Update app.py

app.py

@@ -529,7 +529,7 @@ def quick_predict_ui(image_pil):
                 <div>
                     <span style="font-size:16px;font-weight:bold;">Diagnostic Global</span><br>
                     <div class="highlight malin">
-                        {global_diag} : {ensemble_probs[top_class_idx]*100:.2f}% <br> ╰┈➤  {top_class_name.upper()}
+                        {global_diag} : {ensemble_probs[top_class_idx]*100:.2f}% ▪ {top_class_name.upper()}
                     </div>
                 </div>
             </div>
@@ -538,12 +538,11 @@ def quick_predict_ui(image_pil):
             img_src = "non-mel.webp" if global_diag == "Bénin" else "mel.webp"
             global_diag_html = f'''
             <div class="diagnostic-global clearfix">
-                <img src="https://huggingface.co/spaces/ericjedha/skin_care/resolve/main/{img_src}" width="150" style="float:left;margin-right:10px;">
+                <img src="https://huggingface.co/spaces/ericjedha/skin_care/resolve/main/{img_src}" width="80" style="float:left;margin-right:10px;">
                 <div>
                     <span style="font-size:16px;font-weight:bold;">Diagnostic Global</span><br>
                     <div class="highlight benin">
-                        {global_diag} : {ensemble_probs[top_class_idx]*100:.2f}% <br>
-                        ➜ {top_class_name.upper()}
+                        {global_diag} : {ensemble_probs[top_class_idx]*100:.2f}% ▪ {top_class_name.upper()}
                     </div>
                 </div>
             </div>
@@ -564,24 +563,23 @@ def quick_predict_ui(image_pil):
             '''
             warning_visible = True
 
-        # Génération du graphique
-        df = pd.DataFrame.from_dict(
-        {CLASS_NAMES[i]: float(ensemble_probs[i]*100) for i in range(len(CLASS_NAMES))},
-        orient='index', columns=['Probabilité']
-        ).reset_index().rename(columns={'index': 'Classe'})
-        df = df.sort_values(by='Probabilité', ascending=False)
-        df['Pourcentage'] = df['Probabilité'].apply(lambda x: f"{x:.1f}%")
-        fig = px.bar(df, x="Classe", y="Probabilité", color="Probabilité",
-             color_continuous_scale=px.colors.sequential.Viridis,
-             title="Probabilités par classe", text="Pourcentage")
-        fig.update_traces(textposition="outside")  # Forcer le texte à l'extérieur pour toutes les barres
-        fig.update_layout(xaxis_title="", yaxis_title="Probabilité (%)", height=400)
+        # Génération du graphique avec gr.BarPlot
+        probabilities = {CLASS_NAMES[i]: ensemble_probs[i] * 100 for i in range(len(CLASS_NAMES))}
+        bar_plot = gr.BarPlot(
+            x=CLASS_NAMES,
+            y=[probabilities[cls] for cls in CLASS_NAMES],
+            title="Probabilités par classe",
+            y_title="Probabilité (%)",
+            y_lim=[0, 100],  # Limite y pour inclure toutes les valeurs jusqu'à 100%
+            height=450,  # Hauteur suffisante pour éviter le rognage
+            y_title_format="{value:.1f}%",  # Afficher les pourcentages avec une décimale
+        )
 
         return (
             global_diag_html,
             output_text_html,
-            gr.update(value=warning_html, visible=warning_visible),  # Mise à jour dynamique
-            fig,
+            gr.update(value=warning_html, visible=warning_visible),
+            bar_plot,
             "✅ Analyse terminée."
         )
 
@@ -597,7 +595,7 @@ def quick_predict_ui(image_pil):
             None,
             f"❌ Erreur: {str(e)}"
         )
-
+        
 def generate_gradcam_ui(progress=gr.Progress()):
     global current_image, current_predictions
     if current_image is None or current_predictions is None: