Updating from matplotlib to plotly express for quality plots

app.py

@@ -295,17 +295,25 @@ if st.session_state['prediction_made']:
             classes = list(tumor_info.keys())
             probs = st.session_state['prediction_probs'] * 100
             
-            fig, ax = plt.subplots(figsize=(10, 5))
-            colors = ['#1e88e5' if c != st.session_state['predicted_class'] else '#4CAF50' for c in classes]
-            bars = ax.barh([c.replace('_', ' ').title() for c in classes], probs, color=colors)
-            ax.set_xlabel('Probability (%)')
-            ax.set_title('Prediction Confidence Distribution', fontsize=14, fontweight='bold')
-            ax.set_xlim(0, 100)
-            
-            for bar, prob in zip(bars, probs):
-                ax.text(min(prob + 2, 98), bar.get_y() + bar.get_height()/2, f'{prob:.1f}%', 
-                                ha='left', va='center', color='white', fontweight='bold', fontsize=12)
-            st.pyplot(fig)
+            # Use Plotly instead of matplotlib
+            fig = px.bar(
+                x=probs,
+                y=[c.replace('_', ' ').title() for c in classes],
+                orientation='h',
+                text=probs,
+                color=[c.replace('_', ' ').title() for c in classes],
+                color_discrete_sequence=['#1e88e5' if c != st.session_state['predicted_class'] else '#4CAF50' for c in classes]
+            )
+            fig.update_layout(
+                title='Prediction Confidence Distribution',
+                xaxis_title='Probability (%)',
+                yaxis_title='Class',
+                showlegend=False,
+                uniformtext_minsize=12,
+                uniformtext_mode='hide'
+            )
+            fig.update_traces(texttemplate='%{text:.1f}%', textposition='outside')
+            st.plotly_chart(fig, use_container_width=True)
         
         with tab2:
             if st.session_state['gradcam_img'] is not None: