Update app.py

app.py

@@ -11,7 +11,7 @@ model = tf.keras.models.load_model("MobileNetV2_UL_ML_c3_l0_acc88_auc94_20251007
 classes = ["Cubisme", "Expressionnisme", "Post-impressionnisme"]
 
 # Fonction de prédiction avec graphique personnalisé
-def predire(image):
+"""def predire(image):
     # Prédiction
     image_resized = tf.image.resize(image, (224, 224)) / 255.0
     preds = model.predict(tf.expand_dims(image_resized, axis=0))[0]
@@ -58,8 +58,41 @@ def predire(image):
     
     fig.tight_layout()
     
+    return fig"""
+
+def predire(image):
+    image_resized = tf.image.resize(image, (224, 224)) / 255.0
+    preds = model.predict(tf.expand_dims(image_resized, axis=0))[0]
+
+    sorted_indices = np.argsort(preds)[::-1]
+    sorted_classes = [classes[i] for i in sorted_indices]
+    sorted_probs = [preds[i] for i in sorted_indices]
+    colors = ['#2ecc71' if prob >= 0.5 else '#bdc3c7' for prob in sorted_probs]
+
+    fig = Figure(figsize=(4, 3))  # Format compact adapté mobile
+    ax = fig.add_subplot(111)
+
+    # Barres verticales
+    bars = ax.bar(sorted_classes, sorted_probs, color=colors, edgecolor='black', linewidth=1.5)
+
+    # Ajout pourcentages
+    for bar, prob in zip(bars, sorted_probs):
+        ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.03, 
+                f"{prob*100:.1f}%", ha='center', va='bottom', fontsize=13, fontweight='bold', color='black')
+
+    ax.set_ylabel('Probabilité', fontsize=15, fontweight='bold')
+    ax.set_ylim(0, 1.0)
+    ax.set_title('Probabilités par mouvement pictural', fontsize=16, fontweight='bold', pad=20)
+    ax.grid(axis='y', alpha=0.15, linestyle='--')
+    ax.set_axisbelow(True)
+
+    # Titres inclinés à 45°
+    ax.set_xticklabels(sorted_classes, rotation=45, ha='right', fontsize=15, fontweight='bold')
+    fig.tight_layout()
+
     return fig
 
+
 # Interface Gradio
 demo = gr.Interface(
     fn=predire,