Update app.py

app.py

@@ -149,20 +149,25 @@ def predict(file_obj):
         top_features.append("Others")
         top_values.append(others_sum)
         
+        # Set base_values and expected_value to 0.5 for the binary classification baseline
         explanation = shap.Explanation(
             values=np.array(top_values),
-            base_values=0,  
-            data=np.array([raw_freq_vector[kmer_dict[feat]] if feat != "Others" else np.sum(raw_freq_vector[others_mask]) for feat in top_features]),
+            base_values=0.5,  
+            data=np.array([
+                raw_freq_vector[kmer_dict[feat]] if feat != "Others" 
+                else np.sum(raw_freq_vector[others_mask]) 
+                for feat in top_features
+            ]),
             feature_names=top_features
         )
-        explanation.expected_value = 0
+        explanation.expected_value = 0.5
         
         fig = shap.plots._waterfall.waterfall_legacy(explanation, show=False)
         
         buf = io.BytesIO()
         fig.savefig(buf, format='png')
         buf.seek(0)
-        plot_image = Image.open(buf)  # Convert BytesIO to PIL Image
+        plot_image = Image.open(buf)
 
         pred_class = 1 if probs[0][1] > probs[0][0] else 0
         pred_label = 'human' if pred_class == 1 else 'non-human'
@@ -196,4 +201,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch(share=True)  # Set share=True to create a public link
+    iface.launch(share=True)