Update app.py

app.py

@@ -60,7 +60,8 @@ classes = ["Cubisme", "Expressionnisme", "Post-impressionnisme"]
     
     return fig"""
 
-def predire(image):
+# Solution 2
+"""def predire(image):
     image_resized = tf.image.resize(image, (224, 224)) / 255.0
     preds = model.predict(tf.expand_dims(image_resized, axis=0))[0]
 
@@ -90,9 +91,39 @@ def predire(image):
     ax.set_xticklabels(sorted_classes, rotation=45, ha='right', fontsize=15, fontweight='bold')
     fig.tight_layout()
 
+    return fig"""
+
+# Solution 3 : Passer par Plotly
+import plotly.graph_objects as go
+
+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 = go.Figure(go.Bar(
+        x=sorted_classes,
+        y=sorted_probs,
+        text=[f"{p*100:.1f}%" for p in sorted_probs],
+        marker=dict(color=colors, line=dict(color='black', width=1)),
+        textposition='auto',
+    ))
+    fig.update_layout(
+        xaxis=dict(tickangle=45, tickfont=dict(size=17)),
+        yaxis=dict(range=[0,1], title='Probabilité', tickfont=dict(size=17)),
+        title="Probabilités par mouvement pictural",
+        margin=dict(l=15, r=15, t=40, b=25),
+        height=280,
+        font=dict(size=17)
+    )
     return fig
 
 
+
 # Interface Gradio
 demo = gr.Interface(
     fn=predire,