diff --git a/app_glob.py b/app_glob.py
index 62f8c722f5500b48c6758cc2d9a1fadfdf75a40d..d5783a3a883acd5bf8d48a7eaf7f8ca494981cec 100644
--- a/app_glob.py
+++ b/app_glob.py
@@ -1,93 +1,59 @@
-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
-
- # 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:
- 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])})
+ return predict_with_model("sup0")
+
+def predict_with_model(model_key):
+ try:
+ 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)
diff --git a/templates/index-glob.html b/templates/index-glob.html
index cf39f737f5d6da8cd2793fe50881ee20b2b9a9cf..8efd8799f62aeed03f44e3e880d56ea6aabd2d2a 100644
--- a/templates/index-glob.html
+++ b/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;
}
@@ -49,6 +54,9 @@
form {
max-width: 600px;
margin: auto;
+ background: #282828;
+ padding: 20px;
+ border-radius: 10px;
}
label {
display: block;
@@ -61,23 +69,25 @@
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 +95,7 @@
<!-- 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>