire.pkl', 'rb') as model_file:
-    rf = pickle.load(model_file)
-
-# Charger le scaler entraîné
-with open('scaler_binaire.pkl', 'rb') as scaler_file:
-    scaler = pickle.load(scaler_file)
+# Charger les modèles et scalers
+model_paths = {
+    "binaire": "random_forest_model_binaire.pkl",
+    "sup0": "random_forest_model_sup0.pkl"
+}
+scaler_paths = {
+    "binaire": "scaler_binaire.pkl",
+    "sup0": "scaler_sup0.pkl"
+}
+
+models = {}
+scalers = {}
+
+for key in model_paths:
+    with open(model_paths[key], "rb") as model_file:
+        models[key] = pickle.load(model_file)
+    with open(scaler_paths[key], "rb") as scaler_file:
+        scalers[key] = pickle.load(scaler_file)
+
+# Charger les features
+features_path = "features.txt"
+if os.path.exists(features_path):
+    with open(features_path, "r") as f:
+        features = f.read().splitlines()
+else:
+    features = []
 
 @app.route('/')
-def home():
-    return render_template('index-glob.html')
+def index():
+    return render_template('index-glob.html', features=features)
 
 @app.route('/predict', methods=['POST'])
 def predict():
-    return process_prediction(request.form, '/predict')
+    return predict_with_model("binaire")
 
 @app.route('/predict_sup0', methods=['POST'])
 def predict_sup0():
-    return process_prediction(request.form, '/predict_sup0')
-
-def process_prediction(form_data, endpoint):
-    data = form_data.to_dict()
-    
-    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
+    return predict_with_model("sup0")
 
-    # Supprimer les clés non utilisées directement
-    data.pop('name', None)
-    data.pop('artists', None)
-    data.pop('duration_ms', None)
-    
-    # Convertir les valeurs en float si possible
-    for key in data:
+def predict_with_model(model_key):
     try:
-            data[key] = float(data[key])
-        except ValueError:
-            pass
-    
-    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']
-    
-    input_data = pd.DataFrame([[data.get(key, 0) for key in expected_features]], columns=expected_features)
-    
-    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
-    
-    input_data_scaled = scaler.transform(input_data)
-    predictions = rf.predict(input_data_scaled)
-    
-    return jsonify({'predictions': int(predictions[0])})
+        data = request.get_json()
+        input_data = [float(data[feature]) for feature in features]
+        df_input = pd.DataFrame([input_data], columns=features)
+        df_scaled = scalers[model_key].transform(df_input)
+        prediction = models[model_key].predict(df_scaled)
+        return jsonify({"prediction": int(prediction[0])})
+    except Exception as e:
+        return jsonify({"error": str(e)})
 
 if __name__ == '__main__':
     app.run(debug=True)

templates/index-glob.html

@@ -9,37 +9,42 @@
             font-family: Arial, sans-serif;
             margin: 0;
             display: flex;
+            background-color: #121212;
+            color: white;
         }
         /* Sidebar */
         .sidebar {
-            width: 200px;
-            background-color: #333;
-            color: white;
+            width: 220px;
+            background-color: #181818;
             height: 100vh;
             padding-top: 20px;
             display: flex;
             flex-direction: column;
             align-items: center;
         }
+        .sidebar img {
+            width: 120px;
+            margin-bottom: 20px;
+        }
         .sidebar button {
             width: 80%;
             padding: 10px;
             margin: 10px 0;
             border: none;
-            background: #444;
-            color: white;
+            background: #1DB954;
+            color: black;
             cursor: pointer;
             font-size: 16px;
             text-align: center;
+            border-radius: 20px;
         }
         .sidebar button:hover {
-            background: #555;
+            background: #1ed760;
         }
         .content {
             flex-grow: 1;
             padding: 20px;
         }
-        /* Cacher les sections par défaut */
         .tab-content {
             display: none;
         }
@@ -49,7 +54,11 @@
         form {
             max-width: 600px;
             margin: auto;
+            background: #282828;
+            padding: 20px;
+            border-radius: 10px;
         }
+
         label {
             display: block;
             margin-top: 10px;
@@ -61,23 +70,25 @@
             margin-top: 5px;
             border: 1px solid #ccc;
             border-radius: 4px;
+            background-color: #333;
+            color: white;
         }
         button {
             margin-top: 20px;
             padding: 10px 20px;
-            background-color: #4CAF50;
-            color: white;
+            background-color: #1DB954;
+            color: black;
             border: none;
-            border-radius: 4px;
+            border-radius: 20px;
             cursor: pointer;
         }
         button:hover {
-            background-color: #45a049;
+            background-color: #1ed760;
         }
         #result {
             margin-top: 20px;
             font-size: 1.2em;
-            color: #555;
+            color: #1DB954;
         }
     </style>
 </head>
@@ -85,6 +96,7 @@
 
     <!-- Sidebar pour naviguer entre les onglets -->
     <div class="sidebar">
+        <img src="https://upload.wikimedia.org/wikipedia/commons/2/26/Spotify_logo_with_text.svg" alt="Spotify Logo">
         <button onclick="showTab('tab1')">Prédiction Standard</button>
         <button onclick="showTab('tab2')">Prédiction (>0)</button>
     </div>