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Commit bf3c78c8 authored by Simon Majorczyk's avatar Simon Majorczyk
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Merge branch 'Simon' into dev

parents 63230eb4 76aea6e6
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app_glob.py
+ 44
− 78
View file @ bf3c78c8
from flask import Flask, request, jsonify, render_template from flask import Flask, render_template, request, jsonify
import pickle
import pandas as pd import pandas as pd
from sklearn.preprocessing import StandardScaler import pickle
import numpy as np import os
app = Flask(__name__) app = Flask(__name__)
# Charger le modèle # Charger les modèles et scalers
with open('random_forest_model_binaire.pkl', 'rb') as model_file: model_paths = {
rf = pickle.load(model_file) "binaire": "random_forest_model_binaire.pkl",
"sup0": "random_forest_model_sup0.pkl"
# Charger le scaler entraîné }
with open('scaler_binaire.pkl', 'rb') as scaler_file: scaler_paths = {
scaler = pickle.load(scaler_file) "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('/') @app.route('/')
def home(): def index():
return render_template('index-glob.html') return render_template('index-glob.html', features=features)
@app.route('/predict', methods=['POST']) @app.route('/predict', methods=['POST'])
def predict(): def predict():
return process_prediction(request.form, '/predict') return predict_with_model("binaire")
@app.route('/predict_sup0', methods=['POST']) @app.route('/predict_sup0', methods=['POST'])
def predict_sup0(): def predict_sup0():
return process_prediction(request.form, '/predict_sup0') return predict_with_model("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 def predict_with_model(model_key):
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: try:
data[key] = float(data[key]) data = request.get_json()
except ValueError: input_data = [float(data[feature]) for feature in features]
pass df_input = pd.DataFrame([input_data], columns=features)
df_scaled = scalers[model_key].transform(df_input)
expected_features = ['year', 'acousticness', 'danceability', 'energy', 'explicit', prediction = models[model_key].predict(df_scaled)
'instrumentalness', 'key', 'liveness', 'loudness', 'mode', return jsonify({"prediction": int(prediction[0])})
'speechiness', 'tempo', 'valence', 'nb_caracteres_sans_espaces', except Exception as e:
'nb_artistes', 'featuring', 'duree_minute', 'categorie_annee', 'categorie_tempo'] return jsonify({"error": str(e)})
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])})
if __name__ == '__main__': if __name__ == '__main__':
app.run(debug=True) app.run(debug=True)
templates/index-glob.html
+ 24
− 12
View file @ bf3c78c8
...@@ -9,37 +9,42 @@ ...@@ -9,37 +9,42 @@
font-family: Arial, sans-serif; font-family: Arial, sans-serif;
margin: 0; margin: 0;
display: flex; display: flex;
background-color: #121212;
color: white;
} }
/* Sidebar */ /* Sidebar */
.sidebar { .sidebar {
width: 200px; width: 220px;
background-color: #333; background-color: #181818;
color: white;
height: 100vh; height: 100vh;
padding-top: 20px; padding-top: 20px;
display: flex; display: flex;
flex-direction: column; flex-direction: column;
align-items: center; align-items: center;
} }
.sidebar img {
width: 120px;
margin-bottom: 20px;
}
.sidebar button { .sidebar button {
width: 80%; width: 80%;
padding: 10px; padding: 10px;
margin: 10px 0; margin: 10px 0;
border: none; border: none;
background: #444; background: #1DB954;
color: white; color: black;
cursor: pointer; cursor: pointer;
font-size: 16px; font-size: 16px;
text-align: center; text-align: center;
border-radius: 20px;
} }
.sidebar button:hover { .sidebar button:hover {
background: #555; background: #1ed760;
} }
.content { .content {
flex-grow: 1; flex-grow: 1;
padding: 20px; padding: 20px;
} }
/* Cacher les sections par défaut */
.tab-content { .tab-content {
display: none; display: none;
} }
...@@ -49,7 +54,11 @@ ...@@ -49,7 +54,11 @@
form { form {
max-width: 600px; max-width: 600px;
margin: auto; margin: auto;
background: #282828;
padding: 20px;
border-radius: 10px;
} }
label { label {
display: block; display: block;
margin-top: 10px; margin-top: 10px;
...@@ -61,23 +70,25 @@ ...@@ -61,23 +70,25 @@
margin-top: 5px; margin-top: 5px;
border: 1px solid #ccc; border: 1px solid #ccc;
border-radius: 4px; border-radius: 4px;
background-color: #333;
color: white;
} }
button { button {
margin-top: 20px; margin-top: 20px;
padding: 10px 20px; padding: 10px 20px;
background-color: #4CAF50; background-color: #1DB954;
color: white; color: black;
border: none; border: none;
border-radius: 4px; border-radius: 20px;
cursor: pointer; cursor: pointer;
} }
button:hover { button:hover {
background-color: #45a049; background-color: #1ed760;
} }
#result { #result {
margin-top: 20px; margin-top: 20px;
font-size: 1.2em; font-size: 1.2em;
color: #555; color: #1DB954;
} }
</style> </style>
</head> </head>
...@@ -85,6 +96,7 @@ ...@@ -85,6 +96,7 @@
<!-- Sidebar pour naviguer entre les onglets --> <!-- Sidebar pour naviguer entre les onglets -->
<div class="sidebar"> <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('tab1')">Prédiction Standard</button>
<button onclick="showTab('tab2')">Prédiction (>0)</button> <button onclick="showTab('tab2')">Prédiction (>0)</button>
</div> </div>
......
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