Update app.py

app.py

@@ -94,7 +94,7 @@ classes = ["Cubisme", "Expressionnisme", "Post-impressionnisme"]
     return fig"""
 
 # Solution 3 : Passer par Plotly
-import plotly.graph_objects as go
+"""import plotly.graph_objects as go
 
 def predire(image):
     image_resized = tf.image.resize(image, (224, 224)) / 255.0
@@ -120,6 +120,30 @@ def predire(image):
         height=280,
         font=dict(size=17)
     )
+    return fig"""
+
+# Solution avec le graphique plus haut
+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=(6, 4.2))  # hauteur augmentée
+    ax = fig.add_subplot(111)
+    bars = ax.bar(sorted_classes, sorted_probs, color=colors, edgecolor='black', linewidth=1.5)
+
+    for bar, prob in zip(bars, sorted_probs):
+        ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.035, 
+                f"{prob*100:.1f}%", ha='center', va='bottom', fontsize=12, color='black', fontweight='bold')
+
+    ax.set_ylabel('Probabilité', fontsize=14, fontweight='bold')
+    ax.set_ylim(0, 1)
+    ax.set_title("Probabilités par mouvement pictural", fontsize=14, fontweight='bold', pad=20)
+    ax.set_xticklabels(sorted_classes, rotation=45, ha='right', fontsize=13, fontweight='bold')
+    fig.tight_layout(pad=2.0)
     return fig